| 1. |
- 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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| 2. |
- 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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| 3. |
- Earley, Eric, 1989, et al.
(författare)
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Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities
- 2024
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 32, s. 1013-1022
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Tidskriftsartikel (refereegranskat)abstract
- Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration and implanted electrodes in nerves and muscles for control and sensory feedback. Difficulties maintaining implant hygiene and donning and doffing the prosthesis arose due to his contralateral amputation, ipsilateral eye loss, and contralateral impaired vision necessitating continuous adaptations to the electromechanical interface. Despite these setbacks, the participant still demonstrated improvements in functional outcomes and the ability to control the prosthesis in various limb positions using the implanted electrodes. Our results demonstrate the importance of a multidisciplinary, iterative, and patient-centered approach to making cutting-edge technology accessible to patients with high levels of impairment.
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| 4. |
- Just, Fabian, 1990, et al.
(författare)
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Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics
- 2024
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 12, s. 40439-40449
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Tidskriftsartikel (refereegranskat)abstract
- Motion intent recognition for controlling prosthetic systems has long relied on machine learning algorithms. Artificial neural networks have shown great promise for solving such nonlinear classification tasks, making them a viable method for this purpose. To bring these advanced methods and algorithms beyond the confines of the laboratory and into the daily lives of prosthetic users, self-contained embedded systems are essential. However, embedded systems face constraints in size, computational power, memory footprint, and power consumption, as they must be non-intrusive and discreetly integrated into commercial prosthetic components. One promising approach to tackle these challenges is to use network quantization, which allows complying with limitations without significant loss in accuracy. Here, we compare network quantization performance for self-contained systems using TensorFlow Lite and the recently developed QKeras platform. Due to internal libraries, the use of TensorFlow Lite led to a 8 times higher flash memory usage than that of the unquantized reference network, disadvantageous for self-contained prosthetic systems. In response, we offer open-source code solutions that leverage the QKeras platform, effectively reducing flash memory requirements by 24 times compared to Tensorflow Lite. Additionally, we conducted a comprehensive comparison of state-of-The-Art microcontrollers. Our results reveal that the adoption of new architectures offers substantial reductions in inference time and power consumption. These improvements pave the way for real-Time decoding of motor intent using more advanced machine learning algorithms for daily life usage, possibly enabling more reliable and precise control for prosthetic users.
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