A focused review on upper and lower limb joint torque estimation via neural networks

Joint torque estimation is an essential aspect of the control architecture in assistive devices for rehabilitation and aiding movement impairments. Healthy adult torque trajectories serve as a baseline for controllers to determine the level of assistance required by patients, evaluate impaired motion, understand biomechanics, and design treatment plans. Currently, methods of torque estimation include inverse dynamics using gold standard motion capture systems, generic mathematical models based on joint torque-angle relationships, neuromusculoskeletal modelling using surface electromyography, and neural networks. As such, this review provides a focused overview of the recent and existing neural networks tailored for upper and lower limb joint torque estimation. Dataset preparation, data preprocessing, and evaluation metrics are presented along with a detailed description of the developed networks, which are classified by model architecture. It includes artificial neural networks (ANNs), convolution neural networks (CNNs), long short-term memory (LSTM) networks, and hybrid and alternate architectures such as wavelet or explainable convolution (XCM). The performance, benefits, and limitations of the models are discussed, highlighting CNNs and LSTMs as the current optimal models for time series prediction of joint torque. This is due to their ability to capture spatial and temporal dependencies in the data. Additionally, joint kinematics such as angles, angular velocities, and accelerations are considered optimal input parameters due to their ease of measurement using wearable sensors and integration with wearable assistive technology.

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