How to Achieve Human–Machine Interaction by Foot Gesture Recognition: A Review

Researchers are investigating how to make machines read our body language to make human–machine interaction (HMI) more intelligent and efficient. The lower limbs contain a variety of gestures, and it is also one of the most effective ways to express body information. Therefore, foot gesture recognition (FGR) has become a popular technology for human–machine interface with simple, fast, and accurate features. To give the reader a quick overview of the current state of FGR research, this review takes the sensing methods used in the FGR technology as an entry point, introduces different sensing methods, machine learning algorithms, and applications, and discusses the limitations and future work on FGR systems. The results show that the mainstream sensing methods for FGR are plantar pressure, inertial, visual, surface electromyography (sEMG), and ultrasound (US). Current applications of FGR are simplified control, medical rehabilitation, virtual reality (VR), and smart prosthetics. Research on hybrid sensing methods and deep learning algorithms has gradually increased in recent years. Future research will focus on designing sensor hardware that can respond to environmental changes, using multimodal sensing for interaction, and designing more comfortable and portable FGR systems.