Estimating Energy Expenditure of Walking and Running under Free-living Conditions by Wireless Patch Sensor
Meina Li, Youn Tae Kim · Medicine & Science in Sports & Exercise · 2011
In the modern society, the most physical activities are walking and running. The lack of exercise has become an important factor in our health. We have accurately estimated the energy expenditure of walking and running base on the treadmill in the previous study. However the results for free-living conditions have many different factors with lab environment such as speed and stride length. PURPOSE: To estimate the energy expenditure of walking and running effectively under free-living conditions in young healthy adults by using the combined heart rate and movement sensor method. METHODS: 30 participants (15 female and 15 male) recruited from college were patched the wireless sensor module (AirBeat System) on the chest during walking and running. For measuring free-living environment energy expenditure, the participants performed in comfortable stride length and speed on the school playground. Each participant underwent four steps in different day but same time period. The four steps are comfortable walking and jogging, quickest walk and slowest running with the emotion such as nervous that for ensure the experiments should be as nature as possible. Heart rate and movement intensity were real-time monitored and recorded by the patched type senor. The estimated values were compared with portable indirect calorimeter (Cosmed K4b2). RESULTS: The AirBeat systems can monitor energy expenditure for more than 8 people simultaneously within distance 400m on a real time basis. The quick walking and slow running showed nearly same speed during the test. The energy expenditure assessment can be estimated from the linear regression equation for comfortable walking (r2 = 0.754, P<0.05), jogging (r2 = 0.803, P<0.05), quickest walk (r2 = 0.673, P<0.05) and slowest running (r2 = 0.618, P<0.05). From the result, we found that the developed AirBeat system could apply in monitoring of individual energy expenditure during the exercise performance test for free-living environment. CONCLUSIONS: The heart-rate and movement combined sensor module demonstrates its ability to estimate EE during walking and running under free-living environment. The preliminary results of correlation equations for the Airbeat systems provided similar estimates of activity and energy compares with the indirect calorimetry system.