M. Rossi, Andrea Rizzi, L. Lorenzelli, D. Brunelli
{"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}
引用次数: 10
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.
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