Key result
Deep temporal decomposition improves battery capacity degradation prediction, maintaining mean squared error within ~20%.
Why the study?
Batteries in wearable medical devices show complex, nonlinear degradation patterns from irregular charging and intermittent high loads, challenging accurate capacity degradation prediction critical for continuous operation and patient safety.
A novel deep temporal decomposition model accurately predicts battery capacity degradation in wearable medical devices, maintaining prediction error within 20%.
May reduce unexpected failure in long-term cardiac monitoring; leaves open prospective validation in real-world device studies.
Accurate prediction of battery capacity degradation is critical for the continuous operation of wearable medical devices and patient safety. However, batteries in these devices often show complex, nonlinear degradation patterns caused by irregular charging cycles and intermittent high loads. To address this, we propose a novel framework that integrates deep temporal decomposition with autoregressive fine-tuning. Specifically, a multi-scale feature extraction network is designed to capture both local fluctuations and global trends. A dedicated temporal decomposition module then separates long-term trends from short-term noise, effectively mitigating noise arising from wearable battery operating conditions. Additionally, an autoregressive residual correction mechanism is introduced to correct iterative prediction errors, thereby reducing cumulative bias in long-term forecasts. Extensive experiments on three diverse datasets demonstrate the improved performance of the proposed method, maintaining the Mean Squared Error within 20% across the evaluated datasets and experimental settings.
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Hu et al. (2026) studied Battery capacity degradation in wearable medical devices (n=56). Deep temporal decomposition model with autoregressive fine-tuning vs. DLinear, BiGRU, TCN, Hybrid Model, and Transformer was evaluated on Mean Squared Error (MSE) of battery capacity degradation trajectory prediction. The proposed deep temporal decomposition model improved battery capacity degradation trajectory prediction, maintaining the Mean Squared Error within 20% across diverse datasets.
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