Human–Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review

作者：Zongxing Lu, He Baizheng, Yingjie Cai, Bingxing Chen, Ligang Yao, Huang Haibin, Liu Zhoujie · 发表于：IEEE Sensors Journal · 年份：2023 · DOI：10.1109/jsen.2023.3314104 · 被引用次数：33 · 研究领域：Muscle activation and electromyography studies、Advanced Sensor and Energy Harvesting Materials、EEG and Brain-Computer Interfaces

The gesture recognition (GR) technology as one of the human–machine interfaces can conveniently and effectively express the intention of human and has become the hot research hot spot in recent years. Force level is a key factor while GR for more dexterous and natural prosthetic control. To provide researchers with a systematic and quick overview of the relevant and future developments in GR and force assessment (FA) techniques, this review synthesizes current commonly used sensor interfaces, data processing methods, and methods that have improved recognition performance. The experimental design and related results of GR and FA with various types of sensors are analyzed and compared to understand the scope of application and recognition performance of different sensors. This review summarizes the challenges and future work in the five areas of hardware, use environment, broad applicability, physiological factors, and comfort of use in practical applications. Finally, the conclusion prospects that future research may need to focus on improving model generalization and robustness to environmental, physiological factors, and so on by building large datasets and developing flexible, long-lasting, lightweight, and senseless, high-performance interfaces.