{"title":"防止碰撞及失衡的智能手机联动电动轮椅","authors":"김태현, Chong-Sun Lee, YangGyu Jei","doi":"10.17958/ksmt.21.5.201910.826","DOIUrl":null,"url":null,"abstract":"This research has been conducted to develop a prevention module of collision and balance lost in electric wheelchairs. Ultrasonic sensors were used for detecting obstacles. Giro and acceleration sensors were used for measuring the tilt angle of wheel chair frame. Kalman filters were employed for filtering high-frequency noise of acceleration sensor and removing zero drift of the Giro sensor. The presence and location of obstacles were detected, and their information was sent to the user smartphone. When the tilt angle of the wheelchair frame exceeded a certain value, a warning signal was set to operate. Raspberry Pi camera module was used to transfer rear view image data to the smartphone to help the driver acknowledge obstacles in the rear side. A DSP processor with high computing performance was necessary to realize a real-time processing of the sensor and image data. Our research showed that a prevention module of collision and balance lost connected to a smartphone can be realized at a considerable low price.","PeriodicalId":168106,"journal":{"name":"Journal of the Korean Society of Mechanical Technology","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"충돌 및 균형상실 사고 방지를 위한 스마트폰 연동 전동 휠체어\",\"authors\":\"김태현, Chong-Sun Lee, YangGyu Jei\",\"doi\":\"10.17958/ksmt.21.5.201910.826\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research has been conducted to develop a prevention module of collision and balance lost in electric wheelchairs. Ultrasonic sensors were used for detecting obstacles. Giro and acceleration sensors were used for measuring the tilt angle of wheel chair frame. Kalman filters were employed for filtering high-frequency noise of acceleration sensor and removing zero drift of the Giro sensor. The presence and location of obstacles were detected, and their information was sent to the user smartphone. When the tilt angle of the wheelchair frame exceeded a certain value, a warning signal was set to operate. Raspberry Pi camera module was used to transfer rear view image data to the smartphone to help the driver acknowledge obstacles in the rear side. A DSP processor with high computing performance was necessary to realize a real-time processing of the sensor and image data. Our research showed that a prevention module of collision and balance lost connected to a smartphone can be realized at a considerable low price.\",\"PeriodicalId\":168106,\"journal\":{\"name\":\"Journal of the Korean Society of Mechanical Technology\",\"volume\":\"92 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Korean Society of Mechanical Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17958/ksmt.21.5.201910.826\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Society of Mechanical Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17958/ksmt.21.5.201910.826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This research has been conducted to develop a prevention module of collision and balance lost in electric wheelchairs. Ultrasonic sensors were used for detecting obstacles. Giro and acceleration sensors were used for measuring the tilt angle of wheel chair frame. Kalman filters were employed for filtering high-frequency noise of acceleration sensor and removing zero drift of the Giro sensor. The presence and location of obstacles were detected, and their information was sent to the user smartphone. When the tilt angle of the wheelchair frame exceeded a certain value, a warning signal was set to operate. Raspberry Pi camera module was used to transfer rear view image data to the smartphone to help the driver acknowledge obstacles in the rear side. A DSP processor with high computing performance was necessary to realize a real-time processing of the sensor and image data. Our research showed that a prevention module of collision and balance lost connected to a smartphone can be realized at a considerable low price.