| 1. |
- Malesevic, Nebojsa, et al.
(författare)
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A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions
- 2020
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Hand movement is controlled by a large number of muscles acting on multiple joints in the hand and forearm. In a forearm amputee the control of a hand prosthesis is traditionally depending on electromyography from the remaining forearm muscles. Technical improvements have made it possible to safely and routinely implant electrodes inside the muscles and record high-quality signals from individual muscles. In this study, we present a database of intramuscular EMG signals recorded with fine-wire electrodes alongside recordings of hand forces in an isometric setup and with the addition of spike-sorted metadata. Six forearm muscles were recorded from twelve able-bodied subjects and nine forearm muscles from two subjects. The fully automated recording protocol, based on command cues, comprised a variety of hand movements, including some requiring slowly increasing/decreasing force. The recorded data can be used to develop and test algorithms for control of a prosthetic hand. Assessment of the signals was done in both quantitative and qualitative manners.
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| 2. |
- Jones, Benedict C, et al.
(författare)
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To which world regions does the valence-dominance model of social perception apply?
- 2021
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Ingår i: Nature Human Behaviour. - : Springer Science and Business Media LLC. - 2397-3374. ; 5:1, s. 159-169
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Tidskriftsartikel (refereegranskat)abstract
- Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .
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| 3. |
- Kizyte, Asta, 1993-
(författare)
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High-Density Electromyography-Based Methods for Joint Torque Prediction and Motor Unit Behavior Observation
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Electromyography (EMG) is a technique that measures the electrical activity of muscles. It reflects muscle activation and provides an interface to the central nervous system at the level of the muscle or individual motor units, which helps us understand the mechanisms of muscle force production, control, and coordination. EMG can also be used to detect changes in muscle activity caused by pathology, making it a valuable tool for research, diagnosis, and rehabilitation. One of the latest advancements in EMG technology is high-density EMG (HD-EMG). HD-EMG measures multiple spatially separated samples of muscle activation. This additional spatial information in HD-EMG offers new possibilities for the prediction of joint torques and the ability to look into individual motor units by decomposing the signals using blind source separation methods. This thesis presents two studies that explore the use of HD-EMG methods for joint torque estimation and the observation of motor unit behavior. In the first paper, we presented a detailed investigation of the effects of different EMG and kinematic inputs on the accuracy and robustness of ankle joint torque prediction using support vector regression. To evaluate the robustness, we analyzed the results in three cases (intra-session, inter-subject, and inter-session) and two movement categories (isometric contraction and dynamic movement). We found that HD-EMG-derived inputs improve the accuracy and robustness of torque prediction of the isometric contractions. However, in dynamic movements, good prediction results could only be achieved by including additional kinematic features (ankle joint position and angular velocity), and the type of EMG input did not strongly influence the results.In the second paper, we investigated the changes in motor unit behavior of the ankle plantar flexor (soleus) and dorsiflexor (tibialis anterior) caused by spinal cord injury (SCI). We computed torque, EMG, and motor unit parameters during volitional sub-maximal voluntary contractions for the SCI group and compared them to a non-injured control cohort. We found that participants in both groups could maintain the prescribed torque with similar variability. However, the SCI group required higher muscle activation levels (normalized to maximum) to achieve the same level of relative torque compared to the control group. The SCI group had lower intramuscular coherence in the alpha frequency band than the control group, indicating altered neural synchronization at the sub-cortical level. The soleus motor unit firing patterns were more variable post-SCI than in the control group. In addition, at high torque levels (50% of personal maximum), both muscle's motor units were recruited and de-recruited at lower torques, and motor units fired at lower rates in the tibialis anterior muscle in persons with SCI, indicating altered force gradation strategies after the injury.The studies presented in this thesis demonstrated that HD-EMG is suitable for robust isometric ankle joint torque prediction, which has potential in applications such as robot-assisted rehabilitation and robotic gait assistive technology. In particular, the robustness and accuracy of HD-EMG-based predictions are essential for improved estimation of the joint torque that can then be used in the human-in-the-loop control scheme. In addition, HD-EMG decomposition enables a non-invasive way to observe the motor unit behavior in vivo in persons with neuromusculoskeletal disorders, which can enhance the understanding of the underlying neurophysiological mechanisms of motor impairments. The insights provided by such HD-EMG analysis in the future may be beneficial for developing targeted interventions and personalized therapies.
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| 4. |
- Liu, Jia, et al.
