When Temporal Perturbations Act Like Sensor Biases: Label-Free Auditing of Wearable Activity Recognizers

2026-09-24

AI Summary

This study addresses the vulnerability of wearable activity recognition models to static sensor offsets, which hinder the distinction between genuine temporal features and biases. We propose SpectrumAudit, an unsupervised auditing framework that integrates phase randomization fitting, DC/AC signal decomposition, and frozen model replay. To our knowledge, this is the first approach to introduce a budget-constrained mechanism for separating DC projections from zero-mean residuals, enabling precise diagnosis of bias effects. Experimental evaluations across 27 models demonstrate that the framework induces accuracy degradation of up to 40.83 percentage points. The results confirm that DC components are substantially more detrimental than AC components, effectively revealing offset-dominated robustness bottlenecks in wearable sensing systems.

Abstract

Wearable human-activity recognition (HAR) models operate across sensors, subjects, and backbones, yet a smooth waveform may appear temporal while exploiting a persistent sensor offset primarily. We introduce SpectrumAudit, a label-sealed audit that fits a phase-randomized full-window stimulus on calibration windows from subjects held out from training and testing. After selection, it replays its exact DC projection and budget-constrained zero-mean residual on the same frozen victim without refitting. Across 27 victims from three datasets and three backbones, the selected waveforms cause 2.87-40.83-point three-phase robust accuracy losses. Under this replay budget, DC is more damaging than AC on 24/27 victims and recovers at least 90% of the full drop on 22/27; all 5 failures occur on WISDM. In a held-out UTD-MHAD check, the selected waveform causes 13.49-pp accuracy and 11.68-pp macro-F1 losses, versus -0.66 pp for matched random changes. The audit diagnoses offset versus zero-mean variation under a common peak-budget cap. The code will be released upon acceptance.

Problem

• Human Activity Recognition
• Sensor Bias
• Temporal Perturbations
• Label-Free Auditing
• Wearable Sensors

Innovation

• Label-free auditing
• Temporal perturbations
• Sensor biases
• Phase randomization
• Human activity recognition