Activity Classification Using 3-Axis Accelerometer Wearing on Wrist for the Elderly

Abstract

The monitoring of single elderly is more important due to rapid transition to aging society. There are many bio-signals to monitor the emergent state of elderly. These vital signals including ECG, PPG, blood pressure signal spend heavy processing resource and costs. We have been developed the monitoring device for the elderly emergency monitoring. In this paper we propose new criteria to classify daily life activities. We categorized activities with the motility of real action. The upper most criteria are normal and abnormal activity. The lower criteria are ‘small or large movement’, ‘periodic or random movement’, ‘no movement or shock’. Then we derive some parameters to get thresholds to classify these activities according to our new criteria. The main parameters are entropy, energy and autocorrelation. Some experiments were carried out to determine classifying thresholds. Finally we got results of classified activities such as ‘no movements’, ‘small movements’, ‘large movements’, ‘periodic movements’ and ‘falls’. We got nearly 100% of classifying result for falls and no movements. In this case of ‘quasi-emergency state’ our developing device will investigate further status of elderly by measuring of heart rate and oxygen saturate using pulse oxymeter. Finally the device decides in emergence, it sends a short message to server and then connected to the u-Healthcare center or emergency center and one’s family.