sEMG Based Human Motion Intention Recognition

Abstract

Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

“…A similar study implemented several machine learning classification algorithms to decode discrete hand motion intention using high-density transient EMG signals with a short window (only 150 ms) [47]. A survey on the sEMG technique reported it was adopted in discrete arm motions using classification (approximately 73-98%) and in continuous arm motions and forces using regression (approximately 84-93%) involving offline machine learning and deep learning techniques [48]. Although there is a gap between offline and online performances using sEMG signals, interestingly, the FMG technique was found to perform better in online classification and regression than the sEMG technique [49].…”

Inclusive Human Intention Prediction with Wearable Sensors: Machine Learning Techniques for the Reaching Task Use Case

“…Predicting human intentions by collecting and analyzing body signals is one of the main goals in human-robot interaction [1]. Accurate and real-time recognition of human motion intention could help in achieving suitable human-machine coordination [2] for both interactive robotic interfaces, like collaborative robots, and diagnostic systems, such as rehabilitation devices [3].…”

“…Several kinds of sensors are currently used to detect body signals, like surface electromyography [2,4], electroencephalography [3], and accelerometers. In recent years, research in human movement pattern recognition with the support of wearable sensors was widely conducted [2,3,5], also considering the effect of the positioning of the sensors in the obtained data [6,7].…”

“…Several kinds of sensors are currently used to detect body signals, like surface electromyography [2,4], electroencephalography [3], and accelerometers. In recent years, research in human movement pattern recognition with the support of wearable sensors was widely conducted [2,3,5], also considering the effect of the positioning of the sensors in the obtained data [6,7]. Actually, wearable sensors allow noninvasive motion detection, full integration with commercially available devices [1], the possibility to acquire acceleration and velocity to reconstruct the detected movement [8], and the adaptation to inter-and intra-individual variability [9].…”

“…Actually, wearable sensors allow noninvasive motion detection, full integration with commercially available devices [1], the possibility to acquire acceleration and velocity to reconstruct the detected movement [8], and the adaptation to inter-and intra-individual variability [9]. Since body signals are strongly affected by repeatability lack [2] and motion is subject-dependent, the challenge in predicting human intention increases in specific scenarios, like in a clinical environment, where the pathological subject can present peculiar motion patterns. In particular, laboratory-based optical motion analysis systems are widely adopted for periodical stroke condition assessment during rehabilitation [10] to obtain multiple bio-signals, useful in recognizing pathological symptoms and improving the healing rate of rehabilitation [11].…”