Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moment) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods has emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. At the same time, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.
Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait
Universiti Kebangsaan Malaysia（马来西亚国民大学）；Qatar University（卡塔尔大学）；Dr Ram Manohar Lohia Avadh University（拉姆·马诺哈尔·洛希亚阿瓦德大学）；Xiamen University Malaysia（厦门大学马来西亚分校）；National Research and Innovation Agency（国家研究与创新署）
Confidence-Based Self-Training for EMG-to-Speech: Leveraging Synthetic EMG for Robust Modeling
ETIS, CY Cergy Paris Université - ENSEA – CNRS, UMR 8051（ETIS，CY 塞尔吉巴黎大学 - ENSEA – CNRS，UMR 8051）；Institute for Infocomm Research, A STAR（信息与通信研究所以院）；IPAL (International Research Laboratory on Artificial Intelligence), CNRS（人工智能国际研究实验室，CNRS）
Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds
University of California, Davis（加利福尼亚大学戴维斯分校）；Center for Brain and Mind, University of California, Davis（加利福尼亚大学戴维斯分校脑与心智中心）；Department of Neurobiology, Physiology, and Behavior, University of California, Davis（加利福尼亚大学戴维斯分校神经生物学、生理学与行为学系）；Department of Otolaryngology/Head and Neck Surgery, University of California, Davis（加利福尼亚大学戴维斯分校耳鼻喉科/头颈外科）
Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics
School of Electronic and Electrical Engineering, University of Leeds（利兹大学电子与电气工程学院）；School of Mechanical Engineering, Tianjin University（天津大学机械工程学院）；International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University in Zhejiang（天津大学浙江创新设计与智能制造国际研究院）；Estun Medical Ltd（埃斯顿医疗有限公司）
