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Objective: The purpose of this study is to find the level of physical relaxation of individual subject by monitoring psychophysiological biofeedback to different types of mattresses. And, the study also aims to find a protocol to make a selection of the best mattress based on the measured information. Background: In Korea, there are an increasing number of people using western style bed. However, they are often fastidious in choosing the right mattress for them. In fact, people use their past experience with their old mattress as well as the spontaneous experience they encounter in a show room to finally decide to buy a bed. Method: Total five mattresses were tested in this study. After measuring the elasticity of the mattresses, they were sorted into five different classes. Physiological and psychological variables including Electromyography (EMG), heart rates (HR), oxygen saturations (SaO2) were used. In addition, the peak body pressure concentration rate was used to find uncomfortably pressured body part. Finally, the personal factors and subjective satisfaction were also examined. A protocol was made to select the best mattress for individual subject. The selection rule for the protocol considered all the variables tested in this study. Results: The result revealing psychological comfort range of 0.68 to 0.95, dermal comfort range of 3.15 to 6.07, back muscle relaxation range of 0.25 to 1.64 and personal habit range of 2.0 to 3.4 was drawn in this study. Also a regression model was developed to predict biofeedback with the minimal use of biofeedback devices. Moreover results from the proposed protocol with the regression equation and subjective satisfaction were compared with each other for validation. Ten out of twenty subjects recorded the same level of relaxation, and eight subjects showed one-level difference while two subjects showed two-levels difference. Conclusion: The psychophysiological variables and suitability selection process used in this study seem to be used for selecting and assessing ergonomic products mechanically or emotionally. Application: This regression model can be applied to the mattress industry to estimate back muscle relaxation using dermal, psychophysiology and personal habit values.

The overall aim of this study is to collect stroke assessment methodology for pre-detect the stroke using the wearable devices. The particular objectives of the study were (1) to find various stroke-monitoring methodologies, (2) to compare stroke-monitoring methodologies, (3) to develop a methodology that can pre-detect the stroke. For this purpose, a systematic search was conducted using Google Scholar for studies published from 2010 to 2015. The search was restricted to papers published in English and containing the terms “stroke motion”, “stroke detection”, “stroke application”. The literature survey revealed various stroke detection methods including motion pattern, gait analysis, head impulse test, electrocardiogram, eye movements, muscle activity recognition, and so on.

A hand-held sport characterises racket sports including squash, badminton, tennis, and table tennis. This study discusses squash and kinematics involved. There are varies shots can be played in squash including rail shot, boast shot, and backhand drop shot. Unlike other shots, the backhand drop shot requires control over the racket, control in swing speed and hitting angle, control in angular displacement of elbow, trunk and shoulder joints. With author's knowledge, there were not much research studies on squash kinematics. This study considered squash backhand drop shot and performed the kinematic analysis. Subjects (squash players) in the twenties participated, who had no injuries in the last six months. The players were separated into expert and novice groups. The kinematic analysis was performed through a 3D motion analysis system when the players are performing backhand drop shot. The results compare the displacement and velocities of trunk and racket, and the angular displacements and velocities of elbow and shoulder joints between the groups. Statistical analysis was performed using SPSS 20.0. The lap time required by the expert group was longer than that of the novice group. The expert group was used significantly (p