A Prediction Algorithm Using a Combined Heart Rate and Movement Index to Estimate the Energy Expenditure of Anaerobic Exercises
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In recent times, as healthcare is perceived as important, exact information on the proper amount of exercise is necessary. Therefore, if an algorithm can predict real-time energy expenditure and is used in portable equipment, it can help athletes, the general public, and patients create individualized exercise plans. Physical activities must be evaluated to accurately predict energy expenditure. This study propose a new energy expenditure prediction algorithm (EEPA) of combining the heart rate and movement index and applying them simultaneously to several anaerobic exercises to address the disadvantages of preceding energy expenditure prediction studies. A total of 53 subjects (43 males and 10 females) were recruited for this study. The participants used a wireless patch-type sensor (AIRBEAT System) and a wireless gas analyzer (K4b2: Cosmed, Srl, Italy). AIRBEAT System consists of a sensor board, rubber board, and communication module. The sensor is patched onto the participant's chest to obtain physical activity data, including heart rate, movement index, humidity, and temperature. The system was only applied to measurement of heart rate and movement index, and application of energy expenditure prediction algorithm has been limited so far. The relation test for energy expenditure prediction algorithm proposed in this study yields an error rate within ±5% compared with the gas analyzer (K4b2: Cosmed, Srl, Italy) and proves to be more accurate algorithm to estimate physical activity EE. The algorithm developed for energy expenditure prediction is applied not only to anaerobic exercise but also to evey exercise. It is expected that the algorithm developed for estimating energy expenditure will present new application areas for portable heart-rate measurement equipment, such as the AIRBEAT system, and wireless healthcare monitoring devices.
Abstract
In recent times, as healthcare is perceived as important, exact information on the proper amount of exercise is necessary. Therefore, if an algorithm can predict real-time energy expenditure and is used in portable equipment, it can help athletes, the general public, and patients create individualized exercise plans. Physical activities must be evaluated to accurately predict energy expenditure. This study propose a new energy expenditure prediction algorithm (EEPA) of combining the heart rate and movement index and applying them simultaneously to several anaerobic exercises to address the disadvantages of preceding energy expenditure prediction studies. A total of 53 subjects (43 males and 10 females) were recruited for this study. The participants used a wireless patch-type sensor (AIRBEAT System) and a wireless gas analyzer (K4b2: Cosmed, Srl, Italy). AIRBEAT System consists of a sensor board, rubber board, and communication module. The sensor is patched onto the participant's chest to obtain physical activity data, including heart rate, movement index, humidity, and temperature. The system was only applied to measurement of heart rate and movement index, and application of energy expenditure prediction algorithm has been limited so far. The relation test for energy expenditure prediction algorithm proposed in this study yields an error rate within ±5% compared with the gas analyzer (K4b2: Cosmed, Srl, Italy) and proves to be more accurate algorithm to estimate physical activity EE. The algorithm developed for energy expenditure prediction is applied not only to anaerobic exercise but also to evey exercise. It is expected that the algorithm developed for estimating energy expenditure will present new application areas for portable heart-rate measurement equipment, such as the AIRBEAT system, and wireless healthcare monitoring devices.
참고문헌 (29)
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