开源数据与代码，用加速度计区分五类日常活动
An Open-Source, Open Data Approach to Activity Classification from Triaxial Accelerometry in an Ambulatory Setting
- 用50Hz三轴加速度计采集23人活动数据，构建分类模型
- 二分类准确率F1达0.79，五类活动识别F1达0.83
- 数据与代码全开源，适合医疗监测与健康算法研究者使用
加速度计已成为广泛应用的设备，远超步数或平均能量估算。本研究旨在构建一个开放数据集及配套开源代码，用于基于50 Hz三轴加速度数据在移动环境中分类患者活动水平和自然运动类型。数据来自23名健康受试者（16名男性，7名女性），年龄23至62岁，使用可穿戴设备记录，包含三轴加速度计和同步的等效导联II心电图，每人平均记录26分钟。参与者执行标准化活动流程，包括躺卧、坐姿、站立、步行和慢跑五种动作。构建了两种分类器：一种基于信号处理的二分类器区分高低活动水平，另一种基于卷积神经网络（CNN）的多分类器识别五类活动。主要结果：二分类器的F1得分为0.79，多分类器的F1得分为0.83。分析代码已按开源许可证发布，并提供训练与测试所用数据。该研究展示了行为活动分类对解读传统健康指标的重要价值，有助于未来临床决策支持、预测分析与个性化健康干预工具的发展。
原文摘要 · Abstract (English)
The accelerometer has become an almost ubiquitous device, providing enormous opportunities in healthcare monitoring beyond step counting or other average energy estimates in 15-60 second epochs. Objective: To develop an open data set with associated open-source code for processing 50 Hz tri-axial accelerometry-based to classify patient activity levels and natural types of movement. Approach: Data were collected from 23 healthy subjects (16 males and seven females) aged between 23 and 62 years using an ambulatory device, which included a triaxial accelerometer and synchronous lead II equivalent ECG for an average of 26 minutes each. Participants followed a standardized activity routine involving five distinct activities: lying, sitting, standing, walking, and jogging. Two classifiers were constructed: a signal processing technique to distinguish between high and low activity levels and a convolutional neural network (CNN)-based approach to classify each of the five activities. Main results: The binary (high/low) activity classifier exhibited an F1 score of 0.79. The multi-class CNN-based classifier provided an F1 score of 0.83. The code for this analysis has been made available under an open-source license together with the data on which the classifiers were trained and tested. Significance: The classification of behavioral activity, as demonstrated in this study, offers valuable context for interpreting traditional health metrics and may provide contextual information to support the future development of clinical decision-making tools for patient monitoring, predictive analytics, and personalized health interventions.
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