Myagmarbayar Nergui

DIABETES MELLITUS: ENQUIRY INTO ITS MEDICAL ASPECTS AND BIOENGINEERING OF ITS MONITORING AND REGULATION

Diabetes mellitus (DM) or hyperglycemia (in a more generalized term, high blood sugar) is a metabolic disorder that is now highly prevalent in the world population. Most of the food that people consume is converted into glucose, which enters the bloodstream following absorption–assimilation mechanisms. As a natural process, cells in our body utilize glucose for growth and energy. The glucose balance is maintained by a hormone called insulin that is secreted by the beta cells of pancreas. Hypotheses at the backdrop of DM occurrence are either (i) enough insulin is not produced and secreted resulting in increased level of glucose in blood, or (ii) insulin is insensitive to glucose, or (iii) insulin is non-targeted etc. If DM remains uncontrolled over time, it leads to serious damage to many of the bodys systems, especially the nerves and blood vessels. This paper develops an enquiry into diabetes from many angles: (i) Diabetes as a disorder, its complications, causes, diagnostic tests, and treatment; (ii) ...

Reliable and Robust Transmission and Storage Techniques for Medical Images with Patient Information

There is an increased emphasis on the use of digital techniques in all aspects of human life today. Broadcast radio and television, cellular phone services, consumer and entertainment electronics etc are increasingly using digital signal processing techniques to improve the quality of service. Transmission and storage of documentation and images pertaining to patient records cannot remain an exception to this global trend. Hence, patient records (text and image information) are increasingly stored and processed in digital form. Currently, text and image information, which constitute two separate pieces of data are handled as different files. Thus, there is a possibility of the text and message information, pertaining to different patients, being interchanged and thus mishandled. This can be avoided by merging text and image information in such a manner that the two can be separated without perceptible damage to information contained in either file. Digital watermarking techniques can be used to interleave patient information with medical images. In this work, we have employed digital watermarking along with strong cryptographic protocols and powerful error correcting codes. This reduces the probability of sensitive patient information falling into the wrong hands and ensures information integrity when it is conveyed over noisy channels.

EFFECTS OF MOBILE PHONE RADIATION ON CARDIAC HEALTH

Mobile phones (MPs) progressed from a tool of the privileged few to a gadget for the masses. However, the physical effects, which enable wireless information transmission, did not change; MP technology still relies on pulsed high-frequency electromagnetic (EM) fields. Therefore, the health risks, associated with EM fields, remain. Studies that investigated these health risks have reported dizziness, numbness in the thigh, and heaviness in the chest. This study investigates neurological effects that are caused by EM fields radiated from MPs. The heart rate variability (HRV) can be used as a measure for these neurological effects, because the automated nervous system modulates the HRV. We measured the HRV of 14 healthy male volunteers. We used the following nonlinear parameters to quantify the MP radiation effects on HRV: approximate entropy (ApEn), capacity dimension (CaD), correlation dimension (CD), fractal dimension (FD), Hurst exponent (H), and the largest Lyapunov exponent (LLE). The results indicate ...

Development of a Sound Source Localization System for Assisting Group Conversation

In this study, we developed a sound source localization system, which consists of Jellyfish-02 and HARK robot audition software, in order to reduce the number of wires for evaluating speech duration. Sound source localization performance of Jellyfish-02 is evaluated by precision, recall, and F-measure. Performance of Jellyfish-02 is superior to conventional microphone arrays. During the experiment, we found that F-measure becomes smaller as the number of speakers increases. We investigated the percentage of speech overlapped periods in natural conversation for the purpose of examining the applicability of the system to measure speech duration in group conversation. From the results, Jelyyfish-02 surpasses conventional microphone array in design and usability. It is potentially applicable for assisting group conversion by measuring duration of speech for each participant.

An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot

Our previous studies demonstrated that the idea of bio-monitoring home healthcare mobile robots is feasible. Therefore, by developing algorithms for mobile robot based tracking, measuring, and activity recognition of human subjects, we would be able to help impaired people (MIPs) to spend more time focusing in their motor function rehabilitation process from their homes. In this study we aimed at improving two important modules in these kinds of systems: the control of the robot and visual tracking of the human subject. For this purpose: 1) tracking strategies for different types of home environments were tested in a simulator to investigate the effect on robot behavior; 2) a multi- channel saliency fusion model with high perceptual quality was proposed and integrated into RGB-D based visual tracking. Regarding the control strategies, results showed that, depending on different types of room environment, different tracking strategies should be employed. For the visual tracking, the proposed saliency fusion model yielded good results by improving the saliency output. Also, the integration of this saliency model resulted in better performance of RGB-D based visual tracking application.Saliency computation has become a popular research field for many applications due to the useful information provided by saliency maps. For a saliency map, local relations around the salient regions in multi-channel perspective should be taken into consideration by aiming uniformity on the region of interest as an internal approach. And, irrelevant salient regions have to be avoided as much as possible. Most of the works achieve these criteria with external processing modules; however, these can be accomplished during the conspicuity map fusion process. Therefore, in this paper, a new model is proposed for saliency/conspicuity map fusion with two concepts: a) input image transformation relying on the principal component analysis (PCA), and b) saliency conspicuity map fusion with multi-channel pulsed coupled neural network (m-PCNN). Experimental results, which are evaluated by precision, recall, F-measure, and area under curve (AUC), support the reliability of the proposed method by enhancing the saliency computation.

