Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field

TL;DR

This work investigates collaborative activity recognition using passive inter-body electrostatic field sensing as a low-power wearable modality to complement traditional accelerometers. It introduces a wrist-worn electrostatic sensing front end and an associated dataset collected during TV-Wall assembly tasks to study independent and joint actions. Results show that single-sensor electrostatic-field recognition underperforms relative to accelerometers for individual activities, but sensor fusion, especially across wrist and calf accelerometers with the electrostatic modality, improves collaborative activity detection, achieving up to 16% gains and up to 0.82–0.91 macro-F scores in pairwise settings. The findings highlight both hardware and methodological opportunities for leveraging inter-body electrostatic interactions in practical, energy-efficient multi-user wearable sensing for group activities.

Abstract

The passive body-area electrostatic field has recently been aspiringly explored for wearable motion sensing, harnessing its two thrilling characteristics: full-body motion sensitivity and environmental sensitivity, which potentially empowers human activity recognition both independently and jointly from a single sensing front-end and theoretically brings significant competition against traditional inertial sensor that is incapable in environmental variations sensing. While most works focus on exploring the electrostatic field of a single body as the target, this work, for the first time, quantitatively evaluates the mutual effect of inter-body electrostatic fields and its contribution to collaborative activity recognition. A wearable electrostatic field sensing front-end and wrist-worn prototypes are built, and a sixteen-hour, manually annotated dataset is collected, involving an experiment of manipulating objects both independently and collaboratively. A regression model is finally used to recognize the collaborative activities among users. Despite the theoretical advantages of the body electrostatic field, the recognition of both single and collaborative activities shows unanticipated less-competitive recognition performance compared with the accelerometer. However, It is worth mentioning that this novel sensing modality improves the recognition F-score of user collaboration by 16\% in the fusion result of the two wearable motion sensing modalities, demonstrating the potential of bringing body electrostatic field as a complementary power-efficient signal for collaborative activity tracking using wearables.

Figures (7)

• Figure 1: Example of the type of recognition tasks that our work partly targets: distinguishing group agents among normal walking next to each other, walking next to each other and operating an object alone, and walking next to each other while jointly operating an object. Co-location is not a solid separator, and camera filming is often limited by line of sight.
• Figure 2: Basic structure of a body capacitance sensing method
• Figure 3: A wearable prototype for body motion sensing
• Figure 4: Collaborative Work and the Sensing Modality
• Figure 5: Example Session of Labeling and Preprocessed Signal