Estimation of a physical activity energy expenditure with a patch‐type sensor module using artificial neural network

Abstract

Chronic diseases such as coronary artery diseases and diabetes are caused by lack of physical activities and are leading causes of high death and morbidity rates. In particular, the imbalance of consumption energy and intake energy has increased adult diseases such as obesity with high mortality. Until recently, direct calorimetry by production calorie and indirect calorimetry by energy expenditure have been regarded as the best methods for estimating physical activity and energy expenditure. These calorimetry methods are associated with limited practicality such as data acquisition in a limited time, high cost, and wearing an inconvenient mask for oxygen uptake measurement. In this study, we propose the most accurate method using a wireless patch-type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, we achieved RMSE of 0.1893 and R 2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises. These results indicate that the proposed system is useful and reliable for monitoring user's energy expenditure when using attached patch-type sensors workouts.

The potential of artificial intelligence in enhancing adult weight loss: a scoping review

“…Details on the use of AI for the self-regulation of weight lossrelated behaviours are shown in Table 2. Of the studies on enhancing self-monitoring, twenty-nine (43•9 %) were on eating behaviours (58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76) , seven (10•6 %) were on energy intake (34,(77)(78)(79)(80)(81)(82) , thirty-three (50 %) were on physical activity (26,(51)(52)(53)(54)(55)60,74, and nine (13•6 %) were on energy expenditure (83,85,92,(94)(95)(96)(97)100,101) . Of the studies on optimising goal setting, five were on optimising eating behaviour goals (e.g., eating at a certain time of the day and energy intake) (48,49,53) and six were on optimising physical activity...…”

“…The studies reported recognition accuracies ranging from 69•2 to 99•1 %. Machine recognition techniques used in the included studies were gesture (n 32) (51,56,58,(60)(61)(62)64,65,70,74,81,(83)(84)(85)(86)(87)(88)(89)(90)(91)(92)(93)(95)(96)(97)(98)(99)(100)(101)(102)(103)(104) , image (n 14) (34,63,(66)(67)(68)74,76,(78)(79)(80)88,93,94,101) , sound (n 7) (57)(58)(59)69,(71)(72)…”

“…Energy intake was mostly estimated using image and speech recognition (34,(75)(76)(77)(78)(79)(80) while the other AI recognition techniques were used to detect eating behaviours and food types. Gesture/image recognition was mainly used to detect and estimate physical activity and energy expenditure (83,85,92,(94)(95)(96)(97)100,101) while the other AI techniques were used only for physical activity recognition.…”

“…Accelerometer (26,49,50,29,48,51,52,55,61,62,65,(81)(82)(83)(84)(85)(86)(87)(88)(89)(90)(91)(92)95,96,98,99,101,103,104) , magnetometers (51,81,83,90,102) and gyroscope (61,65,81,82,(89)(90)(91)102) (built-into smartphones kept in front pocket or worn on the ear, wrist, hip or leg)…”

Smart media and application

“…In Estimation of a physical activity energy expenditure with a patch‐type sensor module using artificial neural network by Kang et al, 11 it proposed the most accurate method using a wireless patch‐type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, it achieved RMSE of 0.1893, R2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises.…”