Jeong Bae Ko1, Kwang Bok Kim1, Young Sub Shin1, Hun Han1, Sang Kuy Han2, Duk Young Jung3, Jae Soo Hong1. 1. Digital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Chuncheongnam-do, South Korea. 2. Robotics R&D Department, Korea Institute of Industrial Technology, Ansan, Gyeonggi-do, South Korea. 3. Seongnam Senior Experience Complex, Eulji University, Seongnam, Gyeonggi-do, South Korea.
Abstract
PURPOSE: Sarcopenia is a symptom in which muscle mass decreases due to decreasing in the number of muscle fibers and muscle cross-sectional area as aging. This study aimed to develop a machine learning classification model for predicting sarcopenia through a inertial measurement unit (IMU)-based physical performance measurement data of female elderly. PATIENTS AND METHODS: Seventy-eight female subjects from an elderly population (aged: 78.8±5.7 years) volunteered to participate in this study. To evaluate the physical performance of the elderly, the experiment conducted timed-up-and-go test (TUG) and 6-minute walk test (6mWT) with worn a single IMU. Based on literature review, 132 features were extracted from collected data. Feature selection was performed through the Kruskal-Wallis test, and features datasets were constructed according to feature selection. Three major machine learning-based classification algorithms classified the sarcopenia group in each dataset, and the performance of classification models was compared. RESULTS: As a result of comparing the classification model performance for sarcopenia prediction, the k-nearest neighborhood algorithm (kNN) classification model using 40 major features of TUG and 6mWT showed the best performance at 88%. CONCLUSION: This study can be used as a basic research for the development of self-monitoring technology for sarcopenia.
PURPOSE: Sarcopenia is a symptom in which muscle mass decreases due to decreasing in the number of muscle fibers and muscle cross-sectional area as aging. This study aimed to develop a machine learning classification model for predicting sarcopenia through a inertial measurement unit (IMU)-based physical performance measurement data of female elderly. PATIENTS AND METHODS: Seventy-eight female subjects from an elderly population (aged: 78.8±5.7 years) volunteered to participate in this study. To evaluate the physical performance of the elderly, the experiment conducted timed-up-and-go test (TUG) and 6-minute walk test (6mWT) with worn a single IMU. Based on literature review, 132 features were extracted from collected data. Feature selection was performed through the Kruskal-Wallis test, and features datasets were constructed according to feature selection. Three major machine learning-based classification algorithms classified the sarcopenia group in each dataset, and the performance of classification models was compared. RESULTS: As a result of comparing the classification model performance for sarcopenia prediction, the k-nearest neighborhood algorithm (kNN) classification model using 40 major features of TUG and 6mWT showed the best performance at 88%. CONCLUSION: This study can be used as a basic research for the development of self-monitoring technology for sarcopenia.
Authors: Ion Martinikorena; Alicia Martínez-Ramírez; Marisol Gómez; Pablo Lecumberri; Alvaro Casas-Herrero; Eduardo L Cadore; Nora Millor; Fabricio Zambom-Ferraresi; Fernando Idoate; Mikel Izquierdo Journal: J Am Med Dir Assoc Date: 2015-11-11 Impact factor: 4.669
Authors: Alicia Martínez-Ramírez; Ion Martinikorena; Marisol Gómez; Pablo Lecumberri; Nora Millor; Leocadio Rodríguez-Mañas; Francisco José García García; Mikel Izquierdo Journal: J Neuroeng Rehabil Date: 2015-05-24 Impact factor: 4.262