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Monitoring the compliance of social distancing is critical for schools and offices to recover in-person operations in indoor spaces from the COVID-19 pandemic. Existing systems focus on vision- and wearable-based sensing approaches, which require direct line-of-sight or device-carrying and may also raise privacy concerns. To overcome these limitations, we introduce a new monitoring system for social distancing compliance based on footstep-induced floor vibration sensing. This system is device-free, non-intrusive, and perceived as more privacy-friendly. Our system leverages the insight that footsteps closer to the sensors generate vibration signals with larger amplitudes. The system first estimates the location of each person relative to the sensors based on signal energy and then infers the distance between two people. We evaluated the system through a real-world experiment with 8 people, and the system achieves an average accuracy of 97.8% for walking scenario classification and 80.4% in social distancing violation detection.
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This content will become publicly available on September 3, 2026
Human Heterogeneity Invariant Stress Sensing
Stress affects physical and mental health, and wearable devices have been widely used to detect daily stress through physiological signals. However, these signals vary due to factors such as individual differences and health conditions, making generalizing machine learning models difficult. To address these challenges, we present Human Heterogeneity Invariant Stress Sensing (HHISS), a domain generalization approach designed to find consistent patterns in stress signals by removing person-specific differences. This helps the model perform more accurately across new people, environments, and stress types not seen during training. Its novelty lies in proposing a novel technique called person-wise sub-network pruning intersection to focus on shared features across individuals, alongside preventing overfitting by leveraging continuous labels while training. The present study focuses on people with opioid use disorder (OUD)---a group where stress responses can change dramatically depending on the presents of opioids in their system, including daily timed medication for OUD (MOUD). Since stress often triggers cravings, a model that can adapt well to these changes could support better OUD rehabilitation and recovery. We tested HHISS on seven different stress datasets---four which we collected ourselves and three public datasets. Four are from lab setups, one from a controlled real-world driving setting, and two are from real-world in-the-wild field datasets with no constraints. The present study is the first known to evaluate how well a stress detection model works across such a wide range of data. Results show HHISS consistently outperformed state-of-the-art baseline methods, proving both effective and practical for real-world use. Ablation studies, empirical justifications, and runtime evaluations confirm HHISS's feasibility and scalability for mobile stress sensing in sensitive real-world applications.
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- PAR ID:
- 10657055
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 9
- Issue:
- 3
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 42
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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