Sistema di monitoraggio modulare attraverso tecnologie mobile per la sicurezza in ambiente domestico.

Con questo progetto ci si propone di creare un'applicazione per i device mobili intelligenti, in particolare smartphone e smartwatch, con sistema operativo Android, in grado di rilevare una caduta con il conseguente invio automatico di un messaggio di allarme mirato a richiamare i soccorsi in tempo reale. Data la funzionalità, l'app è stata principalmente progettata per un utente di età avanzata.

Active Assistance for Senior Healthcare: A Wearable System for Fall Detection

Elders lose independence and wellbeing, accompanied by decreased functions in terms of hearing, vision, strength and coordination abilities. These factors contribute to balance difficulties that eventually lead to falls. The injuries due to falls, at this age, are risky, since most of the times may cause a significant – and permanent – decrease of quality of life or, in extreme cases, death. In this context, a fall detection system can bring an added value to assist elderly people.This paper describes a system consisting of a wearable sensor unit, a smartphone and a website. When the sensor detects a fall it sends an alert using the smartphone via Bluetooth 4.0, to notify the family members or stakeholders. The sensor device includes an inertial unit, a barometer, and a temperature and humidity sensor. The website displays the log of previous falls and enables the configuration of emergency contact numbers. The proposed fall detection system is one of multiple components within a larger project under development that offers a holistic perspective on falls; the complete wearable solution will also feature, among others, physical protection (minimizing the impact of falls that occur).

A low-voltage RF-CMOS receiver front-end for a wireless fall detection microsystem

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores, pela Universidade Nova de Ciências e Tecnologia

An enhanced fall detection system for elderly person monitoring using consumer home networks

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate.

From fall-risk assessment to fall detection: inertial sensors in the clinical routine and daily life

Falls are caused by complex interaction between multiple risk factors which may be modified by age, disease and environment. A variety of methods and tools for fall risk assessment have been proposed, but none of which is universally accepted. Existing tools are generally not capable of providing a quantitative predictive assessment of fall risk. The need for objective, cost-effective and clinically applicable methods would enable quantitative assessment of fall risk on a subject-specific basis. Tracking objectively falls risk could provide timely feedback about the effectiveness of administered interventions enabling intervention strategies to be modified or changed if found to be ineffective. Moreover, some of the fundamental factors leading to falls and what actually happens during a fall remain unclear. Objectively documented and measured falls are needed to improve knowledge of fall in order to develop more effective prevention strategies and prolong independent living. In the last decade, several research groups have developed sensor-based automatic or semi-automatic fall risk assessment tools using wearable inertial sensors. This approach may also serve to detect falls. At the moment, i) several fall-risk assessment studies based on inertial sensors, even if promising, lack of a biomechanical model-based approach which could provide accurate and more detailed measurements of interests (e.g., joint moments, forces) and ii) the number of published real-world fall data of older people in a real-world environment is minimal since most authors have used simulations with healthy volunteers as a surrogate for real-world falls. With these limitations in mind, this thesis aims i) to suggest a novel method for the kinematics and dynamics evaluation of functional motor tasks, often used in clinics for the fall-risk evaluation, through a body sensor network and a biomechanical approach and ii) to define the guidelines for a fall detection algorithm based on a real-world fall database availability.

Besafe: un'applicazione Android per la rilevazione di aggressioni attraverso il riconoscimento del tipo di mobilità

