M. Rossi, Andrea Rizzi, L. Lorenzelli, D. Brunelli
{"title":"远程康复监测与支持物联网的嵌入式系统,以实现精确的进度跟踪","authors":"M. Rossi, Andrea Rizzi, L. Lorenzelli, D. Brunelli","doi":"10.1109/ICECS.2016.7841213","DOIUrl":null,"url":null,"abstract":"We present an embedded system designed for enabling telemedicine and remote monitoring of the people's progresses during physical rehabilitation tasks. The system consists of a modular electronics designed to interface a matrix of 32 bendable force sensors (piezoresistive or piezoelectric) assembled on a flexible PCB. It implements the analog conditioning and digital processing of sensors readout to build a pressure map of the patients' activity with up to 62.5 ksps sampling rate. Moreover, the Wi-Fi interface integrated on the microcontroller allows a live communication between user and physician, in addition to standard local logging of workout information. The reduced power consumption in live streaming conditions (less than 750mW) permits more than 8 hours autonomy of the system with a standard battery supply. Results demonstrate the performance of the proposed mapping system.","PeriodicalId":205556,"journal":{"name":"2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Remote rehabilitation monitoring with an IoT-enabled embedded system for precise progress tracking\",\"authors\":\"M. Rossi, Andrea Rizzi, L. Lorenzelli, D. Brunelli\",\"doi\":\"10.1109/ICECS.2016.7841213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an embedded system designed for enabling telemedicine and remote monitoring of the people's progresses during physical rehabilitation tasks. The system consists of a modular electronics designed to interface a matrix of 32 bendable force sensors (piezoresistive or piezoelectric) assembled on a flexible PCB. It implements the analog conditioning and digital processing of sensors readout to build a pressure map of the patients' activity with up to 62.5 ksps sampling rate. Moreover, the Wi-Fi interface integrated on the microcontroller allows a live communication between user and physician, in addition to standard local logging of workout information. The reduced power consumption in live streaming conditions (less than 750mW) permits more than 8 hours autonomy of the system with a standard battery supply. Results demonstrate the performance of the proposed mapping system.\",\"PeriodicalId\":205556,\"journal\":{\"name\":\"2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECS.2016.7841213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECS.2016.7841213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Remote rehabilitation monitoring with an IoT-enabled embedded system for precise progress tracking
We present an embedded system designed for enabling telemedicine and remote monitoring of the people's progresses during physical rehabilitation tasks. The system consists of a modular electronics designed to interface a matrix of 32 bendable force sensors (piezoresistive or piezoelectric) assembled on a flexible PCB. It implements the analog conditioning and digital processing of sensors readout to build a pressure map of the patients' activity with up to 62.5 ksps sampling rate. Moreover, the Wi-Fi interface integrated on the microcontroller allows a live communication between user and physician, in addition to standard local logging of workout information. The reduced power consumption in live streaming conditions (less than 750mW) permits more than 8 hours autonomy of the system with a standard battery supply. Results demonstrate the performance of the proposed mapping system.