• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.291-294
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• 2017

It is known that laptop computer stand is helpful to maintain the good posture while using laptop computer on the desk. But the quantitative validation of its effect has not been reported. Using the wearable neck posture tracker, the forward flexion angle of the neck can be measured in daily life. In this study, the forward flexion angles of the neck while using the laptop computer with and without laptop computer stand were compared. From the posture data of 10 subjects for 6 hours, the average of the forward flexion angle was 0.9 degree with laptop computer stand and 16.3 degree without laptop computer stand. As the conclusion, laptop computer stand can decrease the forward flexion angle which is known as forward head posture while using the laptop computer on the desk.

• Physical Therapy Korea
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• v.30 no.3
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• pp.221-229
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• 2023

Background: In modern society, the use of computers accounts for a large proportion of our daily lives. Although substantial research is being actively conducted on musculoskeletal diseases resulting from computer use, there has been a recent surge in interest in improving the working environment for prevention. Objects: This study aimed to examine the effects of posture correction feedback (PCF) on changes in neck posture and muscle activation during computer typing. Methods: The participants performed a computer typing task in two sessions, each lasting 16 minutes. The participant's dominant side was photographed and analyzed using ImageJ software to verify neck posture. Surface electromyography (EMG) was used to confirm the participant's cervical erector spinae (CES) and upper trapezius muscle activities. The EMG signal was analyzed using the percentage of reference voluntary contraction and amplitude probability distribution function (APDF). In the second session, visual and auditory feedback for posture correction was provided if the neck was flexed by more than 15° in the initial position during computer typing. A 20-minute rest period was provided between the two sessions. Results: The neck angle (p = 0.014), CES muscle activity (p = 0.008), and APDF (p = 0.015) showed significant differences depending on the presence of the PCF. Furthermore, significant differences were observed regarding the CES muscle activity (p = 0.001) and APDF (p = 0.002) over time. Conclusion: Our study showed that the feedback system can correct poor posture and reduces unnecessary muscle activation during computer work. The improved neck posture and reduced CES muscle activity observed in this study suggest that neck pain can be prevented. Based on these results, we suggest that the PCF system can be used to prevent neck pain.

• Physical Therapy Korea
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• v.15 no.4
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• pp.80-86
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• 2008

Cross-legged sitting postures are commonly assumed during computer work. The purpose of this study was to determine the effects of leg crossing on trunk muscle activity while typing at a computer. Trunk muscle activity was measured in three 8 different sitting postures, in random order. These posture were: normal sitting with a straight trunk and both feet on the floor (NS), upper leg crossing (ULC), and ankle on knee (AOK). The right leg was crossed onto the left leg in both cross-legged postures. Twenty able-bodied male volunteers participated in this study. Subjects typed on a computer keyboard for one minute. Surface electromyography (EMG) was used to record bilateral muscle activity in the external oblique (EO). internal oblique (IO), and rectus abdominis (RA). The EMG activity of each muscle in the NS posture was used as a reference (100% EMG activity) in relation to the two cross-legged postures. Muscle activity in the right EO. right IO, and left IO was significantly lower in the ULC posture than in the NS posture. In contrast, muscle activity in the right RA was significantly higher in the ULC posture than in the NS posture. Muscle activity in the tight RA was significantly higher in the AOK posture, as compared to the NS posture, whereas activity in the left IO was significantly lower in the AOK posture, as compared to the NS posture. The right-left muscle activity ratios in the EO and IO showed significantly different patterns in the cross-legged postures, suggesting that asymmetrical right-left oblique muscle activity had occurred.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.99-104
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• 2019

