Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone

It is widely recognized that sleep is a basic phys- iological process having fundamental effects on human health, performance and well-being. Such evidence stimulates the re- search of solutions to foster self-awareness of personal sleeping habits, and correct living environment management policies to encourage sleep. In this context, the use of mobile technologies powered with automatic sleep recognition capabilities can be helpful, and ubiquitous computing devices like smartphones can be leveraged as proxies to unobtrusively analyse the human behaviour. To this aim, we propose a real-time sleep recognition methodology relied on a smartphone equipped with a mobile app that exploits contextual and usage information to infer sleep habits. During an initial training stage, the selected features are processed by k-Nearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine classifiers, to select the best performing one. Moreover, a 1st-order Markov Chain is applied to improve the recognition performance. Experimental results, both offline in a Matlab environment, and online through a fully functional Android app, demonstrate the effectiveness of the proposed approach, achieving acceptable results in term of Precision, Recall, and F1-score

AUTOMOTIVE RADAR APPLICATION FOR STRUCTURAL HEALTH MONITORING

n/

Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone

It is widely recognized that sleep is a basic phys- iological process having fundamental effects on human health, performance and well-being. Such evidence stimulates the re- search of solutions to foster self-awareness of personal sleeping habits, and correct living environment management policies to encourage sleep. In this context, the use of mobile technologies powered with automatic sleep recognition capabilities can be helpful, and ubiquitous computing devices like smartphones can be leveraged as proxies to unobtrusively analyse the human behaviour. To this aim, we propose a real-time sleep recognition methodology relied on a smartphone equipped with a mobile app that exploits contextual and usage information to infer sleep habits. During an initial training stage, the selected features are processed by k-Nearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine classifiers, to select the best performing one. Moreover, a 1st-order Markov Chain is applied to improve the recognition performance. Experimental results, both offline in a Matlab environment, and online through a fully functional Android app, demonstrate the effectiveness of the proposed approach, achieving acceptable results in term of Precision, Recall, and F1-score

Tecnologie ed apparati per comunicazione a frequenze radio: dalla connettività in ambito domotico alla caratterizzazione del canale radio in banda EHF

