Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.

IF 4.8 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Chamalka Kenneth Perera, Alpha A Gopalai, Darwin Gouwanda, Siti A Ahmad, Pei-Lee Teh
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引用次数: 0

Abstract

Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient during rehabilitation or aiding essential daily tasks such as sit-to-walk (STW). STW itself can be generalized into strategies based on individual needs and movement patterns. In this study, three long short-term memory (LSTM) neural networks were empirically trained for hip and knee torque prediction considering these STW strategies and subject anthropometry. The hip and knee are the drivers of STW, while the network architectures were selected for recognizing temporal and spatial relationships. Performance of the LSTMs were compared and evaluated against the STW strategies to accurately generate strategy-specific and user-oriented torque. As such, train and test STW data were obtained from 65 subjects across three age groups: young, middle-aged, and older adults (19-73 years). Model inputs were hip and knee angles with horizontal center of mass velocity, while windowing allowed the LSTMs to dynamically adapt to real-time changes in STW transitions. The encoder-decoder LSTM showcased optimal performance with robust recognition of temporal features. It produced significantly (P<0.05) low hip and knee root mean square error (0.24 ± 0.07 and 0.15 ± 0.02 Nm/kg), strong Spearman's correlation (93.43 ± 2.86 and 84.83 ± 2.96%) and good intraclass correlation coefficients (greater than 0.75), demonstrating model reliability. Hence, this network predicts strategy and user oriented reference torques for more personalized controllers in assistive devices, with natural application of assistance.

利用长短期记忆神经网络预测坐着行走策略的下肢扭矩
在研究生物力学、评估治疗方法和设计动力辅助设备时,关节扭矩预测至关重要。辅助技术中的控制器需要参考扭矩轨迹来设定病人在康复或辅助坐着行走(STW)等基本日常任务时的辅助水平。坐姿行走本身可以根据个人需求和运动模式归纳为不同的策略。在本研究中,考虑到这些 STW 策略和受试者的人体测量,对三个长短期记忆(LSTM)神经网络进行了经验性训练,以预测髋关节和膝关节的扭矩。髋关节和膝关节是 STW 的驱动力,而网络架构则是为识别时间和空间关系而选择的。针对 STW 策略对 LSTM 的性能进行了比较和评估,以准确生成特定策略和面向用户的扭矩。因此,训练和测试 STW 数据来自三个年龄组的 65 名受试者:年轻人、中年人和老年人(19-73 岁)。模型输入为具有水平质心速度的髋关节和膝关节角度,而窗口允许 LSTM 动态适应 STW 过渡的实时变化。编码器-解码器 LSTM 凭借对时间特征的强大识别能力展现了最佳性能。它能明显(P
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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