| 1. |
- Abbaspour, S., et al.
(författare)
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Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
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Tidskriftsartikel (refereegranskat)abstract
- Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
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| 2. |
- Ahkami, Bahareh, et al.
(författare)
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Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review
- 2023
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Ingår i: IEEE Transactions on Medical Robotics and Bionics. - 2576-3202. ; 5:3, s. 547-562
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Tidskriftsartikel (refereegranskat)abstract
- Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.
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| 3. |
- Ahkami, Bahareh, 1994, et al.
(författare)
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Extra-neural signals from severed nerves enable intrinsic hand movements in transhumeral amputations
- 2022
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Robotic prostheses controlled by myoelectric signals can restore limited but important hand function in individuals with upper limb amputation. The lack of individual finger control highlights the yet insurmountable gap to fully replacing a biological hand. Implanted electrodes around severed nerves have been used to elicit sensations perceived as arising from the missing limb, but using such extra-neural electrodes to record motor signals that allow for the decoding of phantom movements has remained elusive. Here, we showed the feasibility of using signals from non-penetrating neural electrodes to decode intrinsic hand and finger movements in individuals with above-elbow amputations. We found that information recorded with extra-neural electrodes alone was enough to decode phantom hand and individual finger movements, and as expected, the addition of myoelectric signals reduced classification errors both in offline and in real-time decoding.
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| 4. |
- Ahkami, Bahareh, 1994, et al.
(författare)
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Locomotion Decoding (LocoD) An Open-Source Modular Platform for Researching Control of Lower Limb Assistive Devices.
- 2023
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Ingår i: Computer Methods and Programs in Biomedicine. - 1872-7565 .- 0169-2607.
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objective: Commercially available motorized prosthetic legs use exclusively non-biological signals to control movements, such as those provided by load cells, pressure sensors, and inertial measurement units (IMUs). Despite that the use of biological signals of neuromuscular origin can provide more natural control of leg prostheses, these signals cannot yet be captured and decoded reliably enough to be used in daily life. Indeed, decoding motor intention from bioelectric signals obtained from the residual limb holds great potential, and therefore the study of decoding algorithms has increased in the past years with standardized methods yet to be established. Methods: In the absence of shared tools to record and process lower limb bioelectric signals, such as electromyography (EMG), we developed an open-source software platform to unify the recording and processing (pre-processing, feature extraction, and classification) of EMG and non-biological signals amongst researchers with the goal of investigating and benchmarking control algorithms. We validated our locomotion decoding (LocoD) software by comparing the accuracy in the classification of locomotion mode using three different combinations of sensors (1 = IMU+EMG, 2 = EMG, 3 = IMU). EMG and non-biological signals (from the IMU and pressure sensor) were recorded while able-bodied participants (n = 21) walked on different surfaces such as stairs and ramps, and this data set is also released publicly along this publication. LocoD was used for all recording, pre-processing, feature extraction, and classification of the recorded signals. We tested the statistical hypothesis that there was a difference in predicted locomotion mode accuracy between sensor combinations using the Wilcoxon signed-rank test. Results: We found that the sensor combination 1 (EMG+IMU) led to significantly more accurate and improved locomotion mode prediction (Accuracy=93.4 ± 3.9) than using EMG (Accuracy= 74.56 ± 5.8) or IMU alone (Accuracy=90.77 ± 4.6) with p-value < 0.001. Conclusions: Our results support previous research and validate the functionality of LocoD as an open-source and modular platform to research control algorithms for prosthetic legs that incorporate bioelectric signals.
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| 5. |
- Ahkami, Bahareh, 1994, et al.
(författare)
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Probability-Based Rejection of Decoding Output Improves the Accuracy of Locomotion Detection During Gait
- 2023
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X.
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Konferensbidrag (refereegranskat)abstract
- Prosthetic users need reliable control over their assistive devices to regain autonomy and independence, particularly for locomotion tasks. Despite the potential for myoelectric signals to reflect the users' intentions more accurately than external sensors, current motorized prosthetic legs fail to utilize these signals, thus hindering natural control. A reason for this challenge could be the insufficient accuracy of locomotion detection when using muscle signals in activities outside the laboratory, which may be due to factors such as suboptimal signal recording conditions or inaccurate control algorithms.This study aims to improve the accuracy of detecting locomotion during gait by utilizing classification post-processing techniques such as Linear Discriminant Analysis with rejection thresholds. We utilized a pre-recorded dataset of electromyography, inertial measurement unit sensor, and pressure sensor recordings from 21 able-bodied participants to evaluate our approach. The data was recorded while participants were ambulating between various surfaces, including level ground walking, stairs, and ramps. The results of this study show an average improvement of 3% in accuracy in comparison with using no post-processing (p-value < 0.05). Participants with lower classification accuracy profited more from the algorithm and showed greater improvement, up to 8% in certain cases. This research highlights the potential of classification post-processing methods to enhance the accuracy of locomotion detection for improved prosthetic control algorithms when using electromyogram signals.Clinical Relevance-Decoding of locomotion intent can be improved using post-processing techniques thus resulting in a more reliable control of lower limb prostheses.
