Altai Zainab, Boukhennoufa Issam, Zhai Xiaojun, Phillips Andrew, Moran Jason, Liew Bernard X W
School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Essex, United Kingdom.
School of Computer Science and Electronic Engineering, University of Essex, Essex, United Kingdom.
Front Bioeng Biotechnol. 2023 Jul 31;11:1215770. doi: 10.3389/fbioe.2023.1215770. eCollection 2023.
Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.
关节力矩测量是关节健康评估中的一个客观生物力学参数。基于三维运动捕捉数据的逆动力学是目前估计关节力矩的 “金标准”。最近，机器学习与可穿戴技术（如肌电图（EMG）、惯性测量单元（IMU）和电子测角仪（GON））测量的数据相结合，已被用于实现快速、简便且低成本的关节力矩测量。本研究调查了各种深度神经网络仅根据IMU传感器预测下肢关节力矩的能力。测试了五种不同深度神经网络（InceptionTimePlus、可解释卷积神经网络（XCM）、XCMplus、循环神经网络（RNNplus）和时间序列Transformer（TSTPlus））的性能，以使用躯干、大腿、小腿和足部四个IMU传感器的加速度和陀螺仪测量值来预测髋关节、膝关节、踝关节和距下关节力矩。考虑了多种运动模式，包括平地行走、跑步机行走、上楼梯、下楼梯、上坡和下坡。我们表明，XCM可以仅使用四个IMU的数据准确预测下肢关节力矩，均方根误差（RMSE）为0.046±0.013 Nm/kg，而其他架构的平均RMSE为0.064±0.003 Nm/kg。我们发现，髋关节、膝关节和踝关节力矩预测的RMSE相当，平均为0.069 Nm/kg，而距下关节力矩的RMSE最低，为0.033 Nm/kg。从所提出的方法中获得的实时反馈对于运动科学家和物理治疗师深入了解生物力学、技术和形态以制定个性化训练和康复计划可能具有极高的价值。
