Research On Wearable Health-monitoring System Based On Human Activity Recognition

Wearable health-monitoring system is a typical application of wearablecomputing in healthcare field. It will change traditional "passive" usage mode oftelemedicine system and family healthcare in our nation, by providing continualmonitoring of physiological signals of end user with slight mental and physicalburden. International and local researchers have done much work in this field, butcurrent researches usually determine health status by only physiological signals,which ingore the relation between human physiological characteristics and activities.Lack of activity information at that time may causes misdiagnosis. Therefore, witheffective combination, it will show more practical significance to study wearablehealth-monitoring system based on human activity recognition.Meeting the requirement of user to move freely, this dissertation designed awaistcoat for wearable health-monitoring to get physiological and movement signals,and then determined health status based on recognition of activities in real time. Thismethod improves precise of human health monitoring in daily life. In thisdissertation, four aspects will be researched: system architecture, human activityrecognition, falling detection, and power management strategy for system. The majorcontributions of this dissertation are stated as follows:(1) Provide a general architecture of wearable health-monitoring system basedon human activity recognition. To address the issues with different type of devicesand communication methods, we construct system architecture based on agent model,and define its communication protocol, interaction processes. This architecture isindependent of hardware units, which makes the system more scalable and therelated softwares easily be deployed.(2) Propose an algorithm to classify activity states of human with singleaccelerometer. According to the constancy feature of daily movements in short time,activity states are divided into steady and unsteady ones. We transform raw datameasured from the three axis into changes of signal vector magnitude to avoiddependence on wearing coordinate, and apply Kalman filter to classify the above twostates in real time.Meanwhile, we use thresholds which are automatically adapted fordifferent users to recognize activities of running and walking when they are onsteady state. Experiment results showed that the algorithm got better performance inaccuracy of activity recognition. It performed higher accuracy for running and walking activities than the decision tree algorithm.(3) Propose an algorithm to recognize dangerous fall of human body with singleaccelerometer, where “dangerous fall” means subject could not return to his/hernormal behaviors after impacting on the ground. Features of overweight, continuousweightless time, tilting angle and continuous still time are abstracted, which are allindependent of the sensor orientation with respect to the body, and simplifycomplexity of computing. To improve accuracy of measurement, the referencedgravity output value will be adapted with activity states. Experimental resultsshowed higher accuracy than one-class SVM algorithm and the algorithms based onmulti-axial directions.(4) State the event-driven strategy for power management of system. In order toreduce energy consumption on mobile device when continuous sensing, an eventmodel is built which regards continually being still on healthy situation as sleepevent, activity state transitions and abnormal physical signals as waken event.Duration of the system working cycle can be adapted automatically according to thestate of subjects. Experimental results demonstrated that, keeping performance inreal time and accuracy, the system could save25%energy than that without thispower management strategy.This dissertation combines physiological monitoring with human activityrecognition to construct "wearable health-monitoring system based on recognition ofhuman activity state (WHMSHAR)", which applies technology of wearablecomputing, signal processing, wireless communication with accelerometer, physicalsensors, Bluetooth, and runs on smart phone and server. Worn by volunteers in dailylife, the tested system can successfully send out alert in case of dangerous fall eventsand abnormal physiological signals in different activity states, which is evidence ofthe reliability of wearable health-monitoring architecture based on human activityrecognition.