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| 6. |
- Ahmed, Kirstin, 1974, et al.
(författare)
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Validation of IMU against optical reference and development of an open source pipeline: Proof of concept case report in transfemoral amputation fitted with a Percutaneous Osseointegrated Implant
- 2024
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Ingår i: Journal of NeuroEngineering and Rehabilitation. - 1743-0003.
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Tidskriftsartikel (refereegranskat)abstract
- Background Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation. Results Average RMSE between the two systems from the amputated participant (TFA) on the amputated and the intact sides were 2.35 ° (IQR = 1.45 °) and 3.59 ° (IQR = 2.00 °) respectively. Equivalent results without amputation (WA) were 2.26 ° (IQR = 1.08 °). Joint level average RMSE between the two systems from the TFA ranged from 1.66 ° to 3.82 ° and from 1.21 ° to 5.46 ° WA. In plane average RMSE between the two systems from the TFA ranged from 2.17 ° (coronal) to 3.91 ° (sagittal) and from 1.96 ° (transverse) to 2.32 ° (sagittal) WA. CMC results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 WA and resulted in ‘excellent’ similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40 % (knee level) to 54.54 % (pelvis level) and from 2.18 % to 36.01 % WA. Conclusions We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI. We have proved our hypothesis that by using this novel pipeline we can validate IMU motion capture data, to a clinically acceptable degree.
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| 7. |
- Al-Tashi, Mohammed, et al.
(författare)
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Classroom-ready open-source educational exoskeleton for biomedical and control engineering
- 2024
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Ingår i: Automatisierungstechnik. - 0178-2312. ; 72:5, s. 460-475
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, robotic arm exoskeletons have emerged as promising tools, finding widespread application in the rehabilitation of neurological disorders and as assistive devices for everyday activities, even alleviating the physical strain on labor-intensive tasks. Despite the growing prominence of exoskeletons in everyday life, a notable knowledge gap exists in the availability of open-source platforms for classroom-ready usage in educational settings. To address this deficiency, we introduce an open-source educational exoskeleton platform aimed at Science, Technology, Engineering, and Mathematics (STEM) education. This platform represents an enhancement of the commercial EduExo Pro by AUXIVO, tailored to serve as an educational resource for control engineering and biomedical engineering courses.
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| 8. |
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| 9. |
- Björkquist, Anna, et al.
(författare)
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Towards Implementation of a Home-Based Phantom Limb Pain Treatment Facilitated by Textile-Electrode System - A Case Study
- 2023
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Ingår i: Caring is Sharing — Exploiting the Value in Data for Health and Innovation. - : IOS Press. - 9781643683881 - 9781643683898 ; , s. 682-683
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Konferensbidrag (refereegranskat)abstract
- This case study reports the use of a new textile-electrode system for self-administered Phantom Motor Execution (PME) treatment at home in one patient with Phantom Limb Pain (PLP). In follow-up interviews, the patient reported reduced pain, increased mobility, and improved mental health, and aspects such as motivation, usability, support, and treatment outcome, could be recognized from an earlier study as crucial for successful implementation and adoption of the home-based long-term treatment. The findings are of interest to developers, providers, users, and researchers planning home-based clinical studies and/or scenarios based on technology-assisted treatment.
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| 10. |
- Buist, Mirka, et al.
(författare)
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Development and Validation of a Wearable Device to Provide Rich Somatosensory Stimulation for Rehabilitation After Sensorimotor Impairment
- 2023
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Ingår i: IEEE Transactions on Biomedical Circuits and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1932-4545 .- 1940-9990. ; 17:3, s. 547-557
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Tidskriftsartikel (refereegranskat)abstract
- Training sensory discrimination of the skin has the potential to reduce chronic pain due to sensorimotor impairments and increase sensorimotor function. Currently, there is no such device that can systematically provide rich skin stimulation suitable for a training protocol for individuals with amputation or major sensory impairment. This study describes the development and validation of a non-invasive wearable device meant to repeatedly and safely deliver somatosensory stimulations. The development was guided by a structured design control process to ensure the verifiability and validity of the design outcomes. Two sub-systems were designed: 1) a tactile display for touch and vibration sensations, and 2) a set of bands for sliding, pressure, and strain sensations. The device was designed with a versatile structure that allows for its application on different body parts. We designed a device-paired interactive computer program to enable structured sensory training sessions. Validation was performed with 11 individuals with intact limbs whose upper arm tactile sensitivity was measured over 5 training sessions. Tactile discrimination and perception threshold were measured using the standard 2-point discrimination and Semmes-Weinstein monofilament tests, respectively. The results of the monofilament test showed a significant improvement (p = 0.011), but the improvement was not significant for the 2-point discrimination test(p = 0.141). These promising results confirm the potential of the proposed training to increase the sensory acuity in the upper arms of individuals with intact limbs. Further studies will be conducted to determine how to transfer the findings of this work to improve the pain and/or functional rehabilitation in individuals with sensorimotor impairments.
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