| 1. |
- Boni, Irene, et al.
(författare)
-
Restoring Natural Forearm Rotation in Transradial Osseointegrated Amputees
- 2018
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 26:12, s. 2333-2341
-
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.
|
|
| 2. |
- Buist, Mirka, et al.
(författare)
-
Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation
- 2024
-
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
-
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.
|
|
| 3. |
- Clemente, Francesco, et al.
(författare)
-
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
-
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.
|
|
| 4. |
- Cubo, Rubén, et al.
(författare)
-
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
-
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.
|
|
| 5. |
- D'Accolti, Daniele, et al.
(författare)
-
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
-
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.
|
|
| 6. |
- D'Accolti, D., et al.
(författare)
-
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.
|
|
| 7. |
|
|
| 8. |
- Dozza, Marco, 1978, et al.
(författare)
-
Effects of linear versus sigmoid coding of visual or audio biofeedback for the control of upright stance.
- 2006
-
Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 14:4, s. 505-12
-
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.
|
|
| 9. |
- Earley, Eric, 1989, et al.
(författare)
-
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
-
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.
|
|
| 10. |
- Herman, Pawel Andrzej, 1979-, et al.
(författare)
-
Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification
- 2008
-
Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 16:4, s. 317-326
-
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..
|
|
