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.
Remote rehabilitation monitoring with an IoT-enabled embedded system for precise progress tracking / Rossi, Maurizio; Rizzi, Andrea; Lorenzelli, Leandro; Brunelli, Davide. - ELETTRONICO. - (2016), pp. 384-387. (Intervento presentato al convegno 23rd IEEE International Conference on Electronics, Circuits and Systems, ICECS 2016 tenutosi a Montecarlo nel 2016, 12) [10.1109/ICECS.2016.7841213].
Remote rehabilitation monitoring with an IoT-enabled embedded system for precise progress tracking
Rossi, Maurizio;Lorenzelli, Leandro;Brunelli, Davide
2016-01-01
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.File | Dimensione | Formato | |
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