Shang Hwa Hsu

Development of Wireless Brain Computer Interface With Embedded Multitask Scheduling and its Application on Real-Time Driver's Drowsiness Detection and Warning

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

A fuzzy CBR technique for generating product ideas

This paper presents a fuzzy CBR (case-based reasoning) technique for generating new product ideas from a product database for enhancing the functions of a given product (called the baseline product). In the database, a product is modeled by a 100-attribute vector, 87 of which are used to model the use-scenario and 13 are used to describe the manufacturing/recycling features. Based on the use-scenario attributes and their relative weights - determined by a fuzzy AHP technique, a fuzzy CBR retrieving mechanism is developed to retrieve product-ideas that tend to enhance the functions of the baseline product. Based on the manufacturing/recycling features, a fuzzy CBR mechanism is developed to screen the retrieved product ideas in order to obtain a higher ratio of valuable product ideas. Experiments indicate that the retrieving-and-filtering mechanism outperforms the prior retrieving-only mechanism in terms of generating a higher ratio of valuable product ideas.

Factors Influencing Player Preferences for Heroic Roles in Role-Playing Games

Two studies were conducted to investigate whether player personality or social cognition influence preferences for heroic roles in role-playing games (RPG). In Study 1, 149 teenager subjects were categorized into five groups according to the Guilford Personality Inventory. Heroes were clustered into three types based on their attributes. The analysis of variance (ANOVA) results indicated that each personality group did not display distinctive preference for any particular heroic type. However, of the three heroic types teenagers most strongly preferred, Justice Warrior was followed, in order of preference, by Visionary Leader and Saint. In Study 2, the influence of three player social cognition factors (similarity, proximity, and familiarity) on player preference for heroic roles was studied. Multiple regression analysis results indicated that similarity and familiarity predicted player preferences for heroic roles.

Exploring design features for enhancing players' challenge in strategy games.

This paper examines how to make a player feel more challenged in a strategic computer game. It is hypothesized that information availability and resource advantage affect play difficulty, which in turn affects the challenge experienced. The difficulty of play can be defined in terms of the mental workload that players experience and the physical effort that players exert. Forty-five male college and graduate students participated in a 3 x 3 (information availability x resource advantage) between-subjects factorial design experiment. This experiment measured player mental workload, physical effort, and challenge. The results indicate that information availability affects player mental workload, and resource advantage affects levels of player physical effort, respectively. Moreover, the relationship between mental workload and challenge was found to be an inverted U-shaped curve; in other words, too much or too little mental workload may decrease player challenge. The relationship between physical effort and challenge exhibited similar characteristics.

An integrated approach to achieving optimal design of computer games

Abstract In a time-to-market environment, designers may not be able to incorporate all the design features in a computer game. For each feature, there are several levels of implementation, which is corresponded to different levels of benefit as well as cost. Therefore, a trade-off decision for determining appropriate levels of implementation is very important, yet has been rarely studied in literature. This paper presents an approach to solve the trade-off decision problem. This approach applies the neural network technique and develops a genetic algorithm to optimize the design of computer games. By this approach, a near-optimal design alternative can be identified in a timely fashion.

Health pal: a PDA phone that will take care of your health

The Kannon project at the National Chiao Tung University (NCTU) in Taiwan has designed a smart PDA phone, christened Health Pal, to help elders to manage their healthcare needs such as planning their own physician visits, medication schedules, exercise routines as well as monitoring their blood pressures, pulse rates and oxygen levels. The paper reports the design concepts of the device as well as the activity oriented approach the team undertook during the design process. A comparison of this device against several similar prototypes was also included to highlight its unique features.

Custos Remote on-demand healthcare aided with wireless sensors and mobile phones

Telemonitoring and remote healthcare are two ICT techniques promise to deliver cost effective long-term healthcare services to the aging population worldwide. Anticipating an emerging market opportunity, NCTU Kannon project team is creating Custos, an open composeable platform for developing remote on-demand healthcare applications using smart phones and wireless sensor networks. Custos is integrated readily with Internet web services and UPnP digital-home platform so that it can be used to detect abnormal health conditions through multi-sensor data fusion, adapt environment conditions to user needs, dispatch medical alerts and instigate remedial actions via SMS/RSS communication. Moreover, Custos is easy to program because it uses Sun SPOT and Squawk Java J2ME as its sensor/actuator hub. In this position paper, we present the design principles, system architecture and operation concepts of Custos as well as a pilot plan for clinical trials in the Geriatric Psychiatry Ward of Wei-Gong Memorial Hospital in Miaoli, Taiwan.

Kannon: Ubiquitous Sensor/Actuator Technologies for Elderly Living and Care: A Multidisciplinary Effort in National Chiao Tung University, Taiwan

A team of researchers including computer scientists, electrical and control engineers, architects, industrial designers, human factor engineers, and cognitive scientists in the National Chiao Tung University (NCTU), Taiwan, along with their overseas collaborators launched the project Kannon, a multi-disciplinary effort to develop Adaptive Assistive Technologies that can be deployed incrementally into existing private/public spaces and collaborate opportunistically to offer monitoring, assisting, communicating and rejuvenating services to healthy elders. The team combined the state-of-art information, communication and robotic know-how with the activity oriented method for product design and the modular functional approach in modern architecture in order to devise a holistic support for successful aging. This paper presents the philosophy, approach and first fruits of this project.

A predictive model for acute allograft rejection of liver transplantation

Propose a predictive model for acute rejection of liver transplantation.Propose an algorithm to transform continuous variables into categorical variables.The experiments use the real data provided by the hospital.The proposed method achieves good performance and provides interpretable results. Orthotopic liver transplantation (OLT) has become an increasingly used treatment for end-stage liver disease. However, acute allograft rejection is still a problem in postoperative care of liver transplantation with immunosuppressive therapy and it can lead to allograft damage and harm the survival of liver transplantation patient. This work proposes to use data-driven approach to build a predictive model for acute rejection. We consider not only prediction accuracy, but also interpretability of the prediction outcome in building the predictive model, so that the medical staffs can identify how the prediction is induced from data. The experiments use the real data provided by liver transplantation intensive care unit (ICU) of Chang Gung Memorial Hospital, Taiwan. In this work, the data is from a medical center, in which the patient data ranges from 2004 to 2013, and the number of data records is approximately 2 million. To the best of our knowledge, this is the first work using a large-scale database to focus on liver transplantation and generate interpretable rules that could be used by medical staffs. We compare with several methods, including SVM, ANN and random forest, and the experimental results indicate that the proposed method is comparative, and provides interpretable results. Central to the proposed method is to consider interpretability, and the goal is to provide interpretable results for the medical staffs to make decisions. The proposed transformation algorithms belong to data-driven approaches, so they could be applied to other intelligent or expert systems. Moreover, the outcomes are presented in rule format, which could be used by medical staffs and other expert systems.