Tematiche di violenza e aggressione sono oggi di estrema attualità e sempre più spesso se ne sente parlare al telegiornale o in programmi specializzati. Aggressione per molestie, furto o per scopi razziali; le motivazioni e i casi d'interesse sono vari e spesso hanno inizio senza alcun apparente motivo e la sensazione di sentirsi sempre meno al sicuro, anche appena usciti di casa, può degenerare ad una vera e propria paranoia. L'unica cosa che è sempre al nostro fianco, oggi giorno, sono i nostri smartphone, che risultano sempre più sofisticati e intelligenti; perché, allora, non provare ad usarli come protezione? L'obiettivo su cui si è incentrata questa tesi è, appunto, il riconoscimento di un'aggressione basata sull'analisi della situazione in cui si trova l'utente, attraverso l'uso dei sensori messi a disposizione dagli odierni smartphone in circolazione. Esistono già numerose applicazioni per la sicurezza personale, ma il metodo utilizzato per la segnalazione di un'aggressione è sempre basato sulla pressione di un pulsante o un'azione particolare che l'utente deve svolgere. L'applicazione creata in questo studio, invece, cerca di riconoscere le situazioni di pericolo osservando i movimenti dell'utente e basa il riconoscimento sulla presenza di situazioni fuori dalla normale quotidianità che, attraverso dei "controlli di conferma", permettono di riconoscere il pericolo in maniera completamente autonoma. Si è deciso di approcciarsi ad un riconoscimento autonomo, in quanto, non sempre si ha la possibilità, o il tempo, di prendere in mano il proprio smartphone per avvisare del pericolo e molte volte il panico potrebbe far perdere la lucidità alla vittima, il cui primo pensiero è quello di difendersi e scappare e non utilizzare il dispositivo. Altre volte, distrarsi anche per un secondo, potrebbe essere fatale per la propria sicurezza. Per questo motivo si è ricercato un'approccio di riconoscimento basato "sull'osservazione" di ciò che sta accadendo, piuttosto che sull'attesa di un segnale. L'obiettivo di riconoscimento prefissato è stato quello delle aggressioni in strada e i sensori utilizzati a questo scopo sono stati: accelerometro, giroscopio, GPS e microfono. Attraverso la combinazione di questi sensori, infatti, è stato possibile riconoscere cadute (di forte entità), urla e probabili spinte/strattoni. Si sono studiate, per tanto, le caratteristiche che collegassero queste tipologie di situazioni per ogni sensore preso in esame, costruendo un'approccio di riconoscimento risultato valido per gli obiettivi minimi prefissati.

It is always on your mind: experiences and perceptions of falling of older people and their carers and the potential of a mobile falls detection device

Background. Falls and fear of falling present a major risk to older people as both can affect their quality of life and independence. Mobile assistive technologies (AT) fall detection devices may maximise the potential for older people to live independently for as long as possible within their own homes by facilitating early detection of falls. Aims. To explore the experiences and perceptions of older people and their carers as to the potential of a mobile falls detection AT device. Methods. Nine focus groups with 47 participants including both older people with a range of health conditions and their carers. Interviews were audio recorded, transcribed verbatim, and thematically analysed. Results. Four key themes were identified relating to participants’ experiences and perceptions of falling and the potential impact of a mobile falls detector: cause of falling, falling as everyday vulnerability, the environmental context of falling, and regaining confidence and independence by having a mobile falls detector. Conclusion. The perceived benefits of a mobile falls detector may differ between older people and their carers. The experience of falling has to be taken into account when designing mobile assistive technology devices as these may influence perceptions of such devices and how older people utilise them.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

Sensor-driven agenda for intelligent home care of the elderly

The exponential increase of home-bound persons who live alone and are in need of continuous monitoring requires new solutions to current problems. Most of these cases present illnesses such as motor or psychological disabilities that deprive of a normal living. Common events such as forgetfulness or falls are quite common and have to be prevented or dealt with. This paper introduces a platform to guide and assist these persons (mostly elderly people) by providing multisensory monitoring and intelligent assistance. The platform operates at three levels. The lower level, denominated ‘‘Data acquisition and processing’’performs the usual tasks of a monitoring system, collecting and processing data from the sensors for the purpose of detecting and tracking humans. The aim is to identify their activities in an intermediate level called ‘‘activity detection’’. The upper level, ‘‘Scheduling and decision-making’’, consists of a scheduler which provides warnings, schedules events in an intelligent manner and serves as an interface to the rest of the platform. The idea is to use mobile and static sensors performing constant monitoring of the user and his/her environment, providing a safe environment and an immediate response to severe problems. A case study on elderly fall detection in a nursery home bedroom demonstrates the usefulness of the proposal.

