The increasing demand for fast and accurate gait-impaired disease diagnosis requires a real-time prediction of gait information in order to enable online information access to determining the disease progression. In addition, the wearable sensor-based information acquisition meets the new trend of take-home healthcare, the access to the great amount of data enables applying data-driven methods in this scenario. In this paper, we propose to use wearable Electromyography (EMG) and inertial measurement unit (IMU) sensors to make an ahead-of-motion prediction of basic gait information, including lower-limb kinematics and kinetics. Particularly, a novel long short term memory (LSTM)-based algorithm is trained to extract features and continuously predict lower-limb angles. Based on the predicted kinematics, the kinetics of lower limbs are calculated by a dynamic model of human segments. EMG signals recorded from nine lower limb muscles and IMU signals from each lower-limb segment were collected for training the regressor. The experimental results with cross-validation among ten subjects have demonstrated the accuracy of the angle prediction and kinetics calculation. In addition, the optimal prediction time was exploited by testing the different sets of prediction time. The implication of this research work highlights the potential of continuous prediction of kinematics and kinetics, which provides fast and accurate access to basic gait information for smart healthcare applications.

对快速而准确的步态受损疾病诊断的需求不断增长，需要对步态信息进行实时预测，以便能够在线访问信息以确定疾病的进展。此外，基于可穿戴式传感器的信息采集满足了带回家的医疗保健的新趋势，在这种情况下，对大量数据的访问使应用数据驱动的方法成为可能。在本文中，我们建议使用可穿戴式肌电（EMG）和惯性测量单元（IMU）传感器对基本步态信息（包括下肢运动学和动力学）进行提前运动预测。特别是，一种新颖的基于长期短期记忆（LSTM）的算法经过训练，可以提取特征并连续预测下肢角度。根据预测的运动学，下肢的动力学是通过人体节段的动力学模型来计算的。收集了从九个下肢肌肉记录的肌电信号和每个下肢节段的IMU信号，以训练回归器。在十个对象之间进行交叉验证的实验结果证明了角度预测和动力学计算的准确性。此外，通过测试不同的预测时间集来利用最佳预测时间。这项研究工作的意义突显了对运动学和动力学进行连续预测的潜力，这为智能医疗保健应用提供了快速准确地访问基本步态信息的信息。在十个对象之间进行交叉验证的实验结果证明了角度预测和动力学计算的准确性。此外，通过测试不同的预测时间集来利用最佳预测时间。这项研究工作的意义突显了对运动学和动力学进行连续预测的潜力，这为智能医疗保健应用提供了快速准确地访问基本步态信息的信息。在十个对象之间进行交叉验证的实验结果证明了角度预测和动力学计算的准确性。此外，通过测试不同的预测时间集来利用最佳预测时间。这项研究工作的意义突显了运动学和动力学连续预测的潜力，这为智能医疗保健应用提供了快速准确地访问基本步态信息的信息。