Aung Aung Phyo Wai

Bio: Aung Aung Phyo Wai is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Body area network & Wireless sensor network. The author has an hindex of 14, co-authored 65 publications receiving 587 citations. Previous affiliations of Aung Aung Phyo Wai include Institute for Infocomm Research Singapore & Agency for Science, Technology and Research.

Multimodal Sleeping Posture Classification

Abstract: Sleeping posture reveals important information for eldercare and patient care, especially for bed ridden patients. Traditionally, some works address the problem from either pressure sensor or video image. This paper presents a multimodal approach to sleeping posture classification. Features from pressure sensor map and video image have been proposed in order to characterize the posture patterns. The spatiotemporal registration of the two modalities has been considered in the design, and the joint feature extraction and data fusion is presented. Using multi-class SVM, experiment results demonstrate that the multimodal approach achieves better performance than the approaches using single modal sensing.

Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

Abstract: Monitoring and timely intervention are extremely important in the continuous management of health and wellness among all segments of the population, but particularly among those with mild dementia. In relation to this, we prescribe three design principles for the construction of services and applications. These are ambient intelligence, service continuity, and micro-context. In this paper, we provide three exemplars from our research and development activities that illustrate the use of these design principles in the construction of services and applications. All the applications are drawn from the field of care for mild dementia patients in their living quarters.

Smart wireless continence management system for persons with dementia.

Abstract: Incontinence is highly prevalent in the elderly population, especially in nursing home residents with dementia. It is a distressing and costly health problem that affects not only the patients but also the caregivers. Effective continence management is required to provide quality care, and to eliminate high labor costs and annoyances to the caregivers resulting from episodes of incontinence. This paper presents the design, development, and preliminary deployment of a smart wireless continence management system for dementia-impaired elderly or patients in institutional care settings such as nursing homes and hospitals. Specifically, the mote wireless platform was used to support the deployment of potentially large quantities of wetness sensors with wider coverage and with dramatically less complexity and cost. It consists of an intelligent signal relay mechanism so that the residents are free to move about in the nursing home or hospital and allows personalized continence management service. Preliminary results from a trial in a local nursing home are promising and can significantly improve the quality of care for patients.

Sensor-Based Activity Recognition

Abstract: Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

A Wireless Sensor Network.

Abstract: The Wireless sensor network play a vital role in collecting a Real – Time data, monitoring environmental conditions based on technology adoption. These sensor network is the combination of sensing, computation, and communication through a single tiny device. Here many tiny nodes assemble and configure themselves. It also controls actuators that extend control from cyberspace into the physical world. Here the sensor nodes communicate with the local peers rather than the high – power control tower or base station. Instead, of relying on a predeployed infrastructure, each individual sensor or actuator become part of the overall infrastructure. Here we have three hardware platforms that addresses the needs of wireless sensor netwoks. The operating system here uses an event based execution to support concurrency. The platform serves as a baseline and does not contain any hardware accelerators. . First platform serves as a baseline and it produces Operating system concepts for refining concurrency mechanisms. The second node validates the architectural designs and improve the communicational rates. The third node represents the full realization of the general architecture. Keywords— node, platform, concurrency.

Machine learning in mental health: a scoping review of methods and applications.

Abstract: BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.