When technology cares for people with dementia:A critical review using neuropsychological rehabilitation as a conceptual framework

Clinicians and researchers have become increasingly interested in the potential of technology in assisting persons with dementia (PwD). However, several issues have emerged in relation to how studies have conceptualized who the main technology user is (PwD/carer), how technology is used (as compensatory, environment modification, monitoring or retraining tool), why it is used (i.e., what impairments and/or disabilities are supported) and what variables have been considered as relevant to support engagement with technology. In this review we adopted a Neuropsychological Rehabilitation perspective to analyse 253 studies reporting on technological solutions for PwD. We analysed purposes/uses, supported impairments and disabilities and how engagement was considered. Findings showed that the most frequent purposes of technology use were compensation and monitoring, supporting orientation, sequencing complex actions and memory impairments in a wide range of activities. The few studies that addressed the issue of engagement with technology considered how the ease of use, social appropriateness, level of personalization, dynamic adaptation and carers' mediation allowed technