Telemedicine systems to gather the patient data from sensors generally follow the two-hop architecture. Data coming from sensors are transmitted to a gateway through sensor-manager link technologies and then they are transmitted from the gateway to the data management system (for storage and consultation) with cellular link technologies. Smartphone and web applications are designed for presenting and analyzing the data coming from the sensors based on the patient body by means of dedicated algorithm. RespirHò is a wireless, battery-powered wearable device used to measure respiratory parameters and perform Human Activity Recognition (HAR). The latest design consists of three IMU-sensor units that communicates via the ANT protocol with a PC. The data from the acquisition is passed in an offline analysis implemented with Python to retrieve the respiratory parameters. The objective of this Thesis is to realize the full-stack development of an Android smartphone and web application for this device which can be easily utilized by the user. For the smartphone application, needed to acquire and record data, many functionalities have been implemented to manage the user authentication, to store the patients’ data in a cloud, to automatically reconnect the sensors in case they disconnect, to back up the recording files periodically and to provide to the user the needed feedback for a correct acquisition. The web application created is designed to allow user’s authentication, show and download the files of all the patients’ acquisitions taken from the smartphone application. The work evaluates the reliability of the data acquired with the smartphone app, estimating the data loss of five recordings on five different subjects, and its performance. Instead, for the web app, a cross browsers testing has been done. Finally, a layout validation on different screen sizes has been done both for the smartphone and the web application.
I sistemi di telemedicina che raccolgono dati provenienti dai sensori indossati dai pazienti seguono la cosiddetta architettura “two-hop”. Il primo è la trasmissione dei dati provenienti dai sensori ad un gateway tramite comunicazione a corto raggio ed il secondo la trasmissione di questi dati dal gateway ad un sistema di gestione dei dati (per archiviarli o consultarli) solitamente tramite connessioni internet. Applicazioni smartphone e web sono progettate per mostrare e analizzare i dati provenienti da sensori collocati sul paziente per mezzo di un algoritmo dedicato. RespirHò è un dispositivo indossabile wireless utilizzato per misurare i parametri respiratori ed eseguire Human Activity Recognition (HAR). Il design più recente consiste in tre sensori IMU che comunicano tramite protocollo ANT con un PC. I dati provenienti dalle acquisizioni sono poi analizzati offline con Python per ottenere i parametri respiratori. L’obiettivo di questa tesi tesi è di realizzare una completa applicazione smartphone e web per questo dispositivo che può essere facilmente utilizzata dall’utente. Per l’applicazione smartphone, pensata per acquisire e registrare dati, sono state sviluppate diverse funzionalità tra cui la gestione dell’autenticazione dell’utente, l’archivio dei dati dei pazienti nel cloud, l’automatica riconessione dei sensori in caso si disconnettano, il back up periodico dei file delle registrazioni e una serie di feedback per l’utente per assicurarsi una corretta acquisizione. L’applicazione web creata è progettata per permettere l’autenticazione dell’utente, mostrare e scaricare i file di tutte le acquisizioni dei pazienti registrate dallo smartphone. Questo lavoro valuta la performance dell’applicazione smartphone e la sua affidabilità nel gestire i dati acquisiti, stimando la perdita di essi su cinque registrazioni, ognuna con un soggetto diverso. Invece, l’applicazione web verrà testata su diversi browsers. Infine, è stata fatta una validazione dei diversi layout su schermi con risoluzione diversa sia per l’applicazione smartphone che web.
Full-stack development of a smartphone and web application of RespirHò : a wearable device for continuous respiratory and activity monitoring
MAESTRONI, MARCO
2020/2021
Abstract
Telemedicine systems to gather the patient data from sensors generally follow the two-hop architecture. Data coming from sensors are transmitted to a gateway through sensor-manager link technologies and then they are transmitted from the gateway to the data management system (for storage and consultation) with cellular link technologies. Smartphone and web applications are designed for presenting and analyzing the data coming from the sensors based on the patient body by means of dedicated algorithm. RespirHò is a wireless, battery-powered wearable device used to measure respiratory parameters and perform Human Activity Recognition (HAR). The latest design consists of three IMU-sensor units that communicates via the ANT protocol with a PC. The data from the acquisition is passed in an offline analysis implemented with Python to retrieve the respiratory parameters. The objective of this Thesis is to realize the full-stack development of an Android smartphone and web application for this device which can be easily utilized by the user. For the smartphone application, needed to acquire and record data, many functionalities have been implemented to manage the user authentication, to store the patients’ data in a cloud, to automatically reconnect the sensors in case they disconnect, to back up the recording files periodically and to provide to the user the needed feedback for a correct acquisition. The web application created is designed to allow user’s authentication, show and download the files of all the patients’ acquisitions taken from the smartphone application. The work evaluates the reliability of the data acquired with the smartphone app, estimating the data loss of five recordings on five different subjects, and its performance. Instead, for the web app, a cross browsers testing has been done. Finally, a layout validation on different screen sizes has been done both for the smartphone and the web application.| File | Dimensione | Formato | |
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2021_12_Maestroni.pdf
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Descrizione: Master Thesis 2021 Marco Maestroni
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https://hdl.handle.net/10589/183336