背景:体力活动正在成为一种结果指标。加速度计已成为监测物理行为的重要工具,新的识别方法分析方法增加了细节的程度。许多研究通过使用多个可穿戴传感器在身体行为分类方面取得了高性能;然而,多个可穿戴设备可能是不切实际的,并且合规性较低。
目的:这项研究的目的是开发和验证一种算法,用于使用单个大腿安装的加速度计和监督的机器学习方案对几种日常身体行为进行分类。
方法:我们通过添加行为类来收集训练数据-运行,骑自行车,爬楼梯,轮椅行走,和车辆驾驶-使用现有的算法,说谎,站立,走路,和过渡。组合训练数据后,我们使用随机森林学习方案进行模型开发。我们通过使用胸部安装的摄像机建立地面真相的模拟自由生活程序验证了该算法。此外,我们调整了我们的算法,并将性能与现有的基于向量阈值的算法进行了比较。
结果:我们开发了一种算法来对11种与康复相关的身体行为进行分类。在模拟的自由生活验证中,该算法的性能下降到57%的平均11类(F-measure)。将班级合并为久坐行为后,站立,走路,跑步,骑自行车,结果表明,与地面实况和现有算法相比,性能更高。
结论:使用单个大腿安装的加速度计,我们在特定行为中获得了较高的分类水平。具有高水平表现的行为大多发生在功能水平较高的人群中。进一步的发展应旨在描述功能水平较低的人群中的行为。
BACKGROUND: Physical activity is emerging as an outcome measure. Accelerometers have become an important tool in monitoring physical behavior, and newer analytical approaches of recognition methods increase the degree of details. Many studies have achieved high performance in the classification of physical behaviors through the use of multiple wearable sensors; however, multiple wearables can be impractical and lower compliance.
OBJECTIVE: The aim of this study was to develop and validate an algorithm for classifying several daily physical behaviors using a single thigh-mounted accelerometer and a supervised machine-learning scheme.
METHODS: We collected training data by adding the behavior classes-running, cycling, stair climbing, wheelchair ambulation, and vehicle driving-to an existing algorithm with the classes of sitting, lying, standing, walking, and transitioning. After combining the training data, we used a random forest learning scheme for model development. We validated the algorithm through a simulated free-living procedure using chest-mounted cameras for establishing the ground truth. Furthermore, we adjusted our algorithm and compared the performance with an existing algorithm based on vector thresholds.
RESULTS: We developed an algorithm to classify 11 physical behaviors relevant for rehabilitation. In the simulated free-living validation, the performance of the algorithm decreased to 57% as an average for the 11 classes (F-measure). After merging classes into sedentary behavior, standing, walking, running, and cycling, the result revealed high performance in comparison to both the ground truth and the existing algorithm.
CONCLUSIONS: Using a single thigh-mounted accelerometer, we obtained high classification levels within specific behaviors. The behaviors classified with high levels of performance mostly occur in populations with higher levels of functioning. Further development should aim at describing behaviors within populations with lower levels of functioning.