MoveTouch: Robotic Motion Capturing System with Wearable Tactile Display to Achieve Safe HRI

TL;DR

MoveTouch addresses safe human–robot interaction by coupling a wearable 2-DOF device that maintains ArUco marker visibility with a wrist-based vibrotactile guidance system. The approach integrates a differential gear train for marker orientation, an ESP32-based electronics suite, and a single RGB mocap setup to translate marker position into the UR10 coordinate frame, enabling collision avoidance. The paper reports a two-stage study: first, pattern recognition across volar and dorsal wrist sites to select robust cues (four patterns: 1L, 2L, 3L, 5H); second, a real-world collaborative task demonstrating preserved safety distance and quantifiable response times (0.24–2.41 s). Findings show higher recognition on the volar wrist and feasible, timely human responses to cues, suggesting practical safety benefits and a design path for broader deployment and optimization in HRI settings.

Abstract

The collaborative robot market is flourishing as there is a trend towards simplification, modularity, and increased flexibility on the production line. But when humans and robots are collaborating in a shared environment, the safety of humans should be a priority. We introduce a novel wearable robotic system to enhance safety during Human-Robot Interaction (HRI). The proposed wearable robot is designed to hold a fiducial marker and maintain its visibility to a motion capture system, which, in turn, localizes the user's hand with good accuracy and low latency and provides vibrotactile feedback to the user's wrist. The vibrotactile feedback guides the user's hand movement during collaborative tasks in order to increase safety and enhance collaboration efficiency. A user study was conducted to assess the recognition and discriminability of ten designed vibration patterns applied to the upper (dorsal) and the down (volar) parts of the user's wrist. The results show that the pattern recognition rate on the volar side was higher, with an average of 75.64% among all users. Four patterns with a high recognition rate were chosen to be incorporated into our system. A second experiment was carried out to evaluate users' response to the chosen patterns in real-world collaborative tasks. Results show that all participants responded to the patterns correctly, and the average response time for the patterns was between 0.24 and 2.41 seconds.

Figures (6)

• Figure 1: MoveTouch, a novel wearable robot for position tracking and haptic feedback. An ArUco marker is located at the end effector to adjust its orientation, while five vibration motors deliver tactile feedback to the user.
• Figure 2: MoveTouch design: 3D model perspectives (a) isometric view, (b) top view revealing the gear structure.
• Figure 3: The designed tactile patterns include: 1) Right-to-left propagation; 2) Left-to-right propagation; 3) Center-to-outside propagation; 4) Outside-to-center propagation; and 5) Simultaneous activation of all vibration motors.
• Figure 4: The experimental setup: a) The pattern recognition experiment setup. b) The system evaluation in a real-world collaborative task setup.
• Figure 5: The distance between the participant and the robot's TCP during one experiment. The red line is the critical distance, while the orange line represents the haptic activation distance. The four highlighted areas are the areas where the haptic patterns were activated.