(författare)
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Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials
- 2024
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Ingår i: Journal of Neuroscience Methods. - 0165-0270. ; 406
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Tidskriftsartikel (refereegranskat)abstract
- Background: Intra-individual variability (IIV), a measure of variance within an individual's performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. New methods: Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. Results: We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. Comparison with existing methods: The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. Conclusions: This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.
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| 5. |
- Liu, Jia, et al.
(författare)
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Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials
- 2024
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Ingår i: JOURNAL OF NEUROSCIENCE METHODS. - 0165-0270 .- 1872-678X. ; 406
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Tidskriftsartikel (refereegranskat)abstract
- Background: Intra-individual variability (IIV), a measure of variance within an individual's performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. New methods: Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. Results: We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. Comparison with existing methods: The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. Conclusions: This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.
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| 6. |
- Liu, Jia, et al.
(författare)
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Long term sustained attention alters dynamic functional connectivity patterns
- 2023
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Ingår i: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). - 9798350324488 - 9798350324471
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Konferensbidrag (refereegranskat)abstract
- Mental fatigue has attracted much attention from researchers as it plays a key role in performance efficiency and safety situations. Functional connectivity analysis using graph theory is an effective method for revealing changes in cognition resources influenced by mental fatigue. Previous studies have revealed that functional networks are dynamically reorganized. Therefore, it is critical to explore dynamic timescales of networks related to specific cognitive abilities. In this study, we used an open EEG dataset of twenty-one subjects recorded in a 60-minutes sustained attention task. After preprocessing, we constructed connectivity matrices using the weighted phase lag index (wPLI) in the theta band and characterized them with dynamic graph measures, namely characteristic path length (CPL) and clustering coefficient (CC). The results show that the frontal-parietal brain networks in theta band are involved in a sustaining attention task. When averaging from temporal and spatial activations, CPL and CC decreased with time-on-task. Our results indicate that mental fatigue results in deteriorations in sustaining attention, and graph theory analysis can provide support for mental fatigue analysis.Clinical Relevance— Identification of the effects of long term sustained attention on dynamic brain networks may be potential for mechanism study and detection of mental states and attentional deficits caused by mental diseases.
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| 7. |
- Lundsberg, Jonathan, et al.
(författare)
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Compressed spike-triggered averaging in iterative decomposition of surface EMG
- 2023
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607. ; 228
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objective: Analysis of motor unit activity is important for assessing and treating diseases or injuries affecting natural movement. State-of-the-art decomposition translates high-density surface electromyography (HDsEMG) into motor unit activity. However, current decomposition methods offer far from complete separation of all motor units. Methods: This paper proposes a peel-offapproach to automatic decomposition of HDsEMG into motor unit action potential (MUAP) trains, based on the Fast Independent Component Analysis algorithm (FastICA). The novel steps include utilizing compression by means of Principal Component Analysis and spike-triggered averaging, to estimate surface MUAP distributions with less noise, which are iteratively subtracted from the HDsEMG dataset. Furthermore, motor unit spike trains are estimated by highdimensional density-based clustering of peaks in the FastICA source output. And finally, a new reliability measure is used to discard poor motor unit estimates by comparing the variance of the FastICA source output before and after the peel-offstep. The method was validated using reconstructed synthetic data at three different signal-to-noise levels and was compared to an established deflationary FastICA approach. Results: Both algorithms had very high recall and precision, over 90%, for spikes from matching motor units, referred to as matched performance. However, the peel-offalgorithm correctly identified more motor units for all noise levels. When accounting for unidentified motor units, total recall was up to 33 percentage points higher; and when accounting for duplicate estimates, total precision was up to 24 percentage points higher, compared to the state-of-the-art reference. In addition, a comparison was done using experimental data where the proposed algorithm had a matched recall of 97% and precision of 85% with respect to the reference algorithm. Conclusion: These results show a substantial performance increase for decomposition of simulated HDsEMG data and serve to validate the proposed approach. This performance increase is an important step towards complete decomposition and extraction of information of motor unit activity. (C) 2022TheAuthor(s). PublishedbyElsevierB.V.
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| 8. |
- Lundsberg, Jonathan, et al.
(författare)
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Inferring position of motor units from high-density surface EMG
- 2024
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Ingår i: SCIENTIFIC REPORTS. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.
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| 9. |
- Malešević, Nebojša, et al.
