Beyond Thresholds: A General Approach to Sensor Selection for Practical Deep Learning-based HAR

Geffen Cooper, Radu Marculescu

Abstract: In deep learning (DL) based human activity recognition (HAR), sensor selection seeks to balance prediction accuracy and sensor utilization (how often a sensor is used). With advances in on-device inference, sensors have become tightly integrated with DL, often restricting access to the underlying model used. Given only sensor predictions, how can we derive a selection policy which does efficient classification while maximizing accuracy? We propose a cascaded inference approach which, given the prediction of any one sensor, determines whether to query all other sensors. Typically, cascades use a sequence of classifiers which terminate once the confidence of a classifier exceeds a threshold. However, a threshold-based policy for sensor selection may be suboptimal; we define a more general class of policies which can surpass the threshold. We extend to settings where little or no labeled data is available for tuning the policy. Our analysis is validated on three HAR datasets by improving upon the F1-score of a threshold policy across several utilization budgets. Overall, our work enables practical analytics for HAR by relaxing the requirement of labeled data for sensor selection and reducing sensor utilization to directly extend a sensor system’s lifetime.