Pulseira inteligente para monitorização de funções vitais

A crescente utilização de dispositivos móveis com diferentes finalidades é uma realidade. Com estes dispositivos, o utilizador tem a necessidade de aceder e usar dados em tempo real provenientes de diversas fontes. Uma tendência acentuada passa pela incorporação destes dispositivos móveis no vestuário, designados por dispositivos wearable. Segundo a empresa IMS Research, o mercado deste tipo de dispositivos irá aumentar de 14 milhões de unidades registadas no presente ano (2013), para cerca de 171 milhões em 2016, sendo esta previsão conservadora, segundo o analista da IMS Research, Theo Ahadome [15]. A maioria dos dispositivos portáteis está atualmente projetada para questões de saúde, como a monitorização do nível de glicose e batimento cardíaco. O objetivo deste trabalho passa por definir e implementar um dispositivo wearable para aplicações de saúde com um conjunto de funcionalidades para monitorização dos sinais vitais do utilizador. Posteriormente esta base pode ser aplicada em cenários de aplicação distintos, em que todos os dispositivos comunicam entre si, e fazem o reencaminhamento da informação para onde mais interessar. Foi desenhado e implementado hardware e software, para a construção de aplicações capazes de realizar a monitorização do batimento cardíaco, temperatura e humidade corporal, deteção de quedas, qualidade do sono, e chamadas de emergência. Este trabalho aborda os diferentes cenários e aplicações da utilização deste dispositivo, invocando as necessidades específicas de cada situação, sendo estas necessidades trabalhadas e transformadas em características e especificações do sistema. A plataforma de hardware e software permite criar um ecossistema de aplicações, permitindo usar todas as infraestruturas do sistema desenvolvido em futuros cenários de aplicação.

Vidéosurveillance intelligente pour la détection de chutes chez les personnes âgées

Les pays industrialisés comme le Canada doivent faire face au vieillissement de leur population. En particulier, la majorité des personnes âgées, vivant à domicile et souvent seules, font face à des situations à risques telles que des chutes. Dans ce contexte, la vidéosurveillance est une solution innovante qui peut leur permettre de vivre normalement dans un environnement sécurisé. L’idée serait de placer un réseau de caméras dans l’appartement de la personne pour détecter automatiquement une chute. En cas de problème, un message pourrait être envoyé suivant l’urgence aux secours ou à la famille via une connexion internet sécurisée. Pour un système bas coût, nous avons limité le nombre de caméras à une seule par pièce ce qui nous a poussé à explorer les méthodes monoculaires de détection de chutes. Nous avons d’abord exploré le problème d’un point de vue 2D (image) en nous intéressant aux changements importants de la silhouette de la personne lors d’une chute. Les données d’activités normales d’une personne âgée ont été modélisées par un mélange de gaussiennes nous permettant de détecter tout événement anormal. Notre méthode a été validée à l’aide d’une vidéothèque de chutes simulées et d’activités normales réalistes. Cependant, une information 3D telle que la localisation de la personne par rapport à son environnement peut être très intéressante pour un système d’analyse de comportement. Bien qu’il soit préférable d’utiliser un système multi-caméras pour obtenir une information 3D, nous avons prouvé qu’avec une seule caméra calibrée, il était possible de localiser une personne dans son environnement grâce à sa tête. Concrêtement, la tête de la personne, modélisée par une ellipsoide, est suivie dans la séquence d’images à l’aide d’un filtre à particules. La précision de la localisation 3D de la tête a été évaluée avec une bibliothèque de séquence vidéos contenant les vraies localisations 3D obtenues par un système de capture de mouvement (Motion Capture). Un exemple d’application utilisant la trajectoire 3D de la tête est proposée dans le cadre de la détection de chutes. En conclusion, un système de vidéosurveillance pour la détection de chutes avec une seule caméra par pièce est parfaitement envisageable. Pour réduire au maximum les risques de fausses alarmes, une méthode hybride combinant des informations 2D et 3D pourrait être envisagée.

