Lightweight Cross-Device Sleep Tracking on the WeBe Wearable Platform

TL;DR

This paper introduces a simple, reproducible sleep tracking method that operates directly on raw accelerometer data, achieving accurate results across different wearable devices and datasets.

Contribution

The authors present a lightweight, device-agnostic sleep tracking pipeline that works directly on raw signals and is validated across multiple datasets and devices.

Findings

Achieves a mean absolute error of 41.6 minutes in TST on MMASH dataset.

On WeBe data, achieves a mean TST error of 27.4 minutes.

Outperforms a commercial ActiGraph pipeline in accuracy.

Abstract

Wearable devices are widely used for continuous health monitoring, yet reliable sleep tracking on emerging platforms remains underexplored due to reliance on proprietary algorithms and device-specific activity representations. We present a lightweight and reproducible sleep tracking pipeline that operates directly on raw accelerometer signals. The method converts data into epoch-level activity features, applies temporal smoothing and normalized scoring, and performs sleep/wake classification using a globally calibrated threshold. We calibrate the model on the Multilevel Monitoring of Activity and Sleep in Healthy People (MMASH) dataset and evaluate it in a cross-device study using the WeBe wearable platform and a commercial ActiGraph device. On MMASH, the method achieves a mean absolute error of 41.6 minutes in Total Sleep Time (TST), with onset and offset errors of 6.3 and 7.4 minutes. On real-world WeBe data from three participants across five sessions, it achieves a mean TST error of 27.4 minutes and onset and offset errors of 13.9 and 8.0 minutes. In contrast, a commercial ActiGraph pipeline shows larger discrepancies relative to ground truth. These results demonstrate accurate and generalizable sleep tracking using a simple and reproducible pipeline.