Development of a Tool for Assisting Group Conversation by Re-Voicing Supportive Responses

Interactive group conversation is very important for improvement of cognitive function and prevention of dementia. Interactive means that all the participants participate equally and actively into group conversation. How to make group conversation more interactive for all participants is a challenging task. Main purpose of this study is to develop the system that assists group conversation, which is capable of dealing with following things, face detection and recognition, speech certain contents recognition, repetition or re-voice of supportive responses based on the result of speech contents recognition, and sound source localization using less sensory environment. We did dialogue experiments with arbitrary pairs among seven young adults and analyzed 14 dialogue data for calculating correct recognition rate of face and speech certain contents, for synthesizing speech based on the result of recognition, and for calculating sound source. The performance of the developed system was preliminary verified through the experimental results. The developed system can be used as a basis of assisting group conversation.

Probabilistic Information Structure of Human Walking

Recently, the area of healthcare has been tremendously benefited from the advent of high performance computing in improving quality of life. Different processing techniques have been developed to understand the hidden complexity of the time series and will help clinicians in diagnosis and treatment. Analysis of human walking helps to study the various pathological conditions affecting balance and the elderly. In an elderly subjects, falls and paralysis are major problems, in terms of both frequency and consequences. Correct postural balance is important to well being and its effects will be felt in every movement and activity. In this paper, Bayesian Network (BN) was applied to recorded muscle activities and joint motions during walking, to extract causal information structure of normal walking and different impaired walking. The aim of this study is to use different BNs to express normal walking and various impaired walking, and identify the most important causal pairs that characterize specific impaired walking, through comparing the BNs for different walking.

Human motion tracking and recognition using HMM by a mobile robot

Purpose – The aim of this paper is to develop autonomous mobile home healthcare robots, which are capable of observing patients’ motions, recognizing the patients’ behaviours based on observation data, and providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost‐effective, safe and easier at‐home rehabilitation to most motor‐function impaired patients (MIPs).Design/methodology/approach – The paper has developed following programs/control algorithms: control algorithms for a mobile robot to track and follow human motions, to measure human joint trajectories, and to calculate angles of lower limb joints; and algorithms for recognizing human gait behaviours based on the calculated joints angle data.Findings – A Hidden Markov Model (HMM) based human gait behaviour recognition taking lower limb joint angles and body angle as input was proposed. The proposed HMM based gait behaviour recognition is compared with the Nearest Neighbour (NN) cla...

Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference

Understanding the biomechanics of the human foot during each stage of walking is important for the objective evaluation of movement dysfunction, accuracy of diagnosis, and prediction of foot impairment. Extracting causal relations from amongst the muscle activities, toe trajectories, and plantar pressures during walking assists in recognizing several disease conditions, and understanding the hidden complexity of human foot functions, thus, facilitating appropriate therapy and treatment. To extract these relations, we applied the Bayesian Network (BN) model to data collected in the stance phase of walking. For a better understanding of foot function, the experimental data were divided into three stages (initial contact, loading response to mid-stance, and terminal stance to pre-swing). BNs were constructed for these three stages of data for normal walking and simulated hemiplegic walking, then compared and analyzed. Results showed that BNs extracted could express the underlying mechanism of foot function.

Human Behavior Recognition by a Mobile Robot Following Human Subjects

Our research is focused on the home healthcare support system for motor function impaired persons (MIPs) whose motor function should be closely monitored during either in-hospital or at-home training therapy process. Especially, for the at-home monitoring, the demand of which is increasing, not only close observation, but also accurate behavior recognition of daily living activity, as well as motor function evaluation, are necessary. In this study, such a system was established by developing a cost-effective, safe and easy to use mobile robot. With such a robotic monitoring system, the in-hospital time for most MIPs and the burden to therapists can be significantly decreased. In order to realize the robotic monitoring system, we proposed several algorithms to solve the difficulties arising from the mobile sensing for moving MIPs, and recognize several frequent daily living activities, including impaired walking. Concretely, algorithms to use both color images and depth images was proposed to improve the accuracy of MIPs measurement, and a Hidden Markov Model (HMM) was implemented to deal with the uncertainty on time sequence data and relate the state transitions over time for daily living activity recognition. Experiments have demonstrated promising results on joint trajectory measurement, and recognition of daily living activities.