| 1. |
- Boni, Irene, et al.
(författare)
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Restoring Natural Forearm Rotation in Transradial Osseointegrated Amputees
- 2018
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 26:12, s. 2333-2341
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Tidskriftsartikel (refereegranskat)abstract
- Osseointegrated transradial prostheses have the potential to preserve the natural range of wrist rotation, which improves the performance of activities of daily living and reduces compensatory movements that potentially lead to secondary health problems over time. This is possible by enabling the radius and the ulna bone to move with respect to each other, restoring the functionality of the original distal-radioulnar joint. In this paper, we report on psychophysics tests performed on an osseointegrated transradial amputee with the aim to understand the extent of mobility of the implants that is required to preserve the natural forearm rotation. Based on these experiments, we designed and developed an attachment device between the implants and the hand prosthesis that serves as an artificial distal radio-ulnar joint. This device was fitted on an osseointegrated transradial amputee and its functionality assessed by means of the Southampton Hand Assessment Procedure (SHAP) and the Minnesota Manual Dexterity test (MMDT). We found that the axial rotation of the implants is required to preserve forearm rotation, to distribute loads equally over the two implants (60% radius - 40% ulna), and to enable loading of the implants without unpleasant feelings for the patient. Higher function was recorded when our attachment device enabled forearm rotation: SHAP from 61 to 71, MMDT from 258s to 231s. Natural forearm rotation can be successfully restored in transradial amputees by using osseointegration and our novel mechanical attachment to the hand prosthesis.
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| 2. |
- Buist, Mirka, et al.
(författare)
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Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation
- 2024
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1558-0210 .- 1534-4320. ; 32, s. 1515-1523
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Tidskriftsartikel (refereegranskat)abstract
- Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.
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| 3. |
- Clemente, Francesco, et al.
(författare)
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Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses
- 2016
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 24:12, s. 1314-1322
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Tidskriftsartikel (refereegranskat)abstract
- Human grasping and manipulation control critically depends on tactile feedback. Without this feedback, the ability for fine control of a prosthesis is limited in upper limb amputees. Although various approaches have been investigated in the past, at present there is no commercially available device able to restore tactile feedback in upper limb amputees. Based on the Discrete Event-driven Sensory feedback Control (DESC) policy we present a device able to deliver short-lasting vibrotactile feedback to transradial amputees using commercially available myoelectric hands. The device (DESC-glove) comprises sensorized thimbles to be placed on the prosthesis digits, a battery-powered electronic board, and vibrating units embedded in an arm-cuff being transiently activated when the prosthesis makes and breaks contact with objects. The consequences of using the DESC-glove were evaluated in a longitudinal study. Five transradial amputees were equipped with the device for onemonth at home. Through a simple test proposed here for the first time-the virtual eggs test-we demonstrate the effectiveness of the device for prosthetic control in daily life conditions. In the future the device could be easily exploited as an add-on to complement myoelectric prostheses or even embedded in prosthetic sockets to enhance their control by upper limb amputees.
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| 4. |
- Cubo, Rubén, et al.
(författare)
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Optimization-Based Contact Fault Alleviation in Deep Brain Stimulation Leads
- 2018
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1534-4320 .- 1558-0210. ; 26:1, s. 69-76
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Tidskriftsartikel (refereegranskat)abstract
- Deep brain stimulation (DBS) is a neurosurgical treatment in, e.g., Parkinsons Disease. Electrical stimulation in DBS is delivered to a certain target through electrodes implanted into the brain. Recent developments aiming at better stimulation target coverage and lesser side effects have led to an increase in the number of contacts in a DBS lead as well as higher hardware complexity. This paper proposes an optimization-based approach to alleviation of the fault impact on the resulting therapeutical effect in field steering DBS. Faulty contacts could be an issue given recent trends of increasing number of contacts in DBS leads. Hence, a fault detection/alleviation scheme, such as the one proposed in this paper, is necessary ensure resilience in the chronic stimulation. Two alternatives are considered and compared with the stimulation prior to the fault: one using higher amplitudes on the remaining contacts and another with alleviating contacts in the neighborhood of the faulty one. Satisfactory compensation for a faulty contact can be achieved in both ways. However, to designate alleviating contacts, a model-based optimization procedure is necessary. Results suggest that stimulating with more contacts yields configurations that are more robust to contact faults, though with reduced selectivity.
