Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Human action recognition and mobility assessment in smart environments with RGB-D sensors

openQuesta attività di ricerca è focalizzata sullo sviluppo di algoritmi e soluzioni per ambienti intelligenti sfruttando sensori RGB e di profondità. In particolare, gli argomenti affrontati fanno riferimento alla valutazione della mobilità di un soggetto e al riconoscimento di azioni umane. Riguardo il primo tema, l'obiettivo è quello di implementare algoritmi per l'estrazione di parametri oggettivi che possano supportare la valutazione di test di mobilità svolta da personale sanitario. Il primo algoritmo proposto riguarda l'estrazione di sei joints sul piano sagittale utilizzando i dati di profondità forniti dal sensore Kinect. La precisione in termini di stima degli angoli di busto e ginocchio nella fase di sit-to-stand viene valutata considerando come riferimento un sistema stereofotogrammetrico basato su marker. Un secondo algoritmo viene proposto per facilitare la realizzazione del test in ambiente domestico e per consentire l'estrazione di un maggior numero di parametri dall'esecuzione del test Timed Up and Go. I dati di Kinect vengono combinati con quelli di un accelerometro attraverso un algoritmo di sincronizzazione, costituendo un setup che può essere utilizzato anche per altre applicazioni che possono beneficiare dell'utilizzo congiunto di dati RGB, profondità ed inerziali. Vengono quindi proposti algoritmi di rilevazione della caduta che sfruttano la stessa configurazione del Timed Up and Go test. Per quanto riguarda il secondo argomento affrontato, l'obiettivo è quello di effettuare la classificazione di azioni che possono essere compiute dalla persona all'interno di un ambiente domestico. Vengono quindi proposti due algoritmi di riconoscimento attività i quali utilizzano i joints dello scheletro di Kinect e sfruttano un SVM multiclasse per il riconoscimento di azioni appartenenti a dataset pubblicamente disponibili, raggiungendo risultati confrontabili con lo stato dell'arte rispetto ai dataset CAD-60, KARD, MSR Action3D.This research activity is focused on the development of algorithms and solutions for smart environments exploiting RGB and depth sensors. In particular, the addressed topics refer to mobility assessment of a subject and to human action recognition. Regarding the first topic, the goal is to implement algorithms for the extraction of objective parameters that can support the assessment of mobility tests performed by healthcare staff. The first proposed algorithm regards the extraction of six joints on the sagittal plane using depth data provided by Kinect sensor. The accuracy in terms of estimation of torso and knee angles in the sit-to-stand phase is evaluated considering a marker-based stereometric system as a reference. A second algorithm is proposed to simplify the test implementation in home environment and to allow the extraction of a greater number of parameters from the execution of the Timed Up and Go test. Kinect data are combined with those of an accelerometer through a synchronization algorithm constituting a setup that can be used also for other applications that benefit from the joint usage of RGB, depth and inertial data. Fall detection algorithms exploiting the same configuration of the Timed Up and Go test are therefore proposed. Regarding the second topic addressed, the goal is to perform the classification of human actions that can be carried out in home environment. Two algorithms for human action recognition are therefore proposed, which exploit skeleton joints of Kinect and a multi-class SVM for the recognition of actions belonging to publicly available datasets, achieving results comparable with the state of the art in the datasets CAD-60, KARD, MSR Action3D.INGEGNERIA DELL'INFORMAZIONECippitelli, EneaCippitelli, Ene

Human action recognition and mobility assessment in smart environments with RGB-D sensors