L’attività di ricerca si è concentrata su diversi temi: i sistemi AIS, la progettazione di dispositivi per la caratterizzazione del canale radio a 76GHz ed i sistemi domotici. Per quanto riguarda l’attività in banda 76GHz il lavoro di ricerca ha riguardato la progettazione e realizzazione dei sistemi riceventi, trasmittenti e di condizionamento ed analisi dei dati. I prototipi sono stati realizzati sia negli aspetti meccanici, che elettronici. Il trasmettitore campione è basato su un oscillatore locale a 12 GHz di derivazione commerciale. Per adattarlo alle esigenze di progetto l’unità è stata sottoposta a reverse engineering e riprogrammazione del PLL. Il segnale viene moltiplicato per 3 da un moltiplicatore armonico commerciale e per 2 da un diodo per microonde (package smd 0102) il cui circuito è stato assemblato con l’aiuto di micromanipolatori. Il segnale a 76GHz viene irradiato da una parabola da 25cm. Tutto il sistema è controllato da una scheda Raspberry PI, che acquisisce anche dati meteo ed elettrici per mezzo di una scheda figlia, anch’essa sviluppata ad hoc. I dati raccolti sono immagazzinati in un database MySQL e resi disponibili per il controllo attraverso interfaccia WEB. L’alimentazione del sistema è realizzata con tecnologia POE. Il ricevitore è parte di un progetto più ampio, volto a realizzare un transverter. La sezione in alta frequenza è simile alla precedente e differisce solo nella tipologia di componente usato per l’ultima moltiplicazione armonica: un doppio diodo al posto di uno singolo. Il segnale ricevuto viene convertito a 144MHz e reso disponibile al connettore della IF. Viene quindi effettuata una ulteriore conversione in discesa a 50 MHz attraverso un mixer bilanciato ed un oscillatore locale realizzato con Si570 (VXCO controllato I2C). La conversione ha consentito il riuso di filtri e ha dato la possibilità di potere correggere eventuali derive in frequenza del sistema. La frequenza dell’oscillatore locale è infatti controllata dalla Raspberry PI attraverso un programma in C. Il segnale viene quindi fornito ad un power meter Analog Devices con uscita in tensione. La tensione risultata viene acquisita attraverso un ADC Microchip a 18bit e riflette, non essendo presenti circuiti di controllo automatico del guadagno, l’intensità del segnale ricevuto a 76GHz. Il valore di tensione viene quindi archiviato in un database MySQL e può essere aggregato ai dati meteo raccolti dal trasmettitore per consentire uno studio più accurato dei fenomeni propagativi in questa banda di frequenza. Il progetto ha visto la collaborazione di alcuni radioamatori locali che hanno messo a disposizione alcuni strumenti per la verifica dei segnali a 76GHz. Appena disponibili sul mercato sono anche state acquisite due unità commerciali operanti a 76GHz che possono operare in ambiente indoor e sono il ’riferimento’ per la calibrazione ed il test dei prototipi realizzati internamente. Le unità trasmettitore e ricevitore sono collegate tra loro attraverso un link point-to-point realizzato in tecnologia Hiperlan. I sistemi AIS sono nati come ausilio alla sicurezza della navigazione marittima, e prevedono l’invio periodico, da parte di natanti e stazioni fisse, di brevi messaggi contenenti i dati di posizione e stato della navigazione. I segnali inviati dalle stazioni fisse del sistema sono particolarmente interessanti in quanto il numero delle stazioni ricevibili (ed in particolare da una ricevente posta in una posizione favorevole come Ancona) varia in funzione delle condizioni di propagazione troposferica. Per confermare questa affermazione è stato realizzato un sistema ricevente composto da unapparato radio commerciale ed un software, scritto in inguaggio PHP, in grado di decodificare le stringhe di testo ricevute. I dati raccolti sono immagazzinati in un database MySQL e pubblicati attraverso un sito web. La serie temporale delle stazioni fisse ricevute è stata confrontata con altri indicatori della bontà della propagazione troposferica: l’intensità del segnale ricevuto da un trasmettitore campione posto in Croazia e le mappe di propagazione redatte sulla base delle condizioni metereologiche. Positivo il riscontro ottenuto in questo secondo caso, a testimonianza del fatto che le stazioni fisse del sistema AIS possano essere usate come indicatore indiretto della bontà della propagazione troposferica in banda VHF. Da questo progetto sono nate una collaborazione con un sito di ascolto radio FM a lunga distanza (fmlist.org) con sede in Germania, con il sito Marinetraffic.com e con il prof. Dimitrios Lekkas professore associato presso l’Università dell’Egeo. In ambito domotico ed AAL, l’attività di ricerca è stata rivolta alla realizzazione della ’Smart Insole’ ed alla interoperabilità tra diverse piattaforme. Nel primo caso è stata curata la realizzazione del sistema di acquisizione dati e comunicazione radio, con particolare riferimento alla parte di gestione dell’energia del sistema, valutando diverse soluzioni hardware per aumentare l’autonomia di funzionamento del dispositivo. Sono stati analizzati diversi prodotti (circuiti integrati di gestione della ricarica per accumulatori LiPO), scegliendo poi un prodotto Microchip che è stato ampiamente caratterizzato ed adattato alle esigenze del progetto. Lo studio ha portato alla realizzazione di una unità molto compatta che integra sia la parte di elaborazione ed invio dati, sia la gestione energetica del sistema, consentendo anche la ricarica wireless. Il secondo contributo è stato dato nella realizzazione della infrastruttura di elaborazione e immagazzinamento dati, integrando nel sistema uno storage molto capiente in tecnologia iSCSI. Ulteriore contributo è stato dato nella ottimizzazione della rete di sensori e nella gestione dei flussi dati verso l’elaboratore centrale.Research activity focused on three main areas: AIS systems, development of a complete set of devices for 76GHz channel characterization and domotic systems. For what EHF communications concerns, the main activity was the development and prototyping of a communication system made up of a transmitter, a receiver and some signal measurements blocks. Project activity has been dealing with mechanical, electrical and RF aspects. The transmitter side is based on a commercial grade synthesizer which covers the spectrum from 12650MHz to 13200MHz. In order to have a 76032MHz output frequency, the oscillator was reverse engineered and hacked by changing the way the internal PLL is programmed. Signal at 12GHz is fed to a first commercial grade multiplier which feeds a microwave diode (Macomm smd package 0102). The active component was glued on the PCB by means of a silver conductive epoxy and micromanipulators. 76GHz signal is fed to a parabolic antenna having a diameter of 25cm. The whole system is controlled using a Raspberry PI micro-computer, which also acts as a data collector using a self-developed daughterboard. Data are stored in a MySql database and users can display the results of data elaboration using a web server. System power is provided via POE. The receiver side is more complex as it derives from a transverter project. The high frequency side is very similar to the one used in transmitter. The active device is a double-diode in a single 0102 package. The 76GHz signal is downconveted to 144MHz (1st conversion) and then to 50 MHz (2nd conversion). The latter is made using a doubly-balanced mixer and a local oscillator (94MHz) realized with a Si570 I2C controlled VXCO. The second conversion allows the reuse of some filters that were available in the laboratory and a frequency correction to compensate main oscillator drifts. Local oscillator frequency can ben programmed via Raspberry interface. 50MHz signal is fed to a power meter manufactured by Analog Devices. It’s a true RMS/DC converter whose output signal is fed to a Microchip ADC with a 18bit resolution. No AGC cirtuitry is present, so the power level measured by the device at 50MHz, reflects the signal level at 76GHz. The output of the power meter is stored in the database too. Signal and weather data can be processed and displayed simultaneously to better characterize channel versus time and weather conditions. Project was developed thanks to the help of some amateur radio enthusiasts who provided know-how and instrumentation to measure 76GHz signals. Two commercial units working at 76GHz were buyed too, in order to have a "reference" for prototypes improvement and characterization. Transmitter and receiver sides are data linked using an Hiperlan connection. AIS is a tracking system used by ships to increase navigation security, by exchanging data among nearby ships, base stations and satellite. Exchanged data is in the form of short messages containing geographical position and navigation status. We focused our attention on base stations (fixed) and noticed that the number of different base station received is time varying. Variation are caused by changes in the tropospheric conditions which lead to an alteration of electromagnetic waves propagation. In order to demonstrate the hypothesis a receiving system has been deployed. It is made up of a commercial VHF receiver and AIS demodulator which output AIS messages in form of a string via tcp/ip connection. AIS messages are decoded by a a software written in PHP language and able to store decoded data in a MySql data base. Data can be accessed via web interface and it is possible to visualize historical data set and plot graphics. The number of fixed stations received was compared versus the signal intensity of a becon transmitter located in Croatia and versus Dx forecasting maps. The latter are drawn startin from numerical meteo data and showed a good correlation with the number of fixed station received. This shows that the number of received fixed stations from AIS can be used to investigate electromagnetic wave propagation in the VHF band. Within this project a collaboration with fmlist.org arouse, which is an international community dedicated to long distance broadcast listeners. Research activity in domotics was focused on the "smart-insole" and interoperability among different platforms. The "smart-insole" is a data acquisition platform which if tailored to fit in shoe. Three resistive force sensors have been installed on the bottom of the insole. Data is acquired by a microcontroller board which process information and transmit the results to a central server by means of a wireless ISM-band connection. A great effort was done in order to keep energy consumption very low and to optimize recharging of integrated LiPo batteries. Many different electronic devices were compared. The final solution was the use of a Microchip-based demo board which was modified to reduce the physical dimensions and to adapt to project constrains. In order to recharge batteries a wireless solution was developed which minimizes human interaction with the system. In the interoperability field the contribution given lead to the creation of a complex hardware/software architecture for data processing and storage. A big network mass-storage was configured using ISCSI in order to be completely hidden to final user