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| 5. |
- D'Accolti, Daniele, et al.
(författare)
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Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
- 2023
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 31, s. 208-217
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Tidskriftsartikel (refereegranskat)abstract
- The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.
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| 6. |
- D'Accolti, D., et al.
(författare)
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Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
- 2023
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 31, s. 208-217
-
Tidskriftsartikel (refereegranskat)abstract
- The design of prosthetic controllers bymeans of neurophysiologicalsignals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of similar to 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of similar to 89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist- hand movements (i.e., with accuracy > 85%). Regarding the cross-subject classifier, while our algorithm obtainedpromising resultswith non-amputees (accuracyup to similar to 80%), they were not as good with amputees (accuracy up to similar to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.
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| 7. |
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| 8. |
- Dozza, Marco, 1978, et al.
(författare)
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Effects of linear versus sigmoid coding of visual or audio biofeedback for the control of upright stance.
- 2006
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 14:4, s. 505-12
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Tidskriftsartikel (refereegranskat)abstract
- Although both visual and audio biofeedback (BF) systems for postural control can reduce sway during stance, a direct comparison between the two systems has never been done. Further, comparing different coding designs of audio and visual BF may help in elucidating how BF information is integrated in the control of posture, and may improve knowledge for the design of innovative BF systems for postural control. The purpose of this paper is to compare the effects of linear versus sigmoid coding of trunk acceleration for audio and visual BF on postural sway in a group of eight, healthy subjects while standing on a foam surface. Results showed that sigmoid-coded audio BF reduced sway acceleration more than did a linear-coded audio BF, whereas a linear-coded visual BF reduced sway acceleration more than a sigmoid-coded visual BF. In addition, audio BF had larger effects on reducing center of pressure (COP) displacement whereas visual BF had larger effects on reducing trunk sway. These results suggest that audio and visual BF for postural control benefit from different types of sensory coding and each type of BF may encourage a different type of postural sway strategy.
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| 9. |
- Earley, Eric, 1989, et al.
(författare)
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Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities
- 2024
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 32, s. 1013-1022
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Tidskriftsartikel (refereegranskat)abstract
- Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration and implanted electrodes in nerves and muscles for control and sensory feedback. Difficulties maintaining implant hygiene and donning and doffing the prosthesis arose due to his contralateral amputation, ipsilateral eye loss, and contralateral impaired vision necessitating continuous adaptations to the electromechanical interface. Despite these setbacks, the participant still demonstrated improvements in functional outcomes and the ability to control the prosthesis in various limb positions using the implanted electrodes. Our results demonstrate the importance of a multidisciplinary, iterative, and patient-centered approach to making cutting-edge technology accessible to patients with high levels of impairment.
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| 10. |
- Herman, Pawel Andrzej, 1979-, et al.
(författare)
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Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification
- 2008
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 16:4, s. 317-326
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Tidskriftsartikel (refereegranskat)abstract
- The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain--computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left-- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper..
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| 11. |
- Kanitz, Gunter, et al.
(författare)
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Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses
- 2018
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : IEEE. - 1534-4320 .- 1558-0210. ; 26:9, s. 1756-1764
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.
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| 12. |
- Khandelwal, Siddhartha, 1987-, et al.
(författare)
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Gait Event Detection in Real-World Environment for Long-Term Applications : Incorporating Domain Knowledge into Time-Frequency Analysis
- 2016
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - Piscataway, NJ : IEEE Press. - 1534-4320 .- 1558-0210. ; 24:12, s. 1363-1372
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Tidskriftsartikel (refereegranskat)abstract
- Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE
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| 13. |
- Kizyte, Asta, et al.
(författare)
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Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression
- 2023
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 31, s. 4286-4294
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Tidskriftsartikel (refereegranskat)abstract
- Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.
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| 14. |
- Kortier, Henk, et al.
