We propose an activity recognition system especially for the elderly using a
wearable sensor module including a triaxial accelerometer. We have mainly
tackled easy battery loss problem minimizing the efficiency decrease of the
activity recognition.
The proposed system consists of main modules; sensor module, gateway, and
PDA phone. The sensor module is worn at the left side of waistband, and the
embedded algorithm installed in a microcontroller of the sensor module
manipulates the sensing data in order to reduce the transferring overhead
which causes enormous battery loss.
After that, the sensor module transfers them to the subject's PDA phone by
means of the Zigbee. Then, the phone accumulates the data for a while and
starts to classify them as an ADL (activities of daily living) like running,
walking, standing, sitting, lying, falling, etc.
As a classifier of ADL, the multi-layer perceptron algorithm is used, and we
have achieved 95.5% of accuracy in inferring an activity out of 9 ADLs. It
shows we can save the battery loss while maintaining the recognition
accuracy similarly compared to related works.