(författare)
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A database of high-density surface electromyogram signals comprising 65 isometric hand gestures
- 2021
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Control of contemporary, multi-joint prosthetic hands is commonly realized by using electromyographic signals from the muscles remaining after amputation at the forearm level. Although this principle is trying to imitate the natural control structure where muscles control the joints of the hand, in practice, myoelectric control provides only basic hand functions to an amputee using a dexterous prosthesis. This study aims to provide an annotated database of high-density surface electromyographic signals to aid the efforts of designing robust and versatile electromyographic control interfaces for prosthetic hands. The electromyographic signals were recorded using 128 channels within two electrode grids positioned on the forearms of 20 able-bodied volunteers. The participants performed 65 different hand gestures in an isometric manner. The hand movements were strictly timed using an automated recording protocol which also synchronously recorded the electromyographic signals and hand joint forces. To assess the quality of the recorded signals several quantitative assessments were performed, such as frequency content analysis, channel crosstalk, and the detection of poor skin-electrode contacts.
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| 10. |
- Maleševic, Nebojša, et al.
(författare)
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Contactless real-time heartbeat detection via 24 ghz continuous-wave doppler radar using artificial neural networks
- 2020
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Ingår i: Sensors (Switzerland). - : MDPI AG. - 1424-8220. ; 20:8
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Tidskriftsartikel (refereegranskat)abstract
- The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.
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| 11. |
- Malesevic, Nebojsa, et al.
(författare)
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Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
- 2022
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 22:13
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Tidskriftsartikel (refereegranskat)abstract
- Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.
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| 12. |
- Malesevic, Nebojsa, et al.
(författare)
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Exploration of sensations evoked during electrical stimulation of the median nerve at the wrist level
- 2023
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Ingår i: Journal of Neural Engineering. - 1741-2560 .- 1741-2552. ; 20:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Nerve rehabilitation following nerve injury or surgery at the wrist level is a lengthy process during which not only peripheral nerves regrow towards receptors and muscles, but also the brain undergoes plastic changes. As a result, at the time when nerves reach their targets, the brain might have already allocated some of the areas within the somatosensory cortex that originally processed hand signals to some other regions of the body. The aim of this study is to show that it is possible to evoke a variety of somatotopic sensations related to the hand while stimulating proximally to the injury, therefore, providing the brain with the relevant inputs from the hand regions affected by the nerve damage. Approach. This study included electrical stimulation of 28 able-bodied participants where an electrode that acted as a cathode was placed above the Median nerve at the wrist level. The parameters of electrical stimulation, amplitude, frequency, and pulse shape, were modulated within predefined ranges to evaluate their influence on the evoked sensations. Main results. Using this methodology, the participants reported a wide variety of somatotopic sensations from the hand regions distal to the stimulation electrode. Significance. Furthermore, to propose an accelerated stimulation tuning procedure that could be implemented in a clinical protocol and/or standalone device for providing meaningful sensations to the somatosensory cortex during nerve regeneration, we trained machine-learning techniques using the gathered data to predict the location/area, naturalness, and sensation type of the evoked sensations following different stimulation patterns.
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| 13. |
- Olsson, Alexander E., et al.
(författare)
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Automatic discovery of resource-restricted Convolutional Neural Network topologies for myoelectric pattern recognition
- 2020
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Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825. ; 120
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Tidskriftsartikel (refereegranskat)abstract
- Convolutional Neural Networks (CNNs) have been subject to extensive attention in the pattern recognition literature due to unprecedented performance in tasks of information extraction from unstructured data. Whereas available methods for supervised training of a CNN with a given network topology are well-defined with rigorous theoretical justification, procedures for the initial selection of topology are currently not. Work incorporating selection of the CNN topology has instead substantially been guided by the domain-specific expertise of the creator(s), followed by iterative improvement via empirical evaluation. This limitation of methodology is restricting in the pursuit of naturally controlled muscle-computer interfaces, where CNNs have been identified as a promising research avenue but effective topology selection heuristics are lacking. With the goal of mitigating ambiguities in topology selection, this paper presents a systematic approach wherein we apply a novel evolutionary algorithm to search a space of candidate topologies. Furthermore, we constrain the search-space by excluding topologies with excessive inference-time computational complexity, making the obtained results implementable in embedded systems. In contrast to manual topology design, our algorithm requires the user to only specify a relatively small set of intuitive hyperparameters. To validate our approach, we use it in order to create topologies for myoelectric pattern recognition via movement decoding of surface electromyography signals. By collating offline classification accuracies obtained from experiments on a collection of publicly available databases, we demonstrate that our method generates computationally lightweight topologies with performance comparable to those of available alternatives.