(författare)
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Hand pose estimation by fusion of inertial and magnetic sensing aided by a permanent magnet
- 2015
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1534-4320 .- 1558-0210. ; 23:5, s. 796-806
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Tidskriftsartikel (refereegranskat)abstract
- Tracking human body motions using inertial sensors has become a well-accepted method in ambulatory applications since the subject is not confined to a lab-bounded volume. However, a major drawback is the inability to estimate relative body positions over time because inertial sensor information only allows position tracking through strapdown integration, but doesn't provide any information about relative positions. In addition, strapdown integration inherently results in drift of the estimated position over time. We propose a novel method in which a permanent magnet combined with 3D magnetometers and 3D inertial sensors are used to estimate the global trunk orientation and relative pose of the hand with respect to the trunk. An Extended Kalman Filter is presented to fuse estimates obtained from inertial sensors with magnetic updates such that the position and orientation between the human hand and trunk as well as the global trunk orientation can be estimated robustly. This has been demonstrated in multiple experiments in which various hand tasks were performed. The most complex task in which simultaneous movements of both trunk and hand were performed resulted in an average rms position difference with an optical reference system of 19:72:2 mm whereas the relative trunk-hand and global trunk orientation error was 2:3 0:9 and 8:68:7 deg respectively.
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| 15. |
- Liu, Yixing, et al.
(författare)
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A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion
- 2021
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 29, s. 1089-1098
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Tidskriftsartikel (refereegranskat)abstract
- Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500ms prior to the step into that mode.
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| 16. |
- Lubel, Emma, et al.
(författare)
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Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation
- 2023
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210.
-
Tidskriftsartikel (refereegranskat)abstract
- Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.
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| 17. |
- Maier, Julian, et al.
(författare)
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Improved Prosthetic Control Based on Myoelectric Pattern Recognition via Wavelet-Based De-Noising
- 2018
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 26:2, s. 506-514
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Tidskriftsartikel (refereegranskat)abstract
- Real-time inference of human motor volition has great potential for the intuitive control of robotic devices. Toward this end, myoelectric pattern recognition (MPR) has shown promise in the control of prosthetic limbs. Interfering noise and susceptibility to motion artifacts have hindered the use of MPR outside controlled environments, and thus represent an obstacle for clinical use. Advanced signal processing techniques have been previously proposed to improve the robustness of MPR systems. However, the investigation of such techniques have been limited to offline implementations with long time windows, which makes real-time use unattainable. In this work, we present a novel algorithm using discrete and stationary wavelet transforms for MPR that can be executed in real-time. Our wavelet-based de-noising algorithm outperformed conventional band-pass filtering (up to 100 Hz) and improved real-time MPR in the presence of motion artifacts, as measured by the motion test. Improved signal-to-noise ratio was found not to be crucial in offline MPR, as machine learning algorithms can integrate high but consistent noise as part of the signal. However, varying interference is expected to occur in real life where signal processing algorithms, as the one introduced in this paper, would potentially have a positive impact. Furthermore implementation of these algorithms in a prosthetic embedded system is required to validate their feasibility and usability during activities of the daily living.
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| 18. |
- Muceli, Silvia, 1981, et al.
(författare)
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Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom
- 2012
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 20:3, s. 371-378
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes and tests on able-bodied subjects a control strategy that can be practically applied in unilateral transradial amputees for simultaneous and proportional control of multiple degrees-of-freedom (DOFs). We used artificial neural networks to estimate kinematics of the complex wrist/hand from high-density surface electromyography (EMG) signals of the contralateral limb during mirrored bilateral movements in free space. The movements tested involved the concurrent activation of wrist flexion/extension, radial/ulnar deviation, forearm pronation/supination, and hand closing. The accuracy in estimation was in the range 79%-88% (r 2 index) for the four DOFs in six able-bodied subjects. Moreover, the estimation of the pronation/supination angle (wrist rotation) was influenced by the reduction in the number of EMG channels used for the estimation to a greater extent than the other DOFs. In conclusion, the proposed method and set-up provide a viable means for proportional and simultaneous control of multiple DOFs for hand prostheses.
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| 19. |
- Ortiz Catalan, Max Jair, 1982, et al.
(författare)
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Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms
- 2014
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 22:4, s. 756-764
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Tidskriftsartikel (refereegranskat)abstract
- The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigated different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the Multi-Layer Perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the One-Vs-One (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability were evaluated in real-time using the Motion Test and Target Achievement Control (TAC) Test respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies.
