Abstract
Lower physical activity and impaired sleep quality play an important role in the etiology of metabolic syndrome, obesity and type 2 diabetes. Recently, wearable sensors have been developed, which enables us to monitor body movements and RR-intervals (RRI). To extract physiologically meaningful data from the big-data, we have developed following algorithms to evaluate PAL and Sleep States. 1) Spatial Magnitude of accelerometer signals and RRI to evaluate PAL and energy expenditure by comparing oxygen consumption during treadmill exercise.2) Attachment of two sensors to evaluate the duration of postural state during job.3) Least square cosine spectrum analysis of RRI to monitor sleep state.4) Sleep apnea detection and feature extraction algorithm by using detrended fluctuation analysis (DFA) from the MIT-BIH polysomnography database.Monitoring PAL, sleep stages, and body postures in the free-activity condition, and parameter extraction may afford useful quantitative information in the fields of nutrition, sleep, medicine, and health promotion.
