Introduction Assessing time spent in different activity types may be important for early detection of mobility limitations in older adults. To date, accelerometer-based activity type prediction using machine learning algorithms have not been validated for this segment of the population. Therefore, the aim of this study was to use Random ...
Accelerometers offer valid and objective measures to assess free-living physical activity (PA) (Kodama et al, 2002; Lyden et al, 2012). While these devices provide rich information with frequent readings over a period of time, the large amount of data is difficult to analyze and special techniques to extract features are needed. Summarizing ...
Introduction Pattern recognition classification algorithms have successfully identified energy cost and activity type from hip-worn accelerometers (ACC) [1,2]. These algorithms evaluate attributes of the acceleration signal during high frequency sampling. ACC output similarities and differences across activity types are well characterized [3]. An often overlooked area of potential difference lies in movement ...