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| 14. |
- Olsson, Alexander E., et al.
(författare)
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Can Deep Synthesis of EMG Overcome the Geometric Growth of Training Data Required to Recognize Multiarticulate Motions?
- 2021
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2021, s. 6380-6383
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Tidskriftsartikel (refereegranskat)abstract
- By being predicated on supervised machine learning, pattern recognition approaches to myoelectric prosthesis control require electromyography (EMG) training data collected concurrently with every detectable motion. Within this framework, calibration protocols for simultaneous control of multifunctional prosthetic hands rapidly become prohibitively long-the number of unique motions grows geometrically with the number of controllable degrees of freedom (DoFs). This paper proposes a technique intended to circumvent this combinatorial explosion. Using EMG windows from 1-DoF motions as input and EMG windows from 2-DoF motions as targets, we train generative deep learning models to synthesize EMG windows appertaining to multi-DoF motions. Once trained, such models can be used to complete datasets consisting of only 1-DoF motions, enabling simple calibration protocols with durations that scale linearly with the number of DoFs. We evaluated synthetic EMG produced in this way via a classification task using a database of forearm surface EMG collected during 1-DoF and 2-DoF motions. Multi-output classifiers were trained on either (I) real data from 1-DoF and 2-DoF motions, (II) real data from only 1-DoF motions, or (III) real data from 1-DoF motions appended with synthetic EMG from 2-DoF motions. When tested on data containing all possible motions, classifiers trained on synthetic-appended data (III) significantly outperformed classifiers trained on 1-DoF real data (II), although significantly underperformed classifiers trained on both 1- and 2-DoF real data (I) (I < 0.05). These findings suggest that it is feasible to model EMG concurrent with multiarticulate motions as nonlinear combinations of EMG from constituent 1-DoF motions, and that such modelling can be harnessed to synthesize realistic training data.
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| 15. |
- Olsson, Alexander E, et al.
(författare)
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End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks
- 2021
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Ingår i: Frontiers in Neuroscience. - 1662-453X .- 1662-4548. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Processing myoelectrical activity in the forearm has for long been considered a promising framework to allow transradial amputees to control motorized prostheses. In spite of expectations, contemporary muscle-computer interfaces built for this purpose typically fail to satisfy one or more important desiderata, such as accuracy, robustness, and/or naturalness of control, in part due to difficulties in acquiring high-quality signals continuously outside laboratory conditions. In light of such problems, surgically implanted electrodes have been made a viable option that allows for long-term acquisition of intramuscular electromyography (iEMG) measurements of spatially precise origin. As it stands, the question of how information embedded in such signals is best extracted and combined across multiple channels remains open. This study presents and evaluates an approach to this end that uses deep neural networks based on the Long Short-Term Memory (LSTMs) architecture to regress forces exerted by multiple degrees of freedom (DoFs) from multichannel iEMG. Three deep learning models, representing three distinct regression strategies, were evaluated: (I) One-to-One, wherein each DoF is separately estimated by an LSTM model processing a single iEMG channel, (II) All-to-One, wherein each DoF is separately estimated by an LSTM model processing all iEMG channels, and (III) All-to-All, wherein a single LSTM model with access to all iEMG channels estimates all DoFs simultaneously. All models operate on raw iEMG, with no preliminary feature extraction required. When evaluated on a dataset comprising six iEMG channels with concurrent force measurements acquired from 14 subjects, all LSTM strategies were found to significantly outperform a baseline feature-based linear control regression method. This finding indicates that recurrent neural networks can learn to transform raw forearm iEMG signals directly into representations that correlate with forces exerted at the level of the hand to a greater degree than simple features do. Furthermore, the All-to-All and All-to-One strategies were found to exhibit better performance than the One-to-One strategy. This finding suggests that, in spite of the spatially local nature of signals, iEMG from muscles not directly actuating the relevant DoF can provide contextual information that aid in decoding motor intent.
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| 16. |
- Olsson, Alexander E., et al.
(författare)
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Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control
- 2021
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Ingår i: Journal of NeuroEngineering and Rehabilitation. - : Springer Science and Business Media LLC. - 1743-0003. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundProcessing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration.MethodsForearm sEMG with 8 channels was collected from healthy human subjects (N=20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts's law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session.ResultsMetric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (p<0.05) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen's d) separating MRL from LDA ranging from d =0.62 to d=1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission.ConclusionsThe results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.
