PDF(Pubmed)
Abstract：
BACKGROUND: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS.
METHODS: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels.
RESULTS: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen\'s kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types.
CONCLUSIONS: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.
METHODS: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels.
RESULTS: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen\'s kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types.
CONCLUSIONS: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.
摘要：
背景：我们在自由生活条件下评估人类的身体行为越准确，我们就越能更好地理解其与健康和福祉的关系。大腿磨损的加速度计可用于高精度地识别基本活动类型以及不同姿势。无需专门编程的用户友好的软件可以支持采用这种方法。本研究旨在评估两种新颖的无代码分类方法的分类精度，即SENS运动和ActiPASS。
方法：38名健康成年人（30.8±9.6岁；53％的女性）在各种体育活动中在大腿上佩戴SENS运动加速度计（12.5Hz；±4g）。参与者在实验室中完成了强度不同的标准化活动。活动包括散步,跑步,骑自行车,坐着,站立,躺下.随后，参与者在实验室外进行不受限制的自由生活活动,同时使用胸部摄像头进行录像.使用预定义的标签方案对视频进行了注释，并将注释作为自由生活条件的参考。将SENS运动软件和ActiPASS软件的分类输出与参考标签进行比较。
结果：共分析了63.6小时的活性数据。我们观察到两种分类算法及其各自在两种条件下的参考之间的高度一致性。在自由生活条件下,科恩的卡帕系数为SENS为0.86，ActiPASS为0.92。在所有活动类型中，SENS的平均平衡精度范围为0.81（骑自行车）至0.99（跑步），ActiPASS的平均平衡精度范围为0.92（步行）至0.99（久坐）。
结论：研究表明，两种可用的无代码分类方法可用于准确识别基本的身体活动类型和姿势。我们的结果强调了基于相对较低采样频率数据的两种方法的准确性。分类方法表现出差异，在自由生活骑自行车（SENS）和慢速跑步机步行（ActiPASS）中观察到较低的敏感性。这两种方法都使用不同定义的活动类的不同集合，这可以解释观察到的差异。我们的结果支持使用SENS运动系统和两种无代码分类方法。
方法：38名健康成年人（30.8±9.6岁；53％的女性）在各种体育活动中在大腿上佩戴SENS运动加速度计（12.5Hz；±4g）。参与者在实验室中完成了强度不同的标准化活动。活动包括散步,跑步,骑自行车,坐着,站立,躺下.随后，参与者在实验室外进行不受限制的自由生活活动,同时使用胸部摄像头进行录像.使用预定义的标签方案对视频进行了注释，并将注释作为自由生活条件的参考。将SENS运动软件和ActiPASS软件的分类输出与参考标签进行比较。
结果：共分析了63.6小时的活性数据。我们观察到两种分类算法及其各自在两种条件下的参考之间的高度一致性。在自由生活条件下,科恩的卡帕系数为SENS为0.86，ActiPASS为0.92。在所有活动类型中，SENS的平均平衡精度范围为0.81（骑自行车）至0.99（跑步），ActiPASS的平均平衡精度范围为0.92（步行）至0.99（久坐）。
结论：研究表明，两种可用的无代码分类方法可用于准确识别基本的身体活动类型和姿势。我们的结果强调了基于相对较低采样频率数据的两种方法的准确性。分类方法表现出差异，在自由生活骑自行车（SENS）和慢速跑步机步行（ActiPASS）中观察到较低的敏感性。这两种方法都使用不同定义的活动类的不同集合，这可以解释观察到的差异。我们的结果支持使用SENS运动系统和两种无代码分类方法。