Détection des chutes par calcul homographique

La vidéosurveillance a pour objectif principal de protéger les personnes et les biens en détectant tout comportement anormal. Ceci ne serait possible sans la détection de mouvement dans l’image. Ce processus complexe se base le plus souvent sur une opération de soustraction de l’arrière-plan statique d’une scène sur l’image. Mais il se trouve qu’en vidéosurveillance, des caméras sont souvent en mouvement, engendrant ainsi, un changement significatif de l’arrière-plan; la soustraction de l’arrière-plan devient alors problématique. Nous proposons dans ce travail, une méthode de détection de mouvement et particulièrement de chutes qui s’affranchit de la soustraction de l’arrière-plan et exploite la rotation de la caméra dans la détection du mouvement en utilisant le calcul homographique. Nos résultats sur des données synthétiques et réelles démontrent la faisabilité de cette approche.

Analyse d’information tridimensionnelle issue de systèmes multi-caméras pour la détection de la chute et l’analyse de la marche

Réalisé en cotutelle avec le laboratoire M2S de Rennes 2

Détection de chute à l'aide d'une caméra de profondeur

Les chutes chez les personnes âgées représentent un problème important de santé publique. Des études montrent qu’environ 30 % des personnes âgées de 65 ans et plus chutent chaque année au Canada, entraînant des conséquences néfastes sur les plans individuel, familiale et sociale. Face à une telle situation la vidéosurveillance est une solution efficace assurant la sécurité de ces personnes. À ce jour de nombreux systèmes d’assistance de services à la personne existent. Ces dispositifs permettent à la personne âgée de vivre chez elle tout en assurant sa sécurité par le port d'un capteur. Cependant le port du capteur en permanence par le sujet est peu confortable et contraignant. C'est pourquoi la recherche s’est récemment intéressée à l’utilisation de caméras au lieu de capteurs portables. Le but de ce projet est de démontrer que l'utilisation d'un dispositif de vidéosurveillance peut contribuer à la réduction de ce fléau. Dans ce document nous présentons une approche de détection automatique de chute, basée sur une méthode de suivi 3D du sujet en utilisant une caméra de profondeur (Kinect de Microsoft) positionnée à la verticale du sol. Ce suivi est réalisé en utilisant la silhouette extraite en temps réel avec une approche robuste d’extraction de fond 3D basée sur la variation de profondeur des pixels dans la scène. Cette méthode se fondera sur une initialisation par une capture de la scène sans aucun sujet. Une fois la silhouette extraite, les 10% de la silhouette correspondant à la zone la plus haute de la silhouette (la plus proche de l'objectif de la Kinect) sera analysée en temps réel selon la vitesse et la position de son centre de gravité. Ces critères permettront donc après analyse de détecter la chute, puis d'émettre un signal (courrier ou texto) vers l'individu ou à l’autorité en charge de la personne âgée. Cette méthode a été validée à l’aide de plusieurs vidéos de chutes simulées par un cascadeur. La position de la caméra et son information de profondeur réduisent de façon considérable les risques de fausses alarmes de chute. Positionnée verticalement au sol, la caméra permet donc d'analyser la scène et surtout de procéder au suivi de la silhouette sans occultation majeure, qui conduisent dans certains cas à des fausses alertes. En outre les différents critères de détection de chute, sont des caractéristiques fiables pour différencier la chute d'une personne, d'un accroupissement ou d'une position assise. Néanmoins l'angle de vue de la caméra demeure un problème car il n'est pas assez grand pour couvrir une surface conséquente. Une solution à ce dilemme serait de fixer une lentille sur l'objectif de la Kinect permettant l’élargissement de la zone surveillée.

