用PPG信号识别老人体位变化，准确率达85.2%
Machine Learning Assisted Postural Movement Recognition using Photoplethysmography(PPG)
- 仅用PPG信号提取脉搏形态与稳态特征，结合机器学习分类
- 人工神经网络表现最优，测试准确率85.2%，F1值78%
- 适合居家跌倒预警场景，无需额外传感器
随着老年人口比例上升和养老机构入住人数增加，迫切需要发展跌倒检测与预防技术。本文首次提出仅通过光电容积脉搏波（PPG）数据，利用机器学习识别体位运动。为此，设计了可读取PPG信号的设备，对信号进行脉搏分割，提取脉搏形态与稳态特征，并评估多种机器学习算法。通过11名受试者完成静止、坐站、躺站等体位变化实验，分析了运动后PPG信号中稳态差异。多种机器学习方法被用于分类，其中人工神经网络（ANN）表现最佳，测试准确率为85.2%，F1得分为78%。
原文摘要 · Abstract (English)
With the growing percentage of elderly people and care home admissions, there is an urgent need for the development of fall detection and fall prevention technologies. This work presents, for the first time, the use of machine learning techniques to recognize postural movements exclusively from Photoplethysmography (PPG) data. To achieve this goal, a device was developed for reading the PPG signal, segmenting the PPG signals into individual pulses, extracting pulse morphology and homeostatic characteristic features, and evaluating different ML algorithms. Investigations into different postural movements (stationary, sitting to standing, and lying to standing) were performed by 11 participants. The results of these investigations provided insight into the differences in homeostasis after the movements in the PPG signal. Various machine learning approaches were used for classification, and the Artificial Neural Network (ANN) was found to be the best classifier, with a testing accuracy of 85.2\% and an F1 score of 78\% from experimental results.
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