Abstract
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.
Highlights
With the increase of life expectancy and the decrease of the fertility rate, the proportion of elders older than 64 years old in the total population explosively increases
The overall performance of the proposed multiphase identification algorithm achieves 76.54% sensitivity, 80.89% precision, 66.45% Jaccard coefficient, and 87.05% accuracy
The proposed algorithm using the k-nearest neighbor (kNN) technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy
Summary
With the increase of life expectancy and the decrease of the fertility rate, the proportion of elders older than 64 years old in the total population explosively increases. Various sensors (e.g., inertial sensors [6], pressure or seismic sensors [7,8] and cameras [9,10]) and machine learning techniques (e.g., support vector machine (SVM), and k-nearest neighbor (kNN)) have been successfully applied to fall-related applications [5,11,12,13,14]. These works have shown that fall events can be automatically detected by the systems. Few works focus on the development of automatic fall recording systems to obtain fall information in fine-grained levels for clinical evaluation and measurement
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