• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.33-39
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• 2013

When the eldery with limited mobility and disabled use a wheelchairs to move, it can cause decreased exercise ability like decline muscular strength in upper limb and lower extremities. The disabled people suffers with spinal cord injuries or post stroke hemiplegia are easily exposed to secondary problems due to limited mobility. In this paper, We designed intelligent wheelchair robot system for upper limb and lower extremities exercise/rehabilitation considering the characteristics of these severely disabled person. The system consists of an electric wheelchair, biometrics module for Identification characteristics of users, upper limb and lower extremities rehabilitation. In this paper, describes the design and configurations and of developed robot. Also, In order to verify the system function, conduct performance evaluation targeting non-disabled about risk context analysis with biomedical signal change and upper limb and lower extremities rehabilitation over wheelchair robot move. Consequently, it indicate sufficient tracking performance for rehabilitation as at about 86.7% average accuracy for risk context analysis and upper limb angle of 2.5 and lower extremities angle of 2.3 degrees maximum error range of joint angle.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.