Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison.

Autor: Paskett MD; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, USA. michael.paskett@utah.edu.; Center for Clinical and Translational Science, University of Utah, Salt Lake City, UT, 84112, USA. michael.paskett@utah.edu., Brinton MR; School of Engineering, Math and Computer Science, Elizabethtown College, Elizabethtown, PA, 17022, USA., Hansen TC; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, USA., George JA; Center for Clinical and Translational Science, University of Utah, Salt Lake City, UT, 84112, USA.; Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.; Division of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, UT, 84112, USA., Davis TS; Department of Neurosurgery, University of Utah, Salt Lake City, UT, 84112, USA., Duncan CC; Division of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, UT, 84112, USA., Clark GA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
Jazyk: angličtina
Zdroj: Journal of neuroengineering and rehabilitation [J Neuroeng Rehabil] 2021 Feb 25; Vol. 18 (1), pp. 45. Date of Electronic Publication: 2021 Feb 25.
DOI: 10.1186/s12984-021-00839-x
Abstrakt: Background: Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm's output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions.
Methods: We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures.
Results: Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms.
Conclusions: These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.
Databáze: MEDLINE
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