A Hybrid Method Integrating A Musculoskeletal Model with Long Short-Term Memory (LSTM) for Human Motion Prediction.

So far, it shows a growing interest in the biomechanics community in the development of wearable technologies and their clinical applications, which enables the diagnosis of movement disorders and design of the rehabilitation interventions. To provide reliable feedback in the human-machine interface for advanced rehabilitation devices, methods to predict motion intention was developed which aim to generate future human motion based on the measured motion. An inertial measurement unit (IMU) is a promising device for motion tracking, with the advantages of low cost and high convenience in sensor placement to measure motion in almost every environment. However, it reveals that few contributions have been devoted to human motion prediction with pure IMU data. Thus, we propose a hybrid method integrating a musculoskeletal (MSK) model and the long short-term memory (LSTM) artificial neural network (ANN) to predict human motion. The proposed method was capable to predict motion in the daily tasks (stand-to-sit-to-stand and walking) for healthy participants: the predicted knee joint angles had an RMSE of 2.93° when compared to measured knee joint angles from the IMU data. The proposed method outperformed the methods based on the ANN/MSK model (RMSE of 31.15°) and LSTM without the integration of the MSK model (RMSE of 31.26°) in the motion prediction. Clinical Relevance- This proposed model based on IMU data alone has the great potential to become a low-cost, easy-to-use alternative in motion prediction to interact with advanced rehabilitation devices in clinical practice.