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

Remote Biofeedback Method for Biomedical Data Analysis

In recent years, the introduction of methods supported by technology has positively modified the traditional paradigm of rehabilitation. Interactive systems have been developed to facilitate patient involvement and to help therapist in patient\u2019s management. ReMoVES (REmote MOnitoring Validation Engineering System) platform addresses the problem of continuity of care in a smart and economical way. It can help patients with neurological, post-stroke and orthopedic impairments in recovering physical, psychological and social functions; such system will not only improve the quality of life and accelerate the recovery process for patients, but also aims at rationalizing and help the manpower required monitoring and coaching individual patients at rehabilitation centers. In order to help and support therapist work, the Remote Biofeedback Method is proposed as an instrument to understand how the patient has executed the rehabilitation exercises without seeing him directly. Therefore, the purpose of this method is to demonstrate that through the joint observation of data from simple sensors, it is possible to determine: time and method of execution of the exercises, performance and improvements during the rehabilitation session, pertinence of exercise and plan of care. The system, during the rehabilitation session, automatically transmits patient\u2019s biofeedback through three different channels: movement, physiological signals and a questionnaire. The therapist uses patient\u2019s data to determine whether the plan of care assigned is appropriate for the recovery of lost functionalities. He will then return a remote feedback to the patient who will not see any kind of graphical or verbal output, but you will see lighter rehabilitative session if it was too difficult or more intense if one assigned was too simple. The rehabilitation protocol proposed consists of the performance of different exercises, which begins with a breathing activity, designed to relax the patient before the \u201ceffective\u201d rehabilitation session. To make the subject comfortable, and to bring again the heartbeat to a basal value, before the rehabilitation session, the patient, in a sitting position, is leading to breathing with a regular rhythm by following a \u201cbreath ball\u201d. From the results obtained in the breathing exercise, it can be concluded that the negative trend of the regression line that approximates the heartbeat signal is an index of relaxation, principal goal for which the exercise was designed. The proposed activities include execution of reaching and grasping, balance and control posture functional exercises, masked through serious games to simulate some of the most common gestures of daily life. In some exercises, a cognitive component will also be involved in achieving the goal required by the activity. For each activity, heart rate, gameplay scores, and different motion parameters were captured and analyzed depending on the type of exercise performed. The heart rate was used as an indicator of motivation and involvement during the execution of several rehabilitative exercises. Others parameters analyzed are the score obtained during the execution of the task, and the time interval between the execution of one exercise and the following one. In addition to the analysis of the individual signals, a preliminary analysis of the correlation between the trend of the heart rate and the performance of the score was also carried out. The results showed that heartbeat in conjunction with score and inter-exercise time could be a high-quality indicator of a patient\u2019s status. The indicators extracted, in fact, in most cases, correspond to the information reported from the therapist who observed the patients during the rehabilitation session. A deep analysis of movement signal was carried on, with the extraction of several indicators for the different body segments involved in rehabilitation, such as the upper limb, the hand, the lower limbs and the posture, included the detection of compensation strategies to reach the targets proposed by the exercise. The results have been extracted by comparing the patient performance to a model extracted by a healthy subjects group. Of particular importance is the spatial map for patients with neglect, an innovative tool that traces the positions where the movement was performed and also provides the therapist with the spatial coordinates where the targets were proposed. Another innovative aspect is the analysis of Center of Pressure (CoP) without the use of a specific footboard, but only through the processing of data from the motion sensor. The results obtained by the application of the Remote Biofeedback Methods to the signals acquired during ReMoVES testing phase show interesting applications of the method to the clinical practice. In fact, the indicators extracted show a realistic correspondence between the disabilities affected the patients and the performance obtained during the execution of the exercises. From the study of the different exercises it can be concluded that the analysis of the signals and the parameters extracted individually, do not provide enough information to outline how the rehabilitation exercise has been executed. By combining the different indicators, it is possible to outline an accurate picture that allows the therapist to make decisions about the assigned plan of care. In conclusion, the Remote Biofeedback Method proposed is now ready to be tested on a wider dataset in order to be consolidated on a larger number of athologies and to associate, if necessary, particular indicators to a particular disease. The future steps will be, a creation of a model starting from patients signals, in order to have a better comparison term, and a testing phase on a larger number of patients, following a clinical protocol, subdividing subject by disease

Multi-frequency segmental bio-impedance device:design, development and applications

Bio-impedance analysis (BIA) provides a rapid, non-invasive technique for body composition estimation. BIA offers a convenient alternative to standard techniques such as MRI, CT scan or DEXA scan for selected types of body composition analysis. The accuracy of BIA is limited because it is an indirect method of composition analysis. It relies on linear relationships between measured impedance and morphological parameters such as height and weight to derive estimates. To overcome these underlying limitations of BIA, a multi-frequency segmental bio-impedance device was constructed through a series of iterative enhancements and improvements of existing BIA instrumentation. Key features of the design included an easy to construct current-source and compact PCB design. The final device was trialled with 22 human volunteers and measured impedance was compared against body composition estimates obtained by DEXA scan. This enabled the development of newer techniques to make BIA predictions. To add a ‘visual aspect’ to BIA, volunteers were scanned in 3D using an inexpensive scattered light gadget (Xbox Kinect controller) and 3D volumes of their limbs were compared with BIA measurements to further improve BIA predictions. A three-stage digital filtering scheme was also implemented to enable extraction of heart-rate data from recorded bio-electrical signals. Additionally modifications have been introduced to measure change in bio-impedance with motion, this could be adapted to further improve accuracy and veracity for limb composition analysis. The findings in this thesis aim to give new direction to the prediction of body composition using BIA. The design development and refinement applied to BIA in this research programme suggest new opportunities to enhance the accuracy and clinical utility of BIA for the prediction of body composition analysis. In particular, the use of bio-impedance to predict limb volumes which would provide an additional metric for body composition measurement and help distinguish between fat and muscle content

Augmenting patient therapies with video game technology

PhD ThesisThere is an increasing body of work showing that video games can be used for more than just entertainment, but can also facilitate positive physical and mental changes. For people suffering debilitating side-effects from illnesses such as stroke, there is need to deliver and monitor effective rehabilitative physical therapies; video game technologies could potentially deliver an effective alternative to traditional rehabilitative physical therapy, and alleviate the need for direct therapist oversight. Most existing research into video game therapies has focussed on the use of offthe- shelf games to augment a patient’s ongoing therapy. There has currently been little progress into how best to design bespoke software capable of integrating with traditional therapy, or how to replicate common therapies and medical measurements in software. This thesis investigates the ability for video games to be applied to stroke rehabilitation, using modern gaming peripherals for input. The work presents a quantitative measurement of motion detection quality afforded by such hardware. An extendible game development framework capable of high quality movement data output is also presented, affording detailed analysis of player responsiveness to a video game delivered therapy for acute stroke. Finally, a system by which therapists can interactively create complex physical movements for their patients to replicate in a video game environment is detailed, enabling bespoke therapies to be developed, and providing the means by which rehabilitative games for stroke can provide an assessment of patient ability similar to that afforded by traditional therapies

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