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| 17. |
- Rohlén, Robin, et al.
(författare)
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A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Objective: Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g., the active MU displacements, arterial pulsations, bones, connective tissue, and noise.Approach: This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against stICA, i.e., the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings.Main results: We found that the spatial and temporal correlation between the MU-associated components from velBSS and stICA was high (0.86 +/- 0.05 and 0.87 +/- 0.06). The spike-triggered averaged twitch responses (using the MU spike trains from EMG) had an extremely high correlation (0.99 +/- 0.01). In addition, the computational time for velBSS was at least 50 times less than for stICA.Significance: The present algorithm (velBSS) outperforms the currently available method (stICA). It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.
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| 18. |
- Rohlén, Robin, et al.
(författare)
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A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics
- 2023
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Ingår i: Journal of Neural Engineering. - 1741-2560 .- 1741-2552.
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g., the active MU displacements, arterial pulsations, bones, connective tissue, and noise.Approach: This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against spatiotemporal independent component analysis (stICA), i.e., the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings.Main results: We found that the computational time for velBSS was at least 20 times less than for stICA, while the twitch responses and spatial maps extracted from stICA and velBSS for the same MU reference were highly correlated (0.96 ± 0.05 and 0.81 ± 0.13).Significance: The present algorithm (velBSS) is computationally much faster than the currently available method (stICA) while maintaining the same performance. It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.
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| 19. |
- Rohlén, Robin, et al.
(författare)
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Estimating the neural spike train from an unfused tetanic signal of low threshold motor units using convolutive blind source separation
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The central nervous system initiates voluntary force production by providing excitatory inputs to spinal motor neurons, each connected to a set of muscle fibres to form a motor unit. Motor units have been imaged and analysed using ultrafast ultrasound based on the separation of ultrasound images. Although this method has great potential to identify regions and trains of motor unit twitches (unfused tetanus) evoked by the spike trains, it currently has a limited motor unit identification rate. One potential explanation is that the current method neglects the temporal information in the separation process of ultrasound images, and including it could lead to significant improvement. Here, we take the first step by asking if it is possible to estimate the spike train of an unfused tetanic signal from simulated and experimental signals using convolutive blind source separation. This finding will provide a direction for ultrasound-based method improvement. In this study, we found that the estimated spike trains highly agreed with the simulated and reference spike trains. This result implies that the convolutive blind source separation of an unfused tetanic signal can be used to estimate its spike train. Although extending this approach to ultrasound images is promising, the translation remains to be investigated in future studies where spatial information is inevitable as a discriminating factor between different motor units.
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| 20. |
- Rohlén, Robin, et al.
(författare)
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Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation
- 2023
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Ingår i: Biomedical engineering online. - : BioMed Central (BMC). - 1475-925X. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Background: Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors.Results: This study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%).Conclusions: This result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.
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| 21. |
- Rohlén, Robin, et al.
(författare)
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Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Human movement is generated by activating motor units (MUs), i.e., the smallest structures that can be voluntarily controlled. Recent findings have shown imaging of voluntarily activated MUs using ultrafast ultrasound based on displacement velocity images and a decomposition algorithm. Given this, estimates of trains of twitches (unfused tetanic signals) evoked by the neural discharges (spikes) of spinal motor neurons are provided. Based on these signals, a band-pass filter method (BPM) has been used to estimate its spike train. In addition, an improved spike estimation method consisting of a continuous Haar wavelet transform method (HWM) has been suggested. However, the parameters of the two methods have not been optimized, and their performance has not been compared rigorously.Method: HWM and BPM were optimized using simulations. Their performance was evaluated based on simulations and two experimental datasets with 21 unfused tetanic contractions considering their rate of agreement, spike offset, and spike offset variability with respect to the simulated or experimental spikes.Results: A range of parameter sets that resulted in the highest possible agreement with simulated spikes was provided. Both methods highly agreed with simulated and experimental spikes, but HWM was a better spike estimation method than BPM because it had a higher agreement, less bias, and less variation (p < 0.001).Conclusions: The optimized HWM will be an important contributor to further developing the identification and analysis of MUs using imaging, providing indirect access to the neural drive of the spinal cord to the muscle by the unfused tetanic signals.
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| 22. |
- Rohlén, Robin, et al.