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| 20. |
- Panarese, Alessandro, et al.
(författare)
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Humans can integrate force feedback to toes in their sensorimotor control of a robotic hand
- 2009
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 17:6, s. 560-567
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Tidskriftsartikel (refereegranskat)abstract
- Tactile sensory feedback is essential for dexterous object manipulation. Users of hand myoelectric prostheses without tactile feedback must depend essentially on vision to control their device. Indeed, improved tactile feedback is one of their main priorities. Previous research has provided evidence that conveying tactile feedback can improve prostheses control, although additional effort is required to solve problems related to pattern recognition learning, unpleasant sensations, sensory adaptation, and low spatiotemporal resolution. Still, these studies have mainly focused on providing stimulation to hairy skin regions close to the amputation site, i.e., usually to the upper arm. Here, we explored the possibility to provide tactile feedback to the glabrous skin of toes, which have mechanical and neurophysiological properties similar to the fingertips. We explored this paradigm in a grasp-and-lift task, in which healthy participants controlled two opposing digits of a robotic hand by changing the spacing of their index finger and thumb. The normal forces applied by the robotic fingertips to a test object were fed back to the right big and second toe. We show that within a few lifting trials, all the participants incorporated the force feedback received by the foot in their sensorimotor control of the robotic hand.
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| 21. |
- Pham, Tuan, 1962-
(författare)
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Texture Classification and Visualization of Time Series of Gait Dynamics in Patients with Neuro-Degenerative Diseases
- 2018
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 26:1, s. 188-196
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Tidskriftsartikel (refereegranskat)abstract
- The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and mortality in neuro-degenerative patients. Feature extraction of physiological time series and classification between gait patterns of healthy control subjects and patients are usually carried out on the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from conventional methods for gait analysis by transforming time series into images, of which texture features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm is applied to transform gait time series into texture images, which can be visualized to gain insight into disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Experimental results using only the right stride-intervals of the four groups show the effectiveness of the application of the proposed approach.
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| 22. |
- Puttaraksa, Gonthicha, et al.
(författare)
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Online tracking of the phase difference between neural drives to antagonist muscle pairs in essential tremor patients
- 2022
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 30, s. 709-718
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Tidskriftsartikel (refereegranskat)abstract
- Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.
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| 23. |
- Shirvany, Yazdan, 1980, et al.
(författare)
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Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials
- 2014
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 22:1, s. 11-20
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Tidskriftsartikel (refereegranskat)abstract
- One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert independently identified the expected source location, further corroborating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations.
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| 24. |
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| 25. |
- Vertriest, S., et al.
(författare)
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Static Load Bearing Exercises of Individuals With Transfemoral Amputation Fitted With an Osseointegrated Implant: Reliability of Kinetic Data
- 2015
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Ingår i: Ieee Transactions on Neural Systems and Rehabilitation Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 23:3, s. 423-430
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed at presenting the intra-tester reliability of the static load bearing exercises (LBEs) performed by individuals with transfemoral amputation (TFA) fitted with an osseointegrated implant to stimulate the bone remodeling process. There is a need for a better understanding of the implementation of these exercises particularly the reliability. The intra-tester reliability is discussed with a particular emphasis on inter-load prescribed, inter-axis and inter-component reliabilities as well as the effect of body weight normalization. Eleven unilateral TFAs fitted with an OPRA implant performed five trials in four loading conditions. The forces and moments on the three axes of the implant were measured directly with an instrumented pylon including a six-channel transducer. Reliability of loading variables was assessed using intraclass correlation coefficients (ICCs) and percentage standard error of measurement values (SEMs). The ICCs of all variables were above 0.9 and the SEM values ranged between 0 and 87. This study showed a high between-participants' variance highlighting the lack of loading consistency typical of symptomatic population as well as a high reliability between the loading sessions indicating a plausible correct repetition of the LBE by the participants. However, these outcomes must be understood within the framework of the proposed experimental protocol.
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| 26. |
- Zbinden, Jan, 1994, et al.
