Deep convolutional neural network classifier for travel patterns using binary sensors

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.This research is partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

Device-Free Non-Privacy Invasive Classification of Elderly Travel Patterns in A Smart House Using PIR Sensors and DCNN

Single resident life style is increasing among the elderly due to the issues of elderly care cost and privacy invasion. However, the single life style cannot be maintained if they have dementia. Thus, the early detection of dementia is crucial. Systems with wearable devices or cameras are not preferred choice for the long-term monitoring. Main intention of this paper is to propose deep convolutional neural network (DCNN) classifier for indoor travel patterns of elderly people living alone using open data set collected by device-free non-privacy invasive binary (passive infrared) sensor data. Travel patterns are classified as direct, pacing, lapping, or random according to Martino– Saltzman (MS) model. MS travel pattern is highly related with person’s cognitive state, and thus can be used to detect early stage of dementia. We have utilized an open data set that was presented by Center for Advanced Studies in Adaptive Systems project, Washington State University. The data set was collected by monitoring a cognitively normal elderly person by wireless passive infrared sensors for 21 months. First, 117 320 travel episodes are extracted from the data set and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12 000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing data set. Finally, DCNN performance was compared with seven other classical machine-learning classifiers. The random forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

Promoting the adoption of assistive technologies to aid with spatial orientation in dementia care

Introduction: In dementia care, locating technologies are a type of assistive technology that hold the potential to improve the quality of life of persons with dementia and their care partners by assisting in the management of spatial orientation impairments and wandering. Although many products are commercially available, their adoption remains low. To better understand how to promote their adoption, we examined user experience and clinical effectiveness resulting from product use and explored barriers to their adoption. Methods: In a first user experience study, a prototype locating technology was tested for four weeks by 17 dyads composed of persons with dementia and their care partners. In a second user experience study, two similar commercially available locating technologies were tested for four weeks each by another 17 dyads. User experience was examined with ratings of product usability, product functions and product features. Clinical effectiveness, frequency of use, purchase willingness, and product satisfaction were assessed with various scales. In a third qualitative focus group interview study with 22 interdisciplinary professional stakeholders, we explored views on the barriers to their adoption, as well as views on services and information dissemination strategies. Results: In the first study, the prototype was rated fairly in terms of usability, product functions and product features. However, usability ratings significantly decreased after four weeks. In the second study, ratings of usability, as well as of several product functions and product features were significantly more favourable for one of the two tested commercial products. Clinical effectiveness was not found in either study. In the third study, the main adoption barriers were based on unclear benefits and ethical concerns, as well as limitations in awareness, technology, product characteristics, and capital investments. Key services and information dissemination strategies centred on digital autonomy support, emergency support, information dissemination actors, product acquisition, and product advertising. Discussion: Results from both user experience studies indicate that focusing on specific product functions and features might substantially improve user experience. This might translate to measurable clinical effectiveness and higher adoption rates. Results from our qualitative study indicate that not only product characteristics and the technology itself impact adoption. Indeed, focusing on services and information dissemination strategies around products warrants closer attention as they might markedly improve adoption.Einleitung: Ortungssysteme in der Demenzversorgung gelten als eine vielversprechende Art von assistierender Technologie, um die Lebensqualität von Menschen mit Demenz und ihren Pflegepartnern zu verbessern, indem sie dabei helfen räumliche Orientierungsstörungen und Wanderungen zu bewältigen. Ihre Verwendung bleibt jedoch trotz der Verfügbarkeit vieler kom-merzieller Produkte gering. Um besser zu verstehen, wie ihre Verwendung gefördert werden kann, haben wir die Nutzererfahrung und klinische Wirksamkeit, die sich aus der Produktnutzung ergeben sowie die Barrieren für ihre Einführung untersucht. Methoden: In einer ersten Nutzererfahrungsstudie wurde ein Prototyp Ortungssystem vier Wo-chen lang von 17 Dyaden bestehend aus Menschen mit Demenz und ihren Pflegepartnern ge-testet. In einer zweiten Nutzererfahrungsstudie wurden zwei ähnliche kommerziell erhältliche Or-tungssysteme jeweils vier Wochen lang von weiteren 17 Dyaden getestet. Die Nutzererfahrung wurde mit Bewertungen der Benutzerfreundlichkeit, Produktfunktionen und Produkteigenschaften untersucht. Klinische Wirksamkeit, Nutzungshäufigkeit, Kaufbereitschaft und Produktzufrieden-heit wurden mit verschiedenen Skalen bewertet. In einer dritten qualitativen Fokusgruppeninter-viewstudie mit 22 interdisziplinären professionellen Stakeholdern untersuchten wir Ansichten zu den Barrieren für ihre Verwendung sowie zu Dienstleistungen und Strategien zur Informationsverbreitung. Ergebnisse: In der ersten Studie waren die Bewertungen der Benutzerfreundlichkeit, Produkt-funktionen und Produkteigenschaften mittelmäßig. Die Bewertung der Benutzerfreundlichkeit ging jedoch nach vier Wochen deutlich zurück. In der zweiten Studie fielen die Bewertungen der Benutzerfreundlichkeit sowie einiger Produktfunktionen und Produkteigenschaften bei einem der beiden getesteten Produkte deutlich besser aus. Klinische Wirksamkeit wurde in keiner der Studien gefunden. In der dritten Studie konzentrierten sich die wichtigsten Einführungsbarrieren auf unklare Vorteile und ethische Bedenken sowie auf bewusstseins-, technologisch-, produktmerkmal- und kapitalinvestitionsbasierte Einschränkungen. Dienstleistungen und Strategien zur Informationsverbreitung konzentrierten sich auf Unterstützung von digitaler Autonomie, Notfallunterstützung, Akteure der Informationsverbreitung, Produktakquisition und Produktwerbung. Diskussion: Die Ergebnisse beider Studien zur Nutzererfahrung zeigen, dass die Nutzererfah-rung durch die Optimierung bestimmter Produktfunktionen und Produkteigenschaften erheblich verbessert werden kann. Dies könnte zu einer messbaren klinischen Wirksamkeit und höheren Verwendung führen. Die Ergebnisse unserer qualitativen Studie zeigen, dass die Verwendung durch mehr als die Produktemerkmale und die Technologie selbst bestimmt wird. Deshalb ist eine gezielte Fokussierung auf Dienstleistungen und Strategien zur Informationsverbreitung rund um Ortungssysteme notwendig, da sie die Verwendung deutlich verbessern könnte

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people