(författare)
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Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units
- 2022
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Ingår i: Journal of Electromyography & Kinesiology. - : Elsevier. - 1050-6411 .- 1873-5711. ; 67
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Tidskriftsartikel (refereegranskat)abstract
- Background: Recent findings have shown that imaging voluntarily activated motor units (MUs) by decomposing ultrasound-based displacement images provides estimates of unfused tetanic signals evoked by spinal motoneurons’ neural discharges (spikes). Two methods have been suggested to estimate its spike trains: band-pass filter (BPM) and Haar wavelet transform (HWM). However, the methods’ optimal parameters and which method performs the best are unknown. This study will answer these questions.Method: HWM and BPM were optimized using simulations. Their performance was evaluated based on simulations and 21 experimental datasets, considering their rate of agreement, spike offset, and spike offset variability to the simulated or experimental spikes.Results: A range of parameter sets that resulted in the highest possible agreement with simulated spikes was provided. Both methods highly agreed with simulated and experimental spikes, but HWM was a better spike estimation method than BPM because it had a higher agreement, less bias, and less variation (p < 0.001).Conclusions: The optimized HWM will be an important contributor to further developing the identification and analysis of MUs using imaging, providing indirect access to the neural drive of the spinal cord to the muscle by the unfused tetanic signals.
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| 23. |
- Rohlén, Robin, et al.
(författare)
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Quantifying the spatial distribution of individual muscle units using high-density surface EMG and ultrafast ultrasound
- 2023
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Ingår i: ECSS Paris 2023 Oral presentations.
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Konferensbidrag (refereegranskat)abstract
- INTRODUCTION:Resistance training is a well-known intervention to improve muscle strength (1), with motor unit (MU) adaptation playing an important role (2). Recently, MUs were tracked in humans before and after resistance training using high-density surface electromyography (HDsEMG), showing a correlation between maximal force increase and MUs’ average discharge rate (3). Although these results demonstrate the relationship between an increase in strength and MU activity, only MU-level neural adaptation was considered. Indeed, neural and muscular information needs to be studied jointly to understand the exact adaptations of the MUs in response to resistance training (4).Recently, a method based on ultrafast ultrasound was presented, providing estimates of MU territories in cross-section and the train of twitches evoked by the spinal motoneurons’ discharges (5). In this study, as a proof-of-concept, we combined ultrafast ultrasound and HDsEMG to explore the spatial distribution of individual MUs.METHODS:In a cross-sectional study, four participants performed low-force isometric contractions of the biceps brachii muscle while recording HDsEMG and ultrafast ultrasound signals from the biceps brachii muscle.The HDsEMG signals were decomposed into individual MU discharge timings (6), and the ultrafast ultrasound signals were decomposed into many components, each having a spatial map and temporal signal (5). We matched each discharge timing of a MU with a component based on spike-triggered averaging of the component’s temporal signal. Given a selected component, we applied a threshold to the spatial map and calculated the centroid and an equivalent diameter.RESULTS:Out of 16 recordings from four subjects, we decomposed 82 MUs from HDsEMG. Given this, we found 32 matches between individual MU discharges and ultrasound components where the triggered twitches had a significant amplitude. The estimated territories were 4.6 ± 1.1 mm (ranging from 2.8 to 8.6 mm), in line with findings from previous research using scanning-EMG (7). Moreover, the components were located 12.7 ± 3.4 mm below the skin (ranging from 6.4 to 19.4 mm).CONCLUSION:Our results show that using ultrafast ultrasound and HDsEMG in a strength training intervention, we should be able to quantify the relative contribution of the nervous system and skeletal muscle at the MU level. This information may provide the time course of both neural and hypertrophic adaptations to resistance training and elucidate the relative contributions of each to strength gain.
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| 24. |
- Rohlén, Robin, et al.
(författare)
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Spatial decomposition of ultrafast ultrasound images to identify motor unit activity : a comparative study with intramuscular and surface EMG
- 2023
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Ingår i: Journal of Electromyography & Kinesiology. - : Elsevier. - 1050-6411 .- 1873-5711.