(författare)
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Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs
- 2024
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 32, s. 1177-1186
-
Tidskriftsartikel (refereegranskat)abstract
- The development of advanced prosthetic devices that can be seamlessly used during an individual's daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including a feedforward neural network with one hidden layer, a feedforward neural network with multiple hidden layers, a temporal convolutional network, and a convolutional neural network with squeeze-and-excitation operations were evaluated in real-time, human-in-the-loop experiments with able-bodied participants and an individual with an amputation. Our results demonstrate that deep learning architectures outperform shallow networks in decoding motor intent, with representation learning effectively extracting underlying motor control information from EMG signals. Furthermore, the observed performance improvements by using deep neural networks were consistent across both able-bodied and amputee participants. By employing deep neural networks instead of a shallow network, more reliable and precise control of a prosthesis can be achieved, which has the potential to significantly enhance prosthetic functionality and improve the quality of life for individuals with amputations.
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| 27. |
- Zhang, Longbin, et al.
(författare)
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Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks
- 2023
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 31, s. 3722-3731
-
Tidskriftsartikel (refereegranskat)abstract
- Accurately predicting joint torque using wearable sensors is crucial for designing assist-as-needed exoskeleton controllers to assist muscle-generated torque and ensure successful task performance. In this paper, we estimated ankle dorsiflexion/plantarflexion, knee flexion/extension, hip flexion/extension, and hip abduction/adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks. The joint torque ground truth for model calibrating and training was obtained through inverse dynamics of captured motion data. A cluster approach that grouped movements based on characteristic similarity was implemented, and its ability to improve the estimation accuracy of both NMS and LSTM models was evaluated. We compared torque estimation accuracy of NMS and LSTM models in three cases: Pooled, Individual, and Clustered models. Pooled models used data from all 10 movements to calibrate or train one model, Individual models used data from each individual movement, and Clustered models used data from each cluster. Individual, Clustered and Pooled LSTM models all had relatively high joint torque estimation accuracy. Individual and Clustered NMS models had similarly good estimation performance whereas the Pooled model may be too generic to satisfy all movement patterns. While the cluster approach improved the estimation accuracy in NMS models in some movements, it made relatively little difference in the LSTM neural networks, which already had high estimation accuracy. Our study provides practical implications for designing assist-as-needed exoskeleton controllers by offering guidelines for selecting the appropriate model for different scenarios, and has potential to enhance the functionality of wearable exoskeletons and improve rehabilitation and assistance for individuals with motor disorders.
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| 28. |
- Zhang, Longbin, et al.
(författare)
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Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning
- 2022
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 30, s. 600-609
-
Tidskriftsartikel (refereegranskat)abstract
- Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The ability to estimate joint torque during daily activities using wearable sensors is increasingly relevant in such settings. In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM) neural networks and transfer learning. LSTM models were trained with muscle electromyography signals and lower limb joint angles. Hip flexion/extension, hip abduction/adduction, knee flexion/extension and ankle dorsiflexion/plantarflexion torques were predicted. The LSTM models' performance in predicting torque was investigated in both intra-subject and inter-subject scenarios. Each scenario was further divided into intra-task and inter-task tests. We observed that LSTM models could predict lower limb joint torques during various activities accurately with relatively low error (root mean square error <= 0.14 Nm/kg, normalized root mean square error <= 8.7%) either through a uniform model or through ten separate models in intra-subject tests. Furthermore, a transfer learning technique was adopted in the inter-task and inter-subject tests to further improve the generalizability of LSTM models by pre-training a model on multiple subjects and/or tasks and transferring the learned knowledge to a target task/subject. Particularly in the inter-subject tests, we could predict joint torques accurately in several movements after training from only a few movements from new subjects.
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| 29. |
- Zheng, Zhefen, et al.
