Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review

Abstract

Human motion intention (HMI) has increasingly gained concerns in lower limb exoskeletons (LLEs). HMI recognition (HMIR) is the precondition for realizing active compliance control in LLEs. Accurate and efficient recognition of the HMI will benefit the LLEs achieving natural and effective human–robot interaction (HRI) and improving the wearing comfort level. A systematic review of HMIR is of great significance in developing LLEs. However, there is no literature comprehensively describing the development roadmap of the human lower limb motion intention recognition (HLLMIR) in the LLEs so far. In order to have a comprehensive understanding of the HLLMIR and explore the current research status and development trend of LLEs, this article provides a systematic review of the HLLMIR research for LLEs. First, the HMI mechanism and understanding are fully illustrated, and the HMIR tasks pertaining to lower limb motions (LLMs) are elaborated on. Next, the intention-related sensing signals with different sources are dissected in detail, including bioelectric signals of electroencephalography (EEG) and electromyogram (EMG), biomechanical signals, and multisource signals fusion. The HMIR methods for the LLEs are thoroughly addressed and analyzed, the methods are categorized as model-based, such as the musculoskeletal model and the model-free method involving heuristic rule-based, conventional machine learning (ML)-based, and deep learning (DL)-based. Finally, an overall discussion on the recognition tasks, sensing signals, recognition methods, and performance assessments is given, and thus, the research challenges of the HLLMIR are summarized and prospected.