The ability to monitor activities of daily living in the natural environments of patients could become a valuable tool for various clinical applications. In this paper, we show that a simple algorithm is capable of classifying manual activities of daily living (ADL) into categories using data from wrist- and finger-worn sensors. Six participants without pathology of the upper limb performed 14 ADL. Gyroscope signals were used to analyze the angular velocity pattern for each activity. The elaboration of the algorithm was based on the examination of the activity at the different levels (hand, fingers and wrist) and the relationship between them for the duration of the activity. A leave-one-out cross-validation was used to validate our algorithm. The algorithm allowed the classification of manual activities into five different categories through three consecutive steps, based on hands ratio (i.e., activity of one or both hands) and fingers-to-wrist ratio (i.e., finger movement independently of the wrist). On average, the algorithm made the correct classification in 87.4% of cases. The proposed algorithm has a high overall accuracy, yet its computational complexity is very low as it involves only averages and ratios.

中文翻译：

---

结果测量的临床应用


监测患者在自然环境中日常生活活动的能力可能成为各种临床应用的宝贵工具。在本文中，我们展示了一种简单的算法，能够使用腕戴式和指戴式传感器的数据将日常生活手动活动 (ADL) 分类。六名没有上肢病变的参与者进行了 14 ADL。陀螺仪信号用于分析每个活动的角速度模式。该算法的制定基于对不同级别（手、手指和手腕）的活动以及活动持续时间内它们之间的关系的检查。使用留一交叉验证来验证我们的算法。该算法根据手部比例（即一只手或双手的活动）和手指与手腕的比例（即独立于手腕的手指运动），通过三个连续步骤将手动活动分为五个不同的类别。平均而言，该算法在 87.4% 的情况下做出了正确分类。所提出的算法具有较高的整体精度，但其计算复杂度非常低，因为它只涉及平均值和比率。