Norzali Haji Mohd

A Non-invasive Facial Visual-Infrared Stereo Vision Based Measurement as an Alternative for Physiological Measurement

Our main aim is to propose a vision-based measurement as an alternative to physiological measurement for recognizing mental stress. The development of this emotion recognition system involved three stages: experimental setup for vision and physiological sensing, facial feature extraction in visual-thermal domain, mental stress stimulus experiment and data analysis and classification based on Support Vector Machine. In this research, 3 vision-based measurement and 2 physiological measurement were implemented in the system. Vision based measurement in facial vision domain consists of eyes blinking and in facial thermal domain consists 3 ROI’s temperature value and blood vessel volume at Supraorbital area. Two physiological measurement were done to measure the ground truth value which is heart rate and salivary amylase level. We also propose a new calibration chessboard attach with fever plaster to locate calibration point in stereo view. A new method of integration of two different sensors for detecting facial feature in both thermal and visual is also presented by applying nostril mask, which allows one to find facial feature namely nose area in thermal and visual domain. Extraction of thermal-visual feature images was done by using SIFT feature detector and extractor to verify the method of using nostril mask. Based on the experiment conducted, 88.6 % of correct matching was detected. In the eyes blinking experiment, almost 98 % match was detected successfully for without glasses and 89 % with glasses. Graph cut algorithm was applied to remove unwanted ROI. The recognition rate of 3 ROI’s was about 90 %–96 %. We also presented new method of automatic detection of blood vessel volume at Supraorbital monitored by LWIR camera. The recognition rate of correctly detected pixel was about 93 %. An experiment to measure mental stress by using the proposed system based on Support Vector Machine classification had been proposed and conducted and showed promising results.

Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.

Preliminary Design of a Robotic Exoskeleton for Arm Rehabilitation

This research paper presents the design of a low-cost and easy-to-use 2 degree of freedom (DOF) robotic exoskeleton for arm rehabilitation. The developed exoskeleton consists of a 2 DOF robotic arm attached on a chair. Force sensitive resistors are also utilized in the design of the device to measure muscles activities during rehabilitation process. Kinovea software is used to analyse the performance of the patient during exercise via video capture. The measured data hopefully can assist physicians and caregivers in designing suitable rehabilitation process for stroke patient. The proposed design provides a novel tool towards upper limb stroke rehabilitation process. Although there are many exoskeleton robotics arms which are commercially available, however, due to the disadvantages such as weight, high-cost and complex mechanisms, this paper proposed new ideas on solving these problems by designing an exoskeleton which is functional, low-cost and users friendly

Tracking and Counting Motion for Monitoring Food Intake Based-On Depth Sensor and UDOO Board: A Comprehensive Review

Technology is all about helping people, which created a new opportunity to take serious action in managing their health care. Moreover, Obesity continues to be a serious public health concern in the Malaysia and continuing to rise. Obesity has been a serious health concern among people. Nearly half of Malaysian people overweight. Most of dietary approach is not tracking and detecting the right calorie intake for weight loss, but currently used tools such as food diaries require users to manually record and track the food calories, making them difficult for daily use. We will be developing a new tool that counts the food intake bite by monitoring hand gesture and face jaw motion movement of caloric intake. The Bite count method showed a good significant that can lead to a successful weight loss by simply monitoring the bite taken during eating. The device used was Kinect Xbox One which used a depth camera to detect the motion on person hand and face during food intake. Previous studies showed that most of the method used to count bite device is worn type. The recent trend is now going towards non-wearable devices due to the difficulty when wearing devices and it has high false alarm ratio. The proposed system gets data from the Kinect that will be monitoring the hand and face gesture of the user while eating. Then, the gesture of hand and face data is sent to the microcontroller board to recognize and start counting bite taken by the user. The system recognizes the patterns of bite taken from user by following the algorithm of basic eating type either using hand or chopstick. This system can help people who are trying to follow a proper way to reduce overweight or eating disorders by monitoring their meal intake and controlling eating rate.

Discovering Web Server Logs Patterns Using Generalized Association Rules Algorithm

With the explosive growth of data available on the World Wide Web (WWW), discovery and analysis of useful information from the World Wide Web becomes a practical necessity. Data Mining is primarily concerned with the discovery of knowledge and aims to provide answers to questions that people do not know how to ask. It is not an automatic process but one that exhaustively explores very large volumes of data to determine otherwise hidden relationships. The process extracts high quality information that can be used to draw conclusions based on relationships or patterns within the data. Using the techniques used in Data Mining, Web Mining applies the techniques to the Internet by analyzing server logs and other personalized data collected from customers to provide meaningful information and knowledge. Web access pattern, which is the sequence of accesses pursued by users frequently, is a kind of interesting and useful knowledge in practice (Pei, 2000). Today web browsers provide easy access to myriad sources of text and multimedia data. With approximately 4.3 billion documents online and 20 million new web pages published each day (Tanasa and Trousse, 2004), more than 1 000 000 000 pages are indexed by search engines, and finding the desired information is not an easy task (Pal et al., 2002). Web Mining is now a popular term of techniques to analyze the data from World Wide Web (Pramudiono, 2004). A widely accepted definition of the web mining is the application of data mining techniques to web data. With regard to the type of web data, web mining can be classified into three types: Web Content Mining, Web Structure Mining and Web Usage Mining. As an important extension of data mining, Web mining is an integrated technology of various research fields including computational linguistics, statistics, informatics, artificial intelligence (AI) and knowledge discovery (Fayyad et al., 1996; Lee and Liu, 2001). Srivastava et al. (2002) classified Web Mining into three categories: Web Content Mining, Web Structure Mining, and Web Usage Mining (see Figure 1). 11