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Tidskriftsartikel (refereegranskat)abstract
- The smallest voluntarily controlled structure of the human body is the motor unit (MU), comprised of a motoneuron and its innervated fibres. MUs have been investigated in neurophysiology research and clinical applications, primarily using electromyographic (EMG) techniques. Nonetheless, EMG (both surface and intramuscular) has a limited detection volume. A recent alternative approach to detect MUs is ultrafast ultrasound (UUS) imaging. The possibility of identifying MU activity from UUS has been shown by blind source separation (BSS) of UUS images, using optimal separation spatial filters. However, this approach has yet to be fully compared with EMG techniques for a large population of unique MU spike trains. Here we identify individual MU activity in UUS images using the BSS method for 401 MU spike trains from eleven participants based on concurrent recordings of either surface or intramuscular EMG from forces up to 30% of the maximum voluntary contraction (MVC) force. We assessed the BSS method’s ability to identify MU spike trains from direct comparison with the EMG-derived spike trains as well as twitch areas and temporal profiles from comparison with the spike-triggered-averaged UUS images when using the EMG-derived spikes as triggers. We found a moderate rate of correctly identified spikes (53.0 ± 16.0%) with respect to the EMG-identified firings. However, the MU twitch areas and temporal profiles could still be identified accurately, including at 30% MVC force. These results suggest that the current BSS methods for UUS can accurately identify the location and average twitch of a large pool of MUs in UUS images, providing potential avenues for studying neuromechanics from a large cross-section of the muscle. On the other hand, more advanced methods are needed to address the convolutive and partly non-linear summation of velocities for recovering the full spike trains.
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| 25. |
- Subash, Tanya, et al.
(författare)
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Comparing algorithms for assessing upper limb use with inertial measurement units
- 2022
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Ingår i: Frontiers in Physiology. - : Frontiers Media SA. - 1664-042X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- The various existing measures to quantify upper limb use from wrist-worn inertial measurement units can be grouped into three categories: 1) Thresholded activity counting, 2) Gross movement score and 3) machine learning. However, there is currently no direct comparison of all these measures on a single dataset. While machine learning is a promising approach to detecting upper limb use, there is currently no knowledge of the information used by machine learning measures and the data-related factors that influence their performance. The current study conducted a direct comparison of the 1) thresholded activity counting measures, 2) gross movement score,3) a hybrid activity counting and gross movement score measure (introduced in this study), and 4) machine learning measures for detecting upper-limb use, using previously collected data. Two additional analyses were also performed to understand the nature of the information used by machine learning measures and the influence of data on the performance of machine learning measures. The intra-subject random forest machine learning measure detected upper limb use more accurately than all other measures, confirming previous observations in the literature. Among the non-machine learning (or traditional) algorithms, the hybrid activity counting and gross movement score measure performed better than the other measures. Further analysis of the random forest measure revealed that this measure used information about the forearm’s orientation and amount of movement to detect upper limb use. The performance of machine learning measures was influenced by the types of movements and the proportion of functional data in the training/testing datasets. The study outcomes show that machine learning measures perform better than traditional measures and shed some light on how these methods detect upper-limb use. However, in the absence of annotated data for training machine learning measures, the hybrid activity counting and gross movement score measure presents a reasonable alternative. We believe this paper presents a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors.
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| 26. |
- Svensson, Pamela, et al.
(författare)
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Characterization of Pneumatic Touch Sensors for a Prosthetic Hand
- 2020
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Ingår i: IEEE Sensors Journal. - 1530-437X. ; 20:16, s. 9518-9527
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the results from the characterization of pneumatic touch sensors (sensing bulbs) designed to be integrated into myoelectric prostheses and body-powered prostheses. The sensing bulbs, made of silicone, were characterized individually (single sensing bulb) and as a set of five sensors integrated into a silicone glove. We looked into the sensing bulb response when applying pressure at different angles, and also studied characteristics such as repeatability, hysteresis, and frequency response. The results showed that the sensing bulbs have the advantage of responding consistently to pressure coming from different angles. Additionally, the output (pneumatic pressure) is dependent on the size of interacting object applied to the sensing bulb. This means that the sensing bulb will give higher sensation when picking up sharper objects than blunt objects. Furthermore, the sensing bulb has good repeatability, linearity with an error of 2.95± 0.40%, and maximum hysteresis error of 2.39± 0.17% on the sensing bulb. This well exceeds the required sensitivity range of a touch sensor. In summary, the sensing bulb shows potential for use in prosthetic hands.
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| 27. |
- Svensson, Pamela, et al.