Recently, modern people are increasing the incidence of various musculoskeletal diseases due to wrong posture. Prevention is possible through proper posture habit, but it is not easy to recognize posture by oneself. Various studies have been conducted to monitor persistent posture in daily life, but most studies using constrained measurement methods and high-cost measurement equipment are not suitable for daily life. In this paper, we implemented a posture discrimination system using a FSR sensor array that can induce posture correction spontaneously through sitting posture monitoring in daily life. The implemented system is designed as a cushion type so it is easy to apply to existing chair. In addition, it can identify five most common postures in everyday life, and can monitor real-time through Android-based smart-phone monitoring application. For the performance evaluation of the implemented system, each posture was measured 50 times repeatedly. As a result, 97.6% accuracy was confirmed.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.219-221
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• 2007

Recent changes to ubiquitous computing requires more natural human-computer(HCI) interfaces that provide high information accessibility. Hand-gesture, i.e., gestures performed by one 'or two hands, is emerging as a viable technology to complement or replace conventional HCI technology. This paper deals with hand-posture recognition. Hand-posture database construction is important in hand-posture recognition. Human hand is composed of 27 bones and the movement of each joint is modeled by 23 degrees of freedom. Even for the same hand-posture,. grabbed images may differ depending on user's characteristic and relative position between the hand and cameras. To solve the difficulty in defining hand-postures and construct database effective in size, we present a method using a 3D hand model. Hand joint angles for each hand-posture and corresponding silhouette images from many viewpoints by projecting the model into image planes are used to construct the ?database. The proposed method does not require additional equations to define movement constraints of each joint. Also using the method, it is easy to get images of one hand-posture from many vi.ewpoints and distances. Hence it is possible to construct database more precisely and concretely. The validity of the method is evaluated by applying it to the hand-posture recognition system.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.37-46
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• 2021

In this paper, we designed and implemented a program to measure and to judge the accuracy of yoga postures using Azure Kinect. The program measures all joint positions of the user through Azure Kinect Camera and sensors. The measured values of joints are used as data to determine accuracy in two ways. The measured joint data are determined by trigonometry and Pythagoras theorem to determine the angle of the joint. In addition, the measured joint value is changed to relative position value. The calculated and obtained values are compared to the joint values and relative position values of the desired posture to determine the accuracy. Azure Kinect Camera organizes the screen so that users can check their posture and gives feedback on the user's posture accuracy to improve their posture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.805-814
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• 2021

Push-ups are one of the body exercises that can be easily measured anytime, anywhere. As one of the most widely used techniques as a test tool for evaluating physical strength, they are broadly used in various fields, especially in fields that require physical ability to estimate, such as military, police, and firefighters. However, social distancing is currently being implemented, and the issue of fairness has been steadily raised due to subtle differences between measurement. Accordingly, in this paper, the correct posture for each individual was photographed and learned by a high-performance computer, and the result was derived by comparing it with the case of performing the incorrect posture of the individual. If method is introduced into the physical fitness evaluation through the proposed method, the individual takes the correct posture and learns the photographed photo, and measures the posture with several images taken during a given time. Through this, it is possible to measure more objectively because it measures with the merit that can be measured even in the present situation and with one's correct posture.

• Journal of the Korean Society of Physical Medicine
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• v.8 no.4
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• pp.601-608
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• 2013

PURPOSE: The purpose of this study was to investigate the effect of distance between trunk and desk on forward head posture and muscle activity of neck and shoulder muscles during computer work. METHODS: Twenty subjects who have healthy conditions were asked to perform computer work in two conditions (distance between trunk and desk was 0 and 15 cm). Forward head angle was analyzed with a SIMI motion analysis system. Surface electromyography recorded the upper trapezius and splenius capitis muscles. The significance of differences in distance between trunk and desk (0cm and 15cm) was tested by paired t-test. The alpha level was set at .05. RESULTS: The results showed that the change of forward head angle was significantly higher during the computer work when the distance between trunk and desk was 15cm than 0cm. The muscle activity of upper trapezius and splenius capitis was also significantly higher during the computer work when the distance between trunk and desk was 15cm than 0cm. CONCLUSION: Our study suggest that the distance between trunk and desk was should minimized for prevention of forward head posture during computer work.