Pornchai Mongkolnam

Automatic multiple Kinect cameras setting for simple walking posture analysis

We propose an automatic setting of multiple skeletal tracking Kinect cameras, in lieu of mere using a single camera, to capture a human skeleton because of possible viewing occlusions. Using multiple cameras from different angles gives a more complete whole body; however, more required steps are needed in combining multiple skeletons into one final skeleton. One camera is used as a reference for the other cameras to transform their coordinates into the reference cameras coordinate system. Once every view is in the same coordinate, one skeleton is able to be composed. Due to cameras sensory errors, nevertheless, the supposedly same joint of the skeleton, which is obtained from the transformations, may not be exactly located at the same position. Therefore, average joints are used for the composed skeleton. The skeleton is then used to analyze the walking posture of a human subject in order to check whether or not the walking is balanced.

Smart bedroom for elderly using kinect

Ideas of building smart homes have been around for many years. However, most of them have been confined to laboratories or specially built, expensive homes due to the high cost of sensory hardware and software. In order to make some of those ideas realized for average-income households, we could resort to lower cost devices and focus more on smaller space like a bedroom or living room. Kinect sensors are able to capture motions of a human and present them as a series of time-dependent skeletons. They have been utilized in many fields such as entertainment, security, and health care. Particularly, they could be applied to assist the elderly population of which the number has been increasing substantially around the world when compared to other age groups. This work proposes the simple yet effective system that would facilitate the daily activities of an elderly person in a bedroom, e.g., helping the elderly turn on or turn off electric devices such as a television, electric fan, or room light without touching them or their remote controls. The system could also detect and alert the elderly of falling out of bed and monitor signs of abnormity or any need of assistance so that family members or caregiver could be timely notified.

Optical Music Recognition on Android Platform

This paper describes the concept and algorithms used in an optical music recognition application on the Android mobile platform. The application can recognize a scanned image or an image taken from a camera phone of sheet music to be interpreted and exported as a playable melody in both MIDI and MusicXML formats while handling resource utilization on an Android mobile phone platform. Limited processing performance and memory capacity, including the lack of image processing and other related APIs, are major issues that cause the algorithms used in the application to be different from traditional approaches applied in software on a PC platform. The proposed system performed with a 76.03% accuracy rate for the scanned sheet music and 71.43% for the sheet music captured by a mobile phone’s camera, which are quite significant values for a mobile platform with limited resources.

Multimedia Delivery for Elderly People: A Conceptual Model

The number of elderly people living alone is increasing proportionally as the ageing population increases. As elderly people are considered to have a greater risk for loneliness, depression, and decreased mobility, they may need someone else’s help for proper health and social care. That concern is particularly important for those living alone. Most of them become less active and less focused in their daily lives. The receiving of care would largely depend on how many family members and friends the elderly has, and whether or not they live close by to the elderly. This research proposes a conceptual model to deliver multimedia to the elderly people in order to lessen the aforementioned problems. The content is sent via the Internet from family members’ and friends’ devices such as smartphones and tablet PCs to the elderly people’s television set. The proposed model is simple yet efficient and effective and is intended to increase the quality of life (QoL) of the ageing population.

Office workers syndrome monitoring using kinect

Many office workers today sit and work at computers for extended periods of time, which can result in a group of symptoms called “Office Workers Syndrome”. To help prevent these symptoms, we propose a novel system to monitor computer users by using a Kinect camera. Firstly, data mining classification is applied for detection of prolonged sitting, while mathematics that include a spherical coordinate system and geometry as well as threshold models is applied to detect unhealthy postures. Secondly, the system gives an alert to the user when unhealthy postures are detected, via simple popup/voice messages or via an alerting device developed by using a microcontroller. Moreover, this research also focuses on enhancing the user experience, various user-interfaces and data visualization techniques for generating useful summary reports are provided.

Detect the Daily Activities and In-house Locations Using Smartphone

Falls are a key cause of significant health problems, especially for elderly people who live alone. Falls are a leading cause of accidental injury and death. To help assist the elderly, we propose a system to detect daily activities and in-house location of a user by means of a smartphone’s sensor and Wi-Fi access points. We applied data mining techniques to classify activity detection (e.g., sitting, standing, lying down, walking, running, walking up/downstairs, and falling) and in-house location detection. Health risk level configurations (threshold model) are applied for unhealthy activity detection with an alarm sounding and also short messages sent to those who have responsibility such as a caregiver or a doctor. Moreover, we provide various forms of easy to understand visualization for monitoring and include health risk level summary, daily activity summary, and in-house location summary.

Postural classification using Kinect

This research focuses on the comparison of posture recognition, using a data mining classification approach on the skeleton data stream obtained from Kinect camera. We classified four standard postures including Stand, Sit, Sit on floor and Lie Down. We compared six classifiers, namely, decision tree, neural network, naïve Bayes, support vector machine, logistic regression and random forest in order to find a suitable classifier. Our best results can correctly classify the postures with 97.88% accuracy, 97.40% sensitivity, and 0.991 ROC area under curve using Max-Min normalization with a decision tree classifier on four transformed attributes. Our future work will use the knowledge obtained to classify a wider range of postures of the elderly while watching television, to be a part of a bigger effort to monitor and study elderly behavior at home.

Multiple-Stage Classification of Human Poses while Watching Television

We compared the accuracy measure between a single-stage classifier model and a multiple-stage classifier model in postural classifications using Kinect. Postural training sets were collected from Kinects skeletal data streams, based on some of the common human postures during television watching. Three types of training sets were used, including Kinects raw skeletal training set, skeletons with attribute selection training set, and skeletal position transformation training set. We selected four learning models, namely, neural network, naïve Bayes, logistic regression, and decision tree, for learning our data sets and classifying a testing set to find the appropriate learning model. The best accuracy value of our experiment was 87.68 % by using skeletal position transformation training set with neural network. In the future, we will apply our technique and methodology to track elderly behaviors while they are watching television.

A Push for Software Process Improvement in Thailand

Capability Maturity Model Integration (CMMI) is considered by far the most comprehensive and well-known model for Software Process Improvement (SPI). The cost of having CMMI appraisal, including effort, time, and money is almost certainly out of reach for most very-small-sized, small-sized, and medium-sized software companies in Thailand. Thus most of them resort to the less expensive and locally invented standard in hope that they could improve their software processes and still stay competitive financially. However, to compete successfully in a long run, they need to formally implement and institutionalize SPI, using the more well-known and mature model such as CMMI. As more and more multinational companies outsource to Asia Pacific region, it even becomes imperative for the software companies to deliver high quality products in time and within budget to demanding customers. Due to the aforementioned needs, Software Industry Promotion Agency (SIPA), one of the leading government’s arms that promote, support, and steer a software industry in Thailand, funded us in 2008 to thoroughly and formally investigate a status of the Thai software industry with respect to SPI, specifically the impact of adopting CMMI on the Thai software industry. The obtained results would then be used to form both near-term and long-term macro strategies, which would help lay down directions and push burgeoning Thai software companies to become competent players in a global software market.

Reverse engineering using loop subdivision

AbstractSubdivision surfaces have become popular in Computer Aided Design (CAD) and animation packages. Popular choices include Loop, Catmull-Clark, Doo-Sabin, etc. Subdivision surfaces have many advantages over the traditional use of NURBS, which are problematic where multiple patches meet. Possible applications of subdivision surfaces are surface reconstruction, mesh compression and reverse engineering of dense triangle meshes. We present the Loop subdivision scheme as a tool to approximate dense triangle meshes of arbitrary topology. The paper shows the process as well as some satisfactory results of CAD models.