用机器学习识别老人使用助行器时的姿势，提升安全行走能力
Skeleton-Based Posture Classification to Promote Safer Walker-Assisted Gait in Older Adults
- 采用几何特征与XGBoost模型，精准识别助行器使用状态
- 在17种姿势分类中，XGBoost训练准确率达99.24%
- 适合智能助行器开发与老年人跌倒预防研究者参考
老年人跌倒是一个重大的公共卫生问题，常导致严重伤害、独立生活能力丧失和医疗成本上升。本研究评估了多种模型（包括几何方法、XGBoost、SVM及多个深度学习架构）在智能助行器中对助行器使用、站立与坐姿、以及姿势分类的效果。几何方法和XGBoost表现最佳。XGBoost在二分类任务中接近完美：助行器选择准确率为99.84%，站立/坐姿分辨准确率为99.69%。在姿势分类方面，几何方法对8种姿势识别准确率达89.9%，而XGBoost在17种姿势的训练中达到99.24%准确率。4层CNN与编码器-解码器CNN等深度学习模型在二分类任务中也表现出色，准确率均超过98%。该研究凸显了机器学习在提升智能助行器人机交互、促进老年人安全步态方面的潜力。
原文摘要 · Abstract (English)
Falls among older adults are a significant public health concern, leading to severe injuries, loss of independence, and increased healthcare costs. This study evaluates the effectiveness of various models, including a Geometric approach, XGBoost, SVM, and several deep learning architectures, in classifying walker usage, standing vs. sitting, and posture for smart walkers used. Geometric and XGBoost were the top performers. XGBoost achieved near-perfect training accuracy in binary classification tasks, with 99.84% for walker choice and 99.69% for standing vs. sitting. For posture classification, Geometric approach attained 89.9% accuracy for 8 postures, and XGBoost obtained 99.24% during training for 17 postures. Deep learning models such as the 4-layer CNN and Encoder-Decoder CNN also demonstrated strong performance in binary classification, with accuracies above 98%. This study underscores the potential of machine learning to enhance human-robot interaction in smart walkers, particularly for fall prevention.
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