(författare)
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Electrotactile Feedback for the Discrimination of Different Surface Textures Using a Microphone
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:10
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Tidskriftsartikel (refereegranskat)abstract
- Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation frequency. Twelve able-bodied volunteers participated in the study, in which they were asked to identify the stroked texture (among four used in this study: Felt, sponge, silicone rubber, and string mesh) using only electrotactile feedback. The experiments were done in three phases: (1) Training, (2) with-feedback, (3) without-feedback. Each texture was stroked 20 times each during all three phases. The results show that the participants were able to differentiate between different textures, with a median accuracy of 85%, by using only electrotactile feedback with the stimulation frequency being the only variable parameter.
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| 28. |
- Svensson, Pamela, et al.
(författare)
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The rubber hand illusion evaluated using different stimulation modalities
- 2023
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Ingår i: FRONTIERS IN NEUROSCIENCE. - 1662-453X .- 1662-4548. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Tactile feedback plays a vital role in inducing ownership and improving motor control of prosthetic hands. However, commercially available prosthetic hands typically do not provide tactile feedback and because of that the prosthetic user must rely on visual input to adjust the grip. The classical rubber hand illusion (RHI) where a brush is stroking the rubber hand, and the user's hidden hand synchronously can induce ownership of a rubber hand. In the classic RHI the stimulation is modality-matched, meaning that the stimulus on the real hand matches the stimulus on the rubber hand. The RHI has also been used in previous studies with a prosthetic hand as the "rubber hand," suggesting that a hand prosthesis can be incorporated within the amputee's body scheme. Interestingly, previous studies have shown that stimulation with a mismatched modality, where the rubber hand was brushed, and vibrations were felt on the hidden hand also induced the RHI. The aim of this study was to compare how well mechanotactile, vibrotactile, and electrotactile feedback induced the RHI in able-bodied participants and forearm amputees. 27 participants with intact hands and three transradial amputees took part in a modified RHI experiment. The rubber hand was stroked with a brush, and the participant's hidden hand/residual limb received stimulation with either brush stroking, electricity, pressure, or vibration. The three latter stimulations were modality mismatched with regard to the brushstroke. Participants were tested for ten different combinations (stimulation blocks) where the stimulations were applied on the volar (glabrous skin), and dorsal (hairy skin) sides of the hand. Outcome was assessed using two standard tests (questionnaire and proprioceptive drift). All types of stimulation induced RHI but electrical and vibration stimulation induced a stronger RHI than pressure. After completing more stimulation blocks, the proprioceptive drift test showed that the difference between pre- and post-test was reduced. This indicates that the illusion was drifting toward the rubber hand further into the session.
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| 29. |
- Wijk, Ulrika, et al.
(författare)
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Sensory Feedback in Hand Prostheses : A Prospective Study of Everyday Use
- 2020
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Ingår i: Frontiers in Neuroscience. - : Frontiers Media SA. - 1662-4548 .- 1662-453X. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Sensory feedback in hand prostheses is lacking but wished for. Many amputees experience a phantom hand map on their residual forearm. When the phantom hand map is touched, it is experienced as touch on the amputated hand. A non-invasive sensory feedback system, applicable to existing hand prostheses, can transfer somatotopical sensory information via phantom hand map. The aim was to evaluate how forearm amputees experienced a non-invasive sensory feedback system used in daily life over a 4-week period. Methods: This longitudinal cohort study included seven forearm amputees. A non-invasive sensory feedback system was used over 4 weeks. For analysis, a mixed method was used, including quantitative tests (ACMC, proprioceptive pointing task, questionnaire) and interviews. A directed content analysis with predefined categories sensory feedback from the prosthesis, agency, body ownership, performance in activity, and suggestions for improvements was applied. Results: The results from interviews showed that sensory feedback was experienced as a feeling of touch which contributed to an experience of completeness. However, the results from the questionnaire showed that the sense of agency and performance remained unchanged or deteriorated. The ability to feel and manipulate small objects was difficult and a stronger feedback was wished for. Phantom pain was alleviated in four out of five patients. Conclusion: This is the first time a non-invasive sensory feedback system for hand prostheses was implemented in the home environment. The qualitative and quantitative results diverged. The sensory feedback was experienced as a feeling of touch which contributed to a feeling of completeness, linked to body ownership. The qualitative result was not verified in the quantitative measurements. Clinical Trial Registration: Name: Evaluation of a Non-invasive Sensory Feedback System in Hand Prostheses. Date of registration: March 15, 2019. Date the first participant was enrolled: April 1, 2015. ClinicalTrials.gov Identifier: NCT03876405 ORCID ID: https://orcid.org/0000-0002-4140-7478.
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