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首页> 外文期刊>IEEE sensors journal >Neuromechanical Signal-Based Parallel and Scalable Model for Lower Limb Movement Recognition
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Neuromechanical Signal-Based Parallel and Scalable Model for Lower Limb Movement Recognition

机译：基于神经力学信号的平行和可扩展模型，用于下肢运动识别

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摘要

Individual who have lost their lower limb because of amputation can use the prosthesis to restore daily living activities. The amputee intent recognition during locomotion modes can be used as source to control lower limb prosthesis. Due to continuous data recording from multiple sensors, the timely recognition of activities of daily living have become a challenging issue for traditional technology and conventional machine learning algorithms. This work hypothesize that parallel discriminant features can be learned from large amount of data generated by aggregating the neuromechanical signals from multiple subjects with parallel and distributed computing platform. Consequently, this paper apply three classifiers including support vector machine, decision tree and random forest on large data sets. The model performance is extensively evaluated in terms of different performance measurement parameters such as accuracy, efficiency, scalability and speedup in sequential and distributed environment. The experimental results show that the parallel approach achieved 3.9x computation speedup as compared to the sequential approach without affecting accuracy level. The parallel support vector machine algorithm demonstrated high speedup and scalability in comparison with random forest and decision tree algorithms. The outcome of this study could promote parallel based model for the unobtrusive recognition of lower limb locomotion modes and could promote the future design for the intelligent control of prostheses and exoskeleton.
机译：由于截肢而失去了下肢的个人可以使用假肢来恢复日常生活活动。在运动模式期间的截肢者意向识别可以用作控制下肢假体的源。由于来自多个传感器的连续数据记录，及时识别日常生活活动已成为传统技术和传统机器学习算法的具有挑战性的问题。这项工作假设可以通过通过与并行和分布计算平台聚合来自多个受试者的神经力学信号来从产生的大量数据中学习并行判别特征。因此，本文应用三个分类器，包括支持向量机，决策树和大型数据集的随机林。在不同的性能测量参数（如顺序和分布式环境中的准确性，效率，可扩展性和加速）方面广泛地评估模型性能。实验结果表明，与顺序方法相比，并行方法实现了3.9倍的计算加速，而不会影响精度水平。并行支持向量机算法与随机林和决策树算法相比，展示了高速度和可扩展性。本研究的结果可以促进基于平行的模型，以便对下肢运动模式不引人注目的认可，可以促进对假体和外骨骼的智能控制的未来设计。

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