Questa attività di ricerca è focalizzata sullo sviluppo di algoritmi e soluzioni per ambienti intelligenti sfruttando sensori RGB e di profondità. In particolare, gli argomenti affrontati fanno riferimento alla valutazione della mobilità di un soggetto e al riconoscimento di azioni umane. Riguardo il primo tema, l'obiettivo è quello di implementare algoritmi per l'estrazione di parametri oggettivi che possano supportare la valutazione di test di mobilità svolta da personale sanitario. Il primo algoritmo proposto riguarda l'estrazione di sei joints sul piano sagittale utilizzando i dati di profondità forniti dal sensore Kinect. La precisione in termini di stima degli angoli di busto e ginocchio nella fase di sit-to-stand viene valutata considerando come riferimento un sistema stereofotogrammetrico basato su marker. Un secondo algoritmo viene proposto per facilitare la realizzazione del test in ambiente domestico e per consentire l'estrazione di un maggior numero di parametri dall'esecuzione del test Timed Up and Go. I dati di Kinect vengono combinati con quelli di un accelerometro attraverso un algoritmo di sincronizzazione, costituendo un setup che può essere utilizzato anche per altre applicazioni che possono beneficiare dell'utilizzo congiunto di dati RGB, profondità ed inerziali. Vengono quindi proposti algoritmi di rilevazione della caduta che sfruttano la stessa configurazione del Timed Up and Go test. Per quanto riguarda il secondo argomento affrontato, l'obiettivo è quello di effettuare la classificazione di azioni che possono essere compiute dalla persona all'interno di un ambiente domestico. Vengono quindi proposti due algoritmi di riconoscimento attività i quali utilizzano i joints dello scheletro di Kinect e sfruttano un SVM multiclasse per il riconoscimento di azioni appartenenti a dataset pubblicamente disponibili, raggiungendo risultati confrontabili con lo stato dell'arte rispetto ai dataset CAD-60, KARD, MSR Action3D.This research activity is focused on the development of algorithms and solutions for smart environments exploiting RGB and depth sensors. In particular, the addressed topics refer to mobility assessment of a subject and to human action recognition. Regarding the first topic, the goal is to implement algorithms for the extraction of objective parameters that can support the assessment of mobility tests performed by healthcare staff. The first proposed algorithm regards the extraction of six joints on the sagittal plane using depth data provided by Kinect sensor. The accuracy in terms of estimation of torso and knee angles in the sit-to-stand phase is evaluated considering a marker-based stereometric system as a reference. A second algorithm is proposed to simplify the test implementation in home environment and to allow the extraction of a greater number of parameters from the execution of the Timed Up and Go test. Kinect data are combined with those of an accelerometer through a synchronization algorithm constituting a setup that can be used also for other applications that benefit from the joint usage of RGB, depth and inertial data. Fall detection algorithms exploiting the same configuration of the Timed Up and Go test are therefore proposed. Regarding the second topic addressed, the goal is to perform the classification of human actions that can be carried out in home environment. Two algorithms for human action recognition are therefore proposed, which exploit skeleton joints of Kinect and a multi-class SVM for the recognition of actions belonging to publicly available datasets, achieving results comparable with the state of the art in the datasets CAD-60, KARD, MSR Action3D

Design and Development of ReMoVES Platform for Motion and Cognitive Rehabilitation

Exergames have recently gained popularity and scientific reliability in the field of assistive computing technology for human well-being. The ReMoVES platform, developed by the author, provides motor and cognitive exergames to be performed by elderly or disabled people, in conjunction with traditional rehabilitation. Data acquisition during the exercise takes place through Microsoft Kinect, Leap Motion and touchscreen monitor. The therapist is provided with feedback on patients' activity over time in order to assess their weakness and correct inaccurate movement attitudes. This work describes the technical characteristics of the ReMoVES platform, designed to be used by multiple locations such as rehabilitation centers or the patient's home, while providing a centralized data collection server. The system includes 15 exergames, developed from scratch by the author, with the aim of promoting motor and cognitive activity through patient entertainment. The ReMoVES platform differs from similar solutions for the automatic data processing features in support of the therapist. Three methods are presented: based on classic data analysis, on Support Vector Machine classification, and finally on Recurrent Neural Networks. The results describe how it is possible to discern patient gaming sessions with adequate performance from those with incorrect movements with an accuracy of up to 92%. The system has been used with real patients and a data database is made available to the scientific community. The aim is to encourage the dissemination of such data to lay the foundations for a comparison between similar studies

Action recognition from RGB-D data

In recent years, action recognition based on RGB-D data has attracted increasing attention. Different from traditional 2D action recognition, RGB-D data contains extra depth and skeleton modalities. Different modalities have their own characteristics. This thesis presents seven novel methods to take advantages of the three modalities for action recognition. First, effective handcrafted features are designed and frequent pattern mining method is employed to mine the most discriminative, representative and nonredundant features for skeleton-based action recognition. Second, to take advantages of powerful Convolutional Neural Networks (ConvNets), it is proposed to represent spatio-temporal information carried in 3D skeleton sequences in three 2D images by encoding the joint trajectories and their dynamics into color distribution in the images, and ConvNets are adopted to learn the discriminative features for human action recognition. Third, for depth-based action recognition, three strategies of data augmentation are proposed to apply ConvNets to small training datasets. Forth, to take full advantage of the 3D structural information offered in the depth modality and its being insensitive to illumination variations, three simple, compact yet effective images-based representations are proposed and ConvNets are adopted for feature extraction and classification. However, both of previous two methods are sensitive to noise and could not differentiate well fine-grained actions. Fifth, it is proposed to represent a depth map sequence into three pairs of structured dynamic images at body, part and joint levels respectively through bidirectional rank pooling to deal with the issue. The structured dynamic image preserves the spatial-temporal information, enhances the structure information across both body parts/joints and different temporal scales, and takes advantages of ConvNets for action recognition. Sixth, it is proposed to extract and use scene flow for action recognition from RGB and depth data. Last, to exploit the joint information in multi-modal features arising from heterogeneous sources (RGB, depth), it is proposed to cooperatively train a single ConvNet (referred to as c-ConvNet) on both RGB features and depth features, and deeply aggregate the two modalities to achieve robust action recognition

Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera

The purpose of this study was to determine the feasibility and validity of using three-dimensional (3D) video data and computer vision to estimate physical activity intensities in young children. Families with children (2–5-years-old) were invited to participate in semi-structured 20-minute play sessions that included a range of indoor play activities. During the play session, children’s physical activity (PA) was recorded using a 3D camera. PA video data were analyzed via direct observation, and 3D PA video data were processed and converted into triaxial PA accelerations using computer vision. PA video data from children (n = 10) were analyzed using direct observation as the ground truth, and the Receiver Operating Characteristic Area Under the Curve (AUC) was calculated in order to determine the classification accuracy of a Classification and Regression Tree (CART) algorithm for estimating PA intensity from video data. A CART algorithm accurately estimated the proportion of time that children spent sedentary (AUC = 0.89) in light PA (AUC = 0.87) and moderate-vigorous PA (AUC = 0.92) during the play session, and there were no significant differences (p \u3e 0.05) between the directly observed and CART-determined proportions of time spent in each activity intensity. A computer vision algorithm and 3D camera can be used to estimate the proportion of time that children spend in all activity intensities indoors

Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection

Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons. The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared. The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificida

Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection

Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Technische Universiteit Eindhoven. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA no. 2010-0012]Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons. The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared. The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificidadPostprint (published version

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Activity monitoring and behaviour analysis using RGB-depth sensors and wearable devices for ambient assisted living applications

Nei paesi sviluppati, la percentuale delle persone anziane è in costante crescita. Questa condizione è dovuta ai risultati raggiunti nel capo medico e nel miglioramento della qualità della vita. Con l'avanzare dell'età, le persone sono più soggette a malattie correlate con l'invecchiamento. Esse sono classificabili in tre gruppi: fisiche, sensoriali e mentali. Come diretta conseguenza dell'aumento della popolazione anziana ci sarà quindi una crescita dei costi nel sistema sanitario, che dovrà essere affrontata dalla UE nei prossimi anni. Una possibile soluzione a questa sfida è l'utilizzo della tecnologia. Questo concetto è chiamato Ambient Assisted living (AAL) e copre diverse aree quali ad esempio il supporto alla mobilità, la cura delle persone, la privacy, la sicurezza e le interazioni sociali. In questa tesi differenti sensori saranno utilizzati per mostrare, attraverso diverse applicazioni, le potenzialità della tecnologia nel contesto dell'AAL. In particolare verranno utilizzate le telecamere RGB-profondità e sensori indossabili. La prima applicazione sfrutta una telecamera di profondità per monitorare la distanza sensore-persona al fine di individuare possibili cadute. Un'implementazione alternativa usa l'informazione di profondità sincronizzata con l'accelerazione fornita da un dispositivo indossabile per classificare le attività realizzate dalla persona in due gruppi: Activity Daily Living e cadute. Al fine di valutare il fattore di rischio caduta negli anziani, la seconda applicazione usa la stessa configurazione descritta in precedenza per misurare i parametri cinematici del corpo durante un test clinico chiamato Timed Up and Go. Infine, la terza applicazione monitora i movimenti della persona durante il pasto per valutare se il soggetto sta seguendo una dieta corretta. L'informazione di profondità viene sfruttata per riconoscere particolari azioni mentre quella RGB per classificare oggetti di interesse come bicchieri o piatti presenti sul tavolo.Nowadays, in the developed countries, the percentage of the elderly is growing. This situation is a consequence of improvements in people's quality life and developments in the medical field. Because of ageing, people have higher probability to be affected by age-related diseases classified in three main groups physical, perceptual and mental. Therefore, the direct consequence is a growing of healthcare system costs and a not negligible financial sustainability issue which the EU will have to face in the next years. One possible solution to tackle this challenge is exploiting the advantages provided by the technology. This paradigm is called Ambient Assisted Living (AAL) and concerns different areas, such as mobility support, health and care, privacy and security, social environment and communication. In this thesis, two different type of sensors will be used to show the potentialities of the technology in the AAL scenario. RGB-Depth cameras and wearable devices will be studied to design affordable solutions. The first one is a fall detection system that uses the distance information between the target and the camera to monitor people inside the covered area. The application will trigger an alarm when recognizes a fall. An alternative implementation of the same solution synchronizes the information provided by a depth camera and a wearable device to classify the activities performed by the user in two groups: Activity Daily Living and fall. In order to assess the fall risk in the elderly, the second proposed application uses the previous sensors configuration to measure kinematic parameters of the body during a specific assessment test called Timed Up and Go. Finally, the third application monitor's the user's movements during an intake activity. Especially, the drinking gesture can be recognized by the system using the depth information to track the hand movements whereas the RGB stream is exploited to classify important objects placed on a table