• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.251-258
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• 2024

In this paper, we propose a posture correction system using the Arduino FSR402 sensor and Skeleton Keypoint artificial intelligence model. In anticipation of the introduction of AI Digital Textbook (AIDT), which will be fully implemented from 2025 under the 2022 curriculum reform, the need for research to raise awareness of the risk of digital diseases among children and adolescents and to prevent them is emphasized. The proposed system learns the correct posture for each user based on their life log information and helps them maintain good posture by determining whether they are using a smart device correctly and guiding them. In particular, for children and adolescents who experience physical changes, it has the advantage of learning the changed body information from the Skeleton Keypoint artificial intelligence model to guide the appropriate posture, and it can be confirmed that the sensor's measurement value operates normally within the error range (average error 2.53%) in measuring the correct posture for each user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1231-1236
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• 2017

In this study, we developed a system that can change position to improve obstructive sleep apnea. Blocking of the breathing airway caused by obstruction of the apnea, lateral position is provided by the airway to improve the apnea. We used a pressure sensor (FSR402) in the form of an array to determine the position of patient. The air cylinder was controlled to raise and lower the bed. As a result of calculating the pressure difference between the supine position and the lateral position, it was $0.41{\pm}0.30$ and $1.09{\pm}0.73$. In other words, when the patient is lateral position, the difference between the sensor values on the right and left side is large. Therefore, it is confirmed that the system can maintain airway to breath for improvement of obstructive sleep.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.402-409
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• 2019

Patients with dementia or elderly patients who can not move at all by themselves are at a high risk of falls and bedsore due to lack of caregivers. In this paper, to solve this problem, we propose an algorithm to determine the patient's lying postures by discriminating the main body parts such as head, shoulders, and hips based on the pressure intensity sensed at regular intervals. A smart mat with a lattice structure in which a pressure sensor is arranged so that the body part can be discriminated irrespective of the physical characteristics has been implemented. It consists of two modules of $7{\times}7$ array size. Each module consists of 49 FSR-406 sensors and independently senses pressure. For each module, the body part corresponding to the upper body or the lower body is sequentially discriminated by using a pressure distribution such as a cumulative pressure sum using a filter. The proposed algorithm can identify five lying positions by examining the inclusion relationship between body parts belonging to layer-1 such as head, shoulder, and hip area.