(författare)
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A Novel Neuromuscular Head-Neck Model and Its Application on Impact Analysis
- 2021
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 29, s. 1394-1402
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Neck muscle activation plays an important role in maintaining posture and preventing trauma injuries of the head-neck system, levels of which are primarily controlled by the neural system. Thus, the present study aims to establish and validate a neuromuscular head-neck model as well as to investigate the effects of realistic neural reflex control on head-neck behaviors during impact loading. Methods: The neuromuscular head-neck model was first established based on a musculoskeletal model by including neural reflex control of the vestibular system and proprioceptors. Then, a series of human posture control experiments was implemented and used to validate the model concerning both joint kinematics of the cervical spine and neck muscle activations. Finally, frontal impact experiments of varying loading severities were simulated with the newly established model and compared with an original model to investigate the influences of the implanted neural reflex controllers on head-neck kinematic responses. Results: The simulation results using the present neuromuscular model showed good correlations with in-vivo experimental data while the original model even cannot reach a correct balance status. Furthermore, the vestibular reflex is noted to dominate the muscle activation in less severe impact loadings while both vestibular and proprioceptive controllers have a lot of effect in higher impact loading severity cases. Conclusions: In summary, a novel neuromuscular head-model was established and its application demonstrated the significance of the neural reflex control in predicting in vivo head-neck responses and preventing related injury risk due to impact loading.
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| 30. |
- Zhou, Guang-Quan, et al.
(författare)
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Automatic Myotendinous Junction Identification in Ultrasound Images Based on Junction-Based Template Measurements
- 2023
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 31, s. 851-862
-
Tidskriftsartikel (refereegranskat)abstract
- Tracking the myotendinous junction (MTJ) motion in consecutive ultrasound images is essential to assess muscle and tendon interaction and understand the mechanics' muscle-tendon unit and its pathological conditions during motion. However, the inherent speckle noises and ambiguous boundaries deter the reliable identification of MTJ, thus restricting their usage in human motion analysis. This study advances a fully automatic displacement measurement method for MTJ using prior shape knowledge on the Y-shape MTJ, precluding the influence of irregular and complicated hyperechoic structures in muscular ultrasound images. Our proposed method first adopts the junction candidate points using a combined measure of Hessian matrix and phase congruency, followed by a hierarchical clustering technique to refine the candidates approximating the position of the MTJ. Then, based on the prior knowledge of Y-shape MTJ, we finally identify the best matching junction points according to intensity distributions and directions of their branches using multiscale Gaussian templates and a Kalman filter. We evaluated our proposed method using the ultrasound scans of the gastrocnemius from 8 young, healthy volunteers. Our results present more consistent with the manual method in the MTJ tracking method than existing optical flow tracking methods, suggesting its potential in facilitating muscle and tendon function examinations with in vivo ultrasound imaging.
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| 31. |
- Antfolk, Christian, et al.
(författare)
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Artificial Redirection of Sensation From Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback
- 2013
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 21:1, s. 112-120
-
Tidskriftsartikel (refereegranskat)abstract
- This work assesses the ability of transradial amputees to discriminate multi-site tactile stimuli in sensory discrimination tasks. It compares different sensory feedback modalities using an artificial hand prosthesis in: 1) a modality matched paradigm where pressure recorded on the five fingertips of the hand was fed back as pressure stimulation on five target points on the residual limb; and 2) a modality mismatched paradigm where the pressures were transformed into mechanical vibrations and fed back. Eight transradial amputees took part in the study and were divided in two groups based on the integrity of their phantom map; group A had a complete phantom map on the residual limb whereas group B had an incomplete or nonexisting map. The ability in localizing stimuli was compared with that of 10 healthy subjects using the vibration feedback and 11 healthy subjects using the pressure feedback (in a previous study), on their forearms, in similar experiments. Results demonstrate that pressure stimulation surpassed vibrotactile stimulation in multi-site sensory feedback discrimination. Furthermore, we demonstrate that subjects with a detailed phantom map had the best discrimination performance and even surpassed healthy participants for both feedback paradigms whereas group B had the worst performance overall. Finally, we show that placement of feedback devices on a complete phantom map improves multi-site sensory feedback discrimination, independently of the feedback modality.
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| 32. |
- Cipriani, Christian, et al.
(författare)
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Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
- 2011
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 19:3, s. 260-270
-
Tidskriftsartikel (refereegranskat)abstract
- A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.
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| 33. |
- Crema, Andrea, et al.
