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Wavelet-Based Analysis of Physical Activity and Sleep Movement Data from Wearable Sensors among Obese Adults

机译：基于小波的肥胖成年人可穿戴式传感器的身体活动和睡眠运动数据分析

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Decreased physical activity in obese individuals is associated with a prevalence of cardiovascular and metabolic disorders. Physicians usually recommend that obese individuals change their lifestyle, specifically changes in diet, exercise, and other physical activities for obesity management. Therefore, understanding physical activity and sleep behavior is an essential aspect of obesity management. With innovations in mobile and electronic health care technologies, wearable inertial sensors have been used extensively over the past decade for monitoring human activities. Despite significant progress with the wearable inertial sensing technology, there is a knowledge gap among researchers regarding how to analyze longitudinal multi-day inertial sensor data to explore activities of daily living (ADL) and sleep behavior. The purpose of this study was to explore new clinically relevant metrics using movement amplitude and frequency from longitudinal wearable sensor data in obese and non-obese young adults. We utilized wavelet analysis to determine movement frequencies on longitudinal multi-day wearable sensor data. In this study, we recruited 10 obese and 10 non-obese young subjects. We found that obese participants performed more low-frequency (0.1 Hz) movements and fewer movements of high frequency (1.1–1.4 Hz) compared to non-obese counterparts. Both obese and non-obese subjects were active during the 00:00–06:00 time interval. In addition, obesity affected sleep with significantly fewer transitions, and obese individuals showed low values of root mean square transition accelerations throughout the night. This study is critical for obesity management to prevent unhealthy weight gain by the recommendations of physical activity based on our results. Longitudinal multi-day monitoring using wearable sensors has great potential to be integrated into routine health care checkups to prevent obesity and promote physical activities.
机译：肥胖个体的体育活动减少与心血管疾病和代谢疾病的患病率有关。医师通常建议肥胖者改变生活方式，特别是改变饮食，运动和其他体育活动以进行肥胖管理。因此，了解身体活动和睡眠行为是肥胖管理的重要方面。随着移动和电子医疗技术的创新，可穿戴式惯性传感器在过去十年中已广泛用于监视人类活动。尽管可穿戴式惯性传感技术取得了重大进展，但研究人员之间在如何分析纵向多日惯性传感器数据以探索日常生活活动（ADL）和睡眠行为方面存在知识差距。这项研究的目的是使用来自肥胖和非肥胖年轻人的纵向可穿戴传感器数据的运动幅度和频率探索新的临床相关指标。我们利用小波分析来确定纵向多天可穿戴传感器数据的运动频率。在这项研究中，我们招募了10名肥胖者和10名非肥胖青年对象。我们发现，与非肥胖参与者相比，肥胖参与者表现出更多的低频（0.1 Hz）运动和更少的高频（1.1–1.4 Hz）运动。肥胖和非肥胖受试者在00：00-06：00时间间隔内均处于活动状态。此外，肥胖症影响睡眠的过渡时间明显减少，肥胖个体整夜的均方根过渡加速度值较低。这项研究对于肥胖管理至关重要，它可以根据我们的研究结果通过体育锻炼的建议来防止体重增加不健康。使用可穿戴式传感器进行的纵向多日监测具有很大的潜力，可以集成到常规的健康检查中，以防止肥胖和促进体育锻炼。

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