Research On Key Techniques Of Wearable Healthcare And Human-computer Interaction

Due to the growing population aging, the development of ubiquitous healthcare and natural human-computer interaction (HCI) is of great demand. Wearable healthcare systems are able to offer ubiquitous healthcare services in daily life without affecting users’daily activities. Multimodal HCI based on wearable devices is able to offer natural interaction experience and an easy way to operate smart devices for the elderly. This dissertation focuses on wearable device-based healthcare and interaction, and on these bases, develops a healthcare system with such functions, especially designed for the elderly.This dissertation introduces a prototype of pulse oximeter designed for mobile healthcare. It is designed to be embedded into the back cover of a personal digital assistant (PDA) to offer the convenient measurement of both heart rate (HR) and arterial oxygen saturation (SpO2) for home or mobile healthcare applications. As opposed to conventional transmission pulse oximeters with finger cots, a reflection pulse oximeter is implemented, which can work on various parts of the body, thus facilitating its applications in mobile and wearable use case. Considering that reflection sensor is easily interfered by ambient light due to the lack of shading effect on flat surface of the back cover of PDAs, a novel lightening modulation, along with a novel circuit module named chopper network, is designed for signal separation and ambient light removal. Aiming at the obtained weak signal amplification and denoising, a novel filtering amplifier is designed to overcome the influence of high-frequency interferences and to improve signal-noise-ratio. Furthermore, a method based on trough detection for improved HR and SpO2estimation is proposed with appropriate simplification for its implementation on wearable devices or mobile devices like PDA. In addition, with the purpose to process unstable PPG (photoplethysmogram) signals acquired by the embedded sensor, adaptive thresholds and parameters are applied to the HR and SpO2estimation algorithm. A PPG validation algorithm is also designed to reject invalid PPG or non-PPG signals towards the embedded oximeter to make measurement results more reliable. Clinical experiments are carried out to calibrate and test our oximeter. Our prototype oximeter can achieve comparable performance to a clinical oximeter according to the statistical analysis using paired T-test, revealing insignificant difference between the two oximeters at (0.3±0.9)%in SpO2measurement and (0.4±2.4) beats per minute in HR measurement (p<0.05). The experimental results demonstrate the feasibility of this proposed prototype.In this dissertation, a wearable gesture-based interaction prototype for mobile phone is developed. More specifically, a homemade wearable gesture capturing device is designed to acquire acceleration (ACC) and surface electromyography (SEMG) signals; an algorithm framework is proposed to process the signals for gesture recognition, and an application program is developed to realize gesture-based real-time interaction. Users are able to manipulate the mobile phone using19predefined gestures or even personalized ones. In the novel segmentation scheme based on the prior one designed for only SEMG signals, a gesture has two asynchronous signal segments, one from SEMG signals and the other from ACC signals. SEMG marked ACC signal segmentation algorithm is proposed to overcome the segmentation challenge for ACC signals. ACC signals shows improved internal consistency when processed by this novel segmentation scheme, facilitating the free and natural performance of gestures for users without cumbersome restraints like grasping hand simultaneously during waving arm. Two new features for ACC signals are proposed and achieve considerable improvement of recognition accuracy. Classifiers based on improved dynamic time wrapping (DTW) and state transition model (STM) respectively are designed to recognize ACC signals. Both can achieve the comparable accuracies to those of hidden Markov model (HMM), but cost much lower computational power. Considering the SEMG segment and ACC segment are usually asynchronous, a score-based fusion scheme is proposed to make final recognition decisions by combining the both using predefined weights. The proposed system can achieve an average accuracy of95.0%in user-dependent testing and89.6%in user-independent testing, offering practical solution to real-time gestural interaction on mobile or wearable devices. Such promising performance during the interaction testing, along with positive user experience from a questionnaire survey, demonstrates the feasibility of our prototype.Taking advantage of the wearable devices in mobile healthcare and intelligent HCI, a healthcare and interaction system is developed for the elderly. It can offer ubiquitous and real-time monitoring of electrocardiograph, HR, SpO2, skin moisture, and body fat in daily life for healthcare. Fall detection service activates the alarm after it detects any emergency. The position information is calculated by our novel location service based on both global position system (GPS) and our pedestrian dead reckoning (PDR) algorithms. Gestural interaction designed for mobile phone and smart TV offers simple and natural control using wearable devices, which facilitates the interaction between the devices and the elderly population.This work was supported in part by National High Technology of Research and Development Program of China (863Program) under Grant No.2009AA01Z322, Fundamental Research Funds for the Central Universities of China under Grand No. WK2100230002, and National Nature Science Foundation of China (NSFC) under Grant No.60703069.