Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 9; p. 3302
• Main Authors: Hsieh, Chia-Yeh, Huang, Hsiang-Yun, Liu, Kai-Chun, Liu, Chien-Pin, Chan, Chia-Tai, Hsu, Steen Jun-Ping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10.05.2021; MDPI
• Subjects: Algorithms; Automatic pilots; Cameras; Caregivers; Consciousness; Data collection; fall recording system; Falls; Identification; Machine learning; Microelectromechanical systems; multiphase identification; Phases; Sensors; Support vector machines; wearable inertial sensor; Young adults; United States > US
• ISSN: 1424-8220; 1424-8220
• 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.