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使用肌肉活动和加速度计测量久坐行为。

Measuring Sedentary Behavior by Means of Muscular Activity and Accelerometry.

机构信息

Division of Physiotherapy, Department of Neurobiology, Care Science and Society, Karolinska Institutet, 141 83 Stockholm, Sweden.

Institute of Mechanical Systems, School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland.

出版信息

Sensors (Basel). 2018 Nov 17;18(11):4010. doi: 10.3390/s18114010.

Abstract

Sedentary Behavior (SB) is among the most frequent human behaviors and is associated with a plethora of serious chronic lifestyle diseases as well as premature death. Office workers in particular are at an increased risk due to their extensive amounts of occupational SB. However, we still lack an objective method to measure SB consistent with its definition. We have therefore developed a new measurement system based on muscular activity and accelerometry. The primary aim of the present study was to calibrate the new-developed 8-CH-EMG+ for measuring occupational SB against an indirect calorimeter during typical desk-based office work activities. In total, 25 volunteers performed nine office tasks at three typical workplaces. Minute-by-minute posture and activity classification was performed using subsequent decision trees developed with artificial intelligence data processing techniques. The 8-CH-EMG+ successfully identified all sitting episodes (AUC = 1.0). Furthermore, depending on the number of electromyography channels included, the device has a sensitivity of 83⁻98% and 74⁻98% to detect SB and active sitting (AUC = 0.85⁻0.91). The 8-CH-EMG+ advances the field of objective SB measurements by combining accelerometry with muscular activity. Future field studies should consider the use of EMG sensors to record SB in line with its definition.

摘要

久坐行为(SB)是最常见的人类行为之一,与许多严重的慢性生活方式疾病以及过早死亡有关。由于大量的职业性 SB,特别是办公室工作人员处于更高的风险之中。然而,我们仍然缺乏一种与定义一致的客观测量 SB 的方法。因此,我们根据肌肉活动和加速度计开发了一种新的测量系统。本研究的主要目的是根据间接热量计,对新开发的 8 通道肌电图+(8-CH-EMG+)在典型的基于办公桌的办公活动中测量职业性 SB 进行校准。总共有 25 名志愿者在三个典型的工作场所完成了九项办公任务。使用后续的决策树,通过人工智能数据处理技术对每分钟的姿势和活动分类进行了分类。8-CH-EMG+成功识别了所有坐姿(AUC=1.0)。此外,根据包含的肌电图通道数量,该设备检测 SB 和主动坐姿的灵敏度为 83%-98%和 74%-98%(AUC=0.85-0.91)。8-CH-EMG+通过将加速度计与肌肉活动相结合,推动了客观 SB 测量领域的发展。未来的现场研究应考虑使用肌电图传感器根据定义来记录 SB。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e996/6263709/a4f5f4a4990b/sensors-18-04010-g001.jpg

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