Probabilistic Person-in-Bed Detection Using Accelerometer Signals

Using accelerometer data in smart bed systems offers a cost-effective solution for person-in-bed detection. In this work, we propose a lightweight probabilistic model for this task. The accelerometer time series is first divided into multiple patches, with high-frequency noise filtered through a combined optimization of 1D convolution and spectral pooling. An LSTM-based feature extraction module is then employed to capture temporal dependencies. Subsequently, a Fourier classification head is applied to generate probabilistic detection outputs. The proposed architecture achieves an accuracy of 1.0 on the segmented detection task and 0.915 on the streaming detection task in the ICASSP 2025 signal Processing Grand Challenge, organized by the Analog Garage. The implementation is available at https://github.com/JiayiGao04/person-in-bed-detection.