Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:109-114. doi: 10.1109/EMBC48229.2022.9871946.
Automatic lying posture tracking is an important factor in human health monitoring. The increasing popularity of the wrist-based trackers provides the means for unobtrusive, affordable, and long-term monitoring with minimized privacy concerns for the end-users and promising results in detecting the type of physical activity, step counting, and sleep quality assessment. However, there is limited research on development of accurate and efficient lying posture tracking models using wrist-based sensor. Our experiments demonstrate a major drop in the accuracy of the lying posture tracking using wrist-based accelerometer sensor due to the unpredictable noise from arbitrary wrist movements and rotations while sleeping. In this paper, we develop a deep transfer learning method that improves performance of lying posture tracking using noisy data from wrist sensor by transferring the knowledge from an initial setting which contains both clean and noisy data. The proposed solution develops an optimal mapping model from the noisy data to the clean data in the initial setting using LSTM sequence regression, and reconstruct clean synthesized data in another setting where no noisy sensor data is available. This increases the lying posture tracking F1-Score by 24.9% for 'left-wrist' and by 18.1% for 'right-wrist' sensors comparing to the case without mapping.
自动躺卧姿势跟踪是人体健康监测的一个重要因素。腕部追踪器的日益普及为非侵入性、经济实惠且长期的监测提供了手段，最大限度地减少了最终用户的隐私问题，并在检测身体活动类型、计步和睡眠质量评估方面取得了有前景的结果。然而，使用腕部传感器开发准确高效的躺卧姿势跟踪模型的研究还很有限。我们的实验表明，由于睡眠时手腕的任意运动和旋转产生的不可预测噪声，基于腕部加速度计传感器的躺卧姿势跟踪的准确性会大幅下降。在本文中，我们开发了一种深度迁移学习方法，通过从包含清洁数据和噪声数据的初始设置中转移知识，提高了基于腕部传感器的噪声数据的躺卧姿势跟踪性能。所提出的解决方案使用 LSTM 序列回归在初始设置中从噪声数据到清洁数据建立最优映射模型，并在没有噪声传感器数据的另一个设置中重建清洁的合成数据。与没有映射的情况相比，“左手腕”传感器的躺卧姿势跟踪 F1 分数提高了 24.9%，“右手腕”传感器的躺卧姿势跟踪 F1 分数提高了 18.1%。
