Sensor positioning for activity recognition using multiple accelerometer-based sensors

Physical activity has a positive impact on people's well-being and it can decrease the occurrence of chronic disease. To date, there has been a substantial amount of research studies, which focus on activity recognition using accelerometer and gyroscope-based sensors. However, the sensor position and the sensor combination, which have the best recognition performance with minimum sensor number, have not been investigated enough. This study proposes a method to adopt multiple accelerometer-based sensors on different body locations to investigate this problem. The dataset was collected in a study conducted by the eCAALYX project. Eight subjects were recruited to perform eight normal scripted activities in different life scenarios, and each repeated three times. Thus a total of 192 activities were recorded. The collected dataset was used to find the most suitable sensor-subset for recognizing Activities of Daily Living (ADLs).