Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model
Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist. We study whether a deep learning model trained on hip-worn accelerometer data can transfer to wrist-worn accelerometer data for sitting versus non-sitting classification. We use CHAP, a CNN-BiLSTM model originally developed for hip accelerometers, and evaluate its zero-shot performance on wrist data as well as its adaptation through finetuning with varying amounts of labeled wrist data. Experiments are conducted on the iWatch dataset with ground-truth posture labels derived from we...
| Authors | Yuliang Chen, Weiwei Shi, Jingjing Zou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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