Chao-Wei Yu

An Intelligent Knowledge-Based and Customizable Home Care System Framework with Ubiquitous Patient Monitoring and Alerting Techniques

This study develops and integrates an efficient knowledge-based system and a component-based framework to design an intelligent and flexible home health care system. The proposed knowledge-based system integrates an efficient rule-based reasoning model and flexible knowledge rules for determining efficiently and rapidly the necessary physiological and medication treatment procedures based on software modules, video camera sensors, communication devices, and physiological sensor information. This knowledge-based system offers high flexibility for improving and extending the system further to meet the monitoring demands of new patient and caregiver health care by updating the knowledge rules in the inference mechanism. All of the proposed functional components in this study are reusable, configurable, and extensible for system developers. Based on the experimental results, the proposed intelligent homecare system demonstrates that it can accomplish the extensible, customizable, and configurable demands of the ubiquitous healthcare systems to meet the different demands of patients and caregivers under various rehabilitation and nursing conditions.

An Efficient Component-Based Framework for Intelligent Home-Care System Design with Video and Physiological Monitoring Machineries

This study proposes a customized and reusable component-based design framework based on the UML modeling process for intelligent home healthcare systems. All the proposed functional components are reusable, replaceable, and extensible for the system developers to implement customized home healthcare systems for different demands of patients and caregivers on healthcare monitoring aspects. The prototype design of the intelligent healthcare system based on these proposed components can provide the following features: 1). the system can monitor and record the videos of rehabilitation situations and actions of the patient by multiple CCD cameras, and the monitoring videos at different times can be accordingly stored in the archive. 2). the system can record the patient¡¦s physiological data records and the corresponding treatment plan, and these records can be stored in a XML archiving database for caregivers¡¦ review. 3). during the times for the patient to take medicine or other healing activities listed on the given treatment plan, the system can automatically alarm the patient and record the patient¡¦s treatment situations. 4). The patient¡¦s caregivers and family members can ubiquitously monitor the videos and physiological records of the patient¡¦s rehabilitation situations via the handheld mobile devices via the internet or wireless communication networks. 5). The caregivers and patients can setup the alarm machinery for the patients¡¦ physiological warning states, and once the patients¡¦ physiological states suddenly deteriorate, the module will immediately alarm the caregivers by sending notification messages to their remote mobile devices or web browsers.

Real-time eye detection and event identification for human-computer interactive control for driver assistance

Eye movements can provide important information for human-computer interactive applications. Due to the progress of computer technology, the detecting accuracy and speed of pattern recognition are promoted. Additionally, according to research advances in embedded systems, the applications of digital cameras, such as internet cameras, smart phones and smart TV, and smart cars, are widely used in nowadays. Therefore, we propose a set of real-time human-eye detection and tracking systems with human-computer interaction applications. This technique can obtain eye movements and can be adopted as interactive control commands on driver assistance systems. The proposed system is implemented on an OMAP4430 for embedded applications, and experimental results show that the proposed architecture is capable of effective and real-time eye position detection and event identification for human-computer interactive applications on driver assistance systems.

Real-time eye tracking and event identification techniques for smart TV applications

Due to the progress of computer technology, the detecting accuracy and speed of pattern recognition are promoted. Additionally, according to research advances in embedded systems, the applications of digital cameras, such as internet cameras, smart phones and smart TV, are widely used in nowadays. Therefore, we propose a set of real-time human-eye recognition and tracking systems with human-computer interaction mechanism. It can be adopted to help the smart TV users or any device which have camera and computing power to improve their using experience. The proposed system is demonstrated on OMAP4430 for embedded applications, and experimental results show that the proposed architecture is capable of effective and real-time eye position tracking. The proposed system can also detect the status of eye blink. Even for the case of uneven lighting, the proposed system can successfully recognize the human eyes with high accuracy.

On-Road Driver Monitoring System Based on a Solar-Powered In-Vehicle Embedded Platform

This study presents an on-road driver monitoring system, which is implemented on a stand-alone in-vehicle embedded system and driven by effective solar cells. The driver monitoring function is performed by an efficient eye detection technique. Through the driver’s eye movements captured from the camera, the attention states of the driver can be determined and any fatigue states can be avoided. This driver monitoring technique is implemented on a low-power embedded in-vehicle platform. Besides, this study also proposed monitoring machinery that can detect the brightness around the car to effectively determine whether this in-vehicle system is driven by the solar cells or by the vehicle battery. On sunny days, the in-vehicle system can be powered by solar cell in places without the vehicle battery. While in the evenings or on rainy days, the ambient solar brightness is insufficient, and the system is powered by the vehicle battery. The proposed system was tested under the conditions that the solar irradiance is 10 to 113 W/m2 and solar energy and brightness at 10 to 170. From the testing results, when the outside solar radiation is high, the brightness of the inside of the car is increased, and the eye detection accuracy can also increase as well. Therefore, this solar powered driver monitoring system can be efficiently applied to electric cars to save energy consumption and promote the driving safety.

Vision-based Hand Recognition Based on ToF Depth Camera

Abstract The current gesture recognition methods mostly adopt the classification-based approaches such as Neural Network (NN), Support Vector Machine (SVM), Hidden Markov Model (HMM) etc. As for the input image features, most research studies combined the color and depth images (ex. RGB-D) to obtain more accurate information of hand area, and such techniques may cost high computational resources and energy consumptions. To provide a low-cost gesture recognition method for wearable devices, this thesis used merely the Time-of-Flight depth camera to achieve a lightweight gesture recognition method. In most traditional gesture recognition methods, users have to wear gloves or bracelets to let depth cameras being able to accurately capture hands areas, and so that the hand contours, palm’s distances, and angle feature can be obtained. Moreover, the Earth Mover’s Distance (EMD) algorithm, which is adopted in most gesture recognition approaches, costs high computational times. In this study, to avoid wearing gloves or bracelets, we propose a new algorithm that can compute the wrist cutting edges and capture the palm areas. In addition, this thesis proposes an efficient finger detection algorithm to judge the number of fingers, and significantly reduce the computing times. In the experimental results, our proposed method achieves a recognition rate of 90% and the performance has 5 frames per second on NVIDIA TX1 embedded platforms.

Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems

Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss.