Abstract
Accelerometer-based activity monitors are popular for monitoring physical activity. In this study, we investigated optimal sensor placement for increasing the quality of studies that utilize accelerometer data to assess physical activity. We performed a two-staged study, focused on sensor location and type of mounting. Ten subjects walked at various walking speeds on a treadmill, performed a deskwork protocol, and walked on level ground, while simultaneously wearing five ProMove2 sensors with a snug fit on an elastic waist belt. We found that sensor location, type of activity, and their interaction-effect affected sensor output. The most lateral positions on the waist belt were the least sensitive for interference. The effect of mounting was explored, by making two subjects repeat the experimental protocol with sensors more loosely fitted to the elastic belt. The loose fit resulted in lower sensor output, except for the deskwork protocol, where output was higher. In order to increase the reliability and to reduce the variability of sensor output, researchers should place activity sensors on the most lateral position of a participant's waist belt. If the sensor hampers free movement, it may be positioned slightly more forward on the belt. Finally, sensors should be fitted tightly to the body.
Highlights
Accelerometer-based activity monitors are currently the most widely used sensors for monitoring physical activity in clinical and free-living settings [1,2,3]
One could assume that averaging sensor data over large populations or over time reduces the effects of usage and other non-controllable factors during free living
While we do not claim we have found the cause for the findings in the aforementioned studies, we do believe that mounting needs to be considered as a factor that affects sensor output
Summary
Accelerometer-based activity monitors are currently the most widely used sensors for monitoring physical activity in clinical and free-living settings [1,2,3]. They can be used for monitoring physical activity to acquire more fundamental knowledge of patterns of physical activity or to generate input for health interventions. For the latter application, activity sensor data is used to determine performance and, subsequently, to provide real-time personalized feedback The influence of the placement of the activity sensor itself on sensor output has not been studied in-depth before [8,9]
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