Body ares networks for human motor assessment

Healthcare, Human Computer Interfaces (HCI), Security and Biometry are the most promising application scenario directly involved in the Body Area Networks (BANs) evolution. Both wearable devices and sensors directly integrated in garments envision a word in which each of us is supervised by an invisible assistant monitoring our health and daily-life activities. New opportunities are enabled because improvements in sensors miniaturization and transmission efficiency of the wireless protocols, that achieved the integration of high computational power aboard independent, energy-autonomous, small form factor devices. Application’s purposes are various: (I) data collection to achieve off-line knowledge discovery; (II) user notification of his/her activities or in case a danger occurs; (III) biofeedback rehabilitation; (IV) remote alarm activation in case the subject need assistance; (V) introduction of a more natural interaction with the surrounding computerized environment; (VI) users identification by physiological or behavioral characteristics. Telemedicine and mHealth [1] are two of the leading concepts directly related to healthcare. The capability to borne unobtrusiveness objects supports users’ autonomy. A new sense of freedom is shown to the user, not only supported by a psychological help but a real safety improvement. Furthermore, medical community aims the introduction of new devices to innovate patient treatments. In particular, the extension of the ambulatory analysis in the real life scenario by proving continuous acquisition. The wide diffusion of emerging wellness portable equipment extended the usability of wearable devices also for fitness and training by monitoring user performance on the working task. The learning of the right execution techniques related to work, sport, music can be supported by an electronic trainer furnishing the adequate aid. HCIs made real the concept of Ubiquitous, Pervasive Computing and Calm Technology introduced in the 1988 by Marc Weiser and John Seeley Brown. They promotes the creation of pervasive environments, enhancing the human experience. Context aware, adaptive and proactive environments serve and help people by becoming sensitive and reactive to their presence, since electronics is ubiquitous and deployed everywhere. In this thesis we pay attention to the integration of all the aspects involved in a BAN development. Starting from the choice of sensors we design the node, configure the radio network, implement real-time data analysis and provide a feedback to the user. We present algorithms to be implemented in wearable assistant for posture and gait analysis and to provide assistance on different walking conditions, preventing falls. Our aim, expressed by the idea to contribute at the development of a non proprietary solutions, driven us to integrate commercial and standard solutions in our devices. We use sensors available on the market and avoided to design specialized sensors in ASIC technologies. We employ standard radio protocol and open source projects when it was achieved. The specific contributions of the PhD research activities are presented and discussed in the following. • We have designed and build several wireless sensor node providing both sensing and actuator capability making the focus on the flexibility, small form factor and low power consumption. The key idea was to develop a simple and general purpose architecture for rapid analysis, prototyping and deployment of BAN solutions. Two different sensing units are integrated: kinematic (3D accelerometer and 3D gyroscopes) and kinetic (foot-floor contact pressure forces). Two kind of feedbacks were implemented: audio and vibrotactile. • Since the system built is a suitable platform for testing and measuring the features and the constraints of a sensor network (radio communication, network protocols, power consumption and autonomy), we made a comparison between Bluetooth and ZigBee performance in terms of throughput and energy efficiency. Test in the field evaluate the usability in the fall detection scenario. • To prove the flexibility of the architecture designed, we have implemented a wearable system for human posture rehabilitation. The application was developed in conjunction with biomedical engineers who provided the audio-algorithms to furnish a biofeedback to the user about his/her stability. • We explored off-line gait analysis of collected data, developing an algorithm to detect foot inclination in the sagittal plane, during walk. • In collaboration with the Wearable Lab – ETH, Zurich, we developed an algorithm to monitor the user during several walking condition where the user carry a load. The remainder of the thesis is organized as follows. Chapter I gives an overview about Body Area Networks (BANs), illustrating the relevant features of this technology and the key challenges still open. It concludes with a short list of the real solutions and prototypes proposed by academic research and manufacturers. The domain of the posture and gait analysis, the methodologies, and the technologies used to provide real-time feedback on detected events, are illustrated in Chapter II. The Chapter III and IV, respectively, shown BANs developed with the purpose to detect fall and monitor the gait taking advantage by two inertial measurement unit and baropodometric insoles. Chapter V reports an audio-biofeedback system to improve balance on the information provided by the use centre of mass. A walking assistant based on the KNN classifier to detect walking alteration on load carriage, is described in Chapter VI.