Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
Sensors (Basel). 2017 Mar 10;17(3):559. doi: 10.3390/s17030559.
Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects' movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects' movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen's Kappa, corrected kappa, Krippendorff's alpha and Fleiss' kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.
身体活动监测算法通常是在不代表实际生活活动的条件下开发的，不是针对目标人群开发的，或者没有标记到足够高的分辨率，无法捕捉到人类运动的真实细节。我们设计了一种半结构化的监督实验室基础活动协议和一种非监督的自由生活活动协议，并记录了 20 名老年人在佩戴多达 12 个身体传感器的情况下同时执行这两种协议。使用同步摄像机（≥25 fps）记录受试者的运动，摄像机部署在实验室环境中，以捕捉协议的实验室部分，以及用于实验室外活动的佩戴式摄像机。使用 11 个不同的类别标签，由五名评估员对受试者的运动进行视频标记。总体一致性水平很高（一致性百分比>90.05%，以及 Cohen's Kappa、校正 Kappa、Krippendorff's alpha 和 Fleiss' kappa >0.86）。共记录了 43.92 小时的活动，包括 9.52 小时的实验室活动和 34.41 小时的实验室外活动。在实验室和实验室外场景中，分别记录了计划过渡的 88.37%和 152.01%。本研究产生了迄今为止最详细的惯性传感器数据数据集，与在自由生活环境中从独立生活的老年人记录的高帧率（≥25 fps）视频标记数据同步。该数据集适合验证现有的活动分类系统和开发新的活动分类算法。
