This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy.

In this paper, we propose a virtual reality (VR)-based teleoperation system that utilizes Electrical Muscle Stimulation (EMS) to provide haptic guidance for gesture-based mobile robot control. We develop an operator-independent, posture-aware regression model that predicts wrist movement directions induced by EMS across the control space. This estimation model is integrated into a VR teleoperation system that evaluates obstacle proximity and actuates the user’s muscles to safely steer their gestures away from obstacles, thereby preventing collisions. To validate the efficacy of the proposed system, we conducted a user study comparing EMS guidance, vibration guidance, and no-guidance conditions for a mobile robot teleoperation task. The study revealed that our system provides two distinct benefits. First, as a shared control interface, it significantly improved navigation safety. Second, participants who underwent EMS training maintained improved navigation performance even without guidance. These findings indicate that the proposed method functions effectively as a real-time safety mechanism while supporting the internalization of safer control strategies.