People Walking Classification using Automotive Radar

none4Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.noneSenigagliesi, Linda; Ciattaglia, Gianluca; De Santis, Adelmo; Gambi, EnnioSenigagliesi, Linda; Ciattaglia, Gianluca; De Santis, Adelmo; Gambi, Enni

ADL recognition through machine learning algorithms on IoT air quality sensor dataset

The Human Activity Recognition is a focal point for Ambient Assisted Living, and may be implemented in several ways usually involving the use of different technologies, as wearable, video, environmental or radio frequency sensors, which can be used alone or in combination among them. Recently, the approaches based on machine learning have attracted a lot of interest, especially in order to create recognition systems that do not require a high detection capacity by the single sensor, as they base their decision on the processing of the information acquired from multiple sensors simultaneously. The aim of the present work is to derive information about the activities that are carried out inside the house on the basis of the data acquired by a set of sensors analyzing the air components. The Human Activity Recognition is then the result of a machine learning classification of the output of an array of low cost “commercial off-the-shelf” air quality sensors. The considered recognition system exploits electrochemical sensing, Wi-Fi technology, cloud computing, machine learning and application services. The obtained results evidence that a good accuracy in the recognition of “activities of daily living” is reached, even if a not calibrated sensing was performed

A WKNN-based approach for NB-IoT sensors localization

With the recent introduction of NarrowBand Internet of Things (NB-IoT) technology in the 4th and 5th generations of mobile radio networks, the mobile communications context opens up significantly to the world of sensors. By means of NB-IoT, the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks, thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications. Among the functions of interest for sensor networks, the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest. The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals, which therefore does not require further hardware implementations on board the nodes. This procedure, based on the RF fingerprinting technique and on machine learning processing, has been tested experimentally and has achieved interesting performances

Performance Evaluation of Vibrational Measurements through mmWave Automotive Radars

Thanks to the availability of a significant amount of inexpensive commercial Frequency Modulated Continuous Wave Radar sensors, designed primarily for the automotive domain, it is interesting to understand if they can be used in alternative applications. It is well known that with a radar system it is possible to identify the micro-Doppler feature of a target, to detect the nature of the target itself (what the target is) or how it is vibrating. In fact, thanks to their high transmission frequency, large bandwidth and very short chirp signals, radars designed for automotive applications are able to provide sub-millimeter resolution and a large detection bandwidth, to the point that it is here proposed to exploit them in the vibrational analysis of a target. The aim is to evaluate what information on the vibrations can be extracted, and what are the performance obtainable. In the present work, the use of a commercial Frequency Modulated Continuous Wave radar is described, and the performances achieved in terms of displacement and vibration frequency measurement of the target are compared with the measurement results obtained through a laser vibrometer, considered as the reference instrument. The attained experimental results show that the radar under test and the reference laser vibrometer achieve comparable outcomes, even in a cluttered scenario