Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
Research Center for Information Technology Innovation, Academia Sinica, Taipei 11529, Taiwan.
Sensors (Basel). 2021 May 10;21(9):3302. doi: 10.3390/s21093302.
Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.
与跌倒相关的信息可以帮助临床专业人员进行诊断和制定跌倒预防策略。这些信息包括不同跌倒阶段的各种特征，例如跌倒时间和落地反应。为了提供不同阶段的信息，本试点研究提出了一种用于感知阶段的跌倒记录系统的自动多阶段识别算法。招募了 7 名年轻人进行跌倒实验。一个惯性传感器佩戴在腰部以收集身体运动数据，共收集了 525 次试验。所提出的多阶段识别算法结合了机器学习技术和片段修改算法，以识别跌倒过程中的预跌倒、自由落体、撞击、静止和恢复阶段。五种机器学习技术，包括支持向量机、k-最近邻 (kNN)、朴素贝叶斯、决策树和自适应增强，用于识别五个阶段。片段修改算法使用规则来检测与邻居结果不同的片段。使用 kNN 技术的多阶段识别算法在 82.17%的灵敏度、85.74%的精度、73.51%的杰卡德系数和 90.28%的准确率方面表现最佳。结果表明，该算法有可能为临床测量和评估提供自动的细粒度跌倒信息。