(författare)
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A Wearable Multi-Site System for NMES-Based Hand Function Restoration
- 2018
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 26:2, s. 428-440
-
Tidskriftsartikel (refereegranskat)abstract
- Reaching and grasping impairments significantly affect the quality of life for people who have experienced a stroke or spinal cord injury. The long-term well-being of patients varies greatly according to the restorable residual capabilities. Electrical stimulation could be a promising solution to restore motor functions in these conditions, but its use is not clinically widespread. Here, we introduce the HandNMES, an electrode array (EA) for neuromuscular electrical stimulation (NMES) aimed at grasp training and assistance. The device was designed to deliver electrical stimulation to extrinsic and intrinsic hand muscles. Six independent EAs, positioned on the user forearm and hand, deliver NMES pulses originating from an external stimulator equipped with demultiplexers for interfacing with a large number of electrodes. The garment was designed to be adaptable to user needs and anthropometric characteristics; size, shape, and contact materials can be customized, and stimulation characteristics such as intensity of stimulation and virtual electrode location, and size can be adjusted. We performed extensive tests with nine healthy subjects showing the efficacy of the HandNMES in terms of stimulation performance and personalization. Because encouraging results were achieved, in the coming months, the HandNMES device will be tested in pilot clinical trials.
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| 34. |
- Dumas, Raphael, et al.
(författare)
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Gait analysis of transfemoral amputees: errors in inverse dynamics are substantial and depend on prosthetic design
- 2017
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320. ; 25:6, s. 679-685
-
Tidskriftsartikel (refereegranskat)abstract
- Quantitative assessments of prostheses performances rely more and more frequently on gait analysis focusing on prosthetic knee joint forces and moments computed by inverse dynamics. However, this method is prone to errors, as demonstrated in comparison with direct measurements of these forces and moments. The magnitude of errors reported in the literature seems to vary depending on prosthetic components. Therefore, the purposes of this study were (A) to quantify and compare the magnitude of errors in knee joint forces and moments obtained with inverse dynamics and direct measurements on ten participants with transfemoral amputation during walking and (B) to investigate if these errors can be characterised for different prosthetic knees. Knee joint forces and moments computed by inverse dynamics presented substantial errors, especially during the swing phase of gait. Indeed, the median errors in percentage of the moment magnitude were 4% and 26% in extension/flexion, 6% and 19% in adduction/abduction as well as 14% and 27% in internal/external rotation during stance and swing phase, respectively. Moreover, errors varied depending on the prosthetic limb fitted with mechanical or microprocessor-controlled knees. This study confirmed that inverse dynamics should be used cautiously while performing gait analysis of amputees. Alternatively, direct measurements of joint forces and moments could be relevant for mechanical characterising of components and alignments of prosthetic limbs.
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| 35. |
- Frossard, Laurent, et al.
(författare)
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Load-relief of walking AIDS on osseointegrated fixation: instrument for evidence-based practice.
- 2009
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. - 1558-0210. ; 17:1, s. 9-14
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Tidskriftsartikel (refereegranskat)abstract
- Clinicians are currently in demand of tools enabling individual assessment during their daily practice of load-relief of walking aids. The first aim of this article is to describe a portable kinetic system that could be used to measure directly the true load applied on the residuum during assisted walking. The second aim is to present the information that can be derived from the raw loading data. The third aim is to provide an example for a participant. One active transfemoral amputee fitted with an osseointegrated fixation was asked to walk in straight level line with no aid, one stick, one and two elbow crutches on a 20 m walkway. The load-relief was measured using a six-channel transducer and recorded using a data logger. The overall loading was decreased by 2% using one stick, 5% using one crutch and by 10% using two crutches. This study presents a method that can be used by clinicians facing the challenge of prescribing and assessing walking aids to restore the locomotion of lower limb amputees in the framework of an evidence-based practice.
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| 36. |
- Isaksson, Johan, et al.
(författare)
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Audomni : Super-Scale Sensory Supplementation to Increase the Mobility of Blind and Low-Vision Individuals - A Pilot Study
- 2020
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 28:5, s. 1187-1197
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Blindness and low vision have severe effects on individuals' quality of life and socioeconomic cost; a main contributor of which is a prevalent and acutely decreased mobility level. To alleviate this, numerous technological solutions have been proposed in the last 70 years; however, none has become widespread. Method: In this paper, we introduce the vision-to-audio, super-scale sensory substitution/supplementation device Audomni; we address the field-encompassing issues of ill-motivated and overabundant test methodologies and metrics; and we utilize our proposed Desire of Use model to evaluate proposed pilot user tests, their results, and Audomni itself. Results: Audomni holds a spatial resolution of 80 x 60 pixels at 1.2° angular resolution and close to real-time temporal resolution, outdoor-viable technology, and several novel differentiation methods. The tests indicated that Audomni has a low learning curve, and several key mobility subtasks were accomplished; however, the tests would benefit from higher real-life motivation and data collection affordability. Conclusion: Audomni shows promise to be a viable mobility device - with some addressable issues. Employing Desire of Use to design future tests should provide both high real-life motivation and relevance to them. Significance: As far as we know, Audomni features the greatest information conveyance rate in the field, yet seems to offer comprehensible and fairly intuitive sonification; this work is also the first to utilize Desire of Use as a tool to evaluate user tests, a device, and to lay out an overarching project aim.
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| 37. |
- Isaksson, Johan, et al.
(författare)
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Desire of Use : A Hierarchical Decomposition of Activities and its Application on Mobility of by Blind and Low-Vision Individuals
- 2020
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 28:5, s. 1146-1156
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Blind and low-vision individuals often have severely reduced mobility, affecting their quality of life and associated socioeconomic cost. Despite numerous efforts and great technological progress, the only used primary mobility aids are still white canes and seeing-eye dogs. Furthermore, there is a permeating tendency in the field to ignore knowledge of both mobility and the target group, as well as constantly design new metrics and tests that makes comparisons between solutions markedly more difficult. Method: The Desire of Use model is introduced in an effort to promote a more holistic approach; it should be generalizable for any activity by any user, but is here applied on mobility of blind and low-vision individuals by a proposal and integration of parameters. Results: An embodiment of the model is presented and with it we show why popular mobility metrics of today are insufficient to guide design; what tasks and metrics that should provide better understanding; as well as which fundamental properties determine them and are critical to discuss. Conclusion: Desire of Use has been introduced as a tool and a theoretical framework, and a realization has been proposed. Significance: Desire of Use offers both a structured perspective of pertinent design challenges facing a given solution, as well as a platform from which to compare test results and properties of existing solutions; in for example the field of electronic travel aids it should prove valuable for designing and evaluating new tests and devices.
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| 38. |
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| 39. |
- Sanchez, Justin C, et al.
(författare)
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Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.
- 2005
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. - 1534-4320. ; 13:2, s. 213-9
-
Tidskriftsartikel (refereegranskat)abstract
- We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.
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| 40. |
- Zhu, Yongjie, et al.
(författare)
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Dynamic Community Detection for Brain Functional Networks During Music Listening With Block Component Analysis
- 2023
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 31, s. 2438-2447
-
Tidskriftsartikel (refereegranskat)abstract
- The human brain can be described as a complex network of functional connections between distinct regions, referred to as the brain functional network. Recent studies show that the functional network is a dynamic process and its community structure evolves with time during continuous task performance. Consequently, it is important for the understanding of the human brain to develop dynamic community detection techniques for such time-varying functional networks. Here, we propose a temporal clustering framework based on a set of network generative models and surprisingly it can be linked to Block Component Analysis to detect and track the latent community structure in dynamic functional networks. Specifically, the temporal dynamic networks are represented within a unified three-way tensor framework for simultaneously capturing multiple types of relationships between a set of entities. The multi-linear rank- $(L_{r}, L_{r}, 1)$ block term decomposition (BTD) is adopted to fit the network generative model to directly recover underlying community structures with the specific evolution of time from the temporal networks. We apply the proposed method to the study of the reorganization of the dynamic brain networks from electroencephalography (EEG) data recorded during free music listening. We derive several network structures ( $L_{r}$ communities in each component) with specific temporal patterns (described by BTD components) significantly modulated by musical features, involving subnetworks of frontoparietal, default mode, and sensory-motor networks. The results show that the brain functional network structures are dynamically reorganized and the derived community structures are temporally modulated by the music features. The proposed generative modeling approach can be an effective tool for describing community structures in brain networks that go beyond static methods and detecting the dynamic reconfiguration of modular connectivity elicited by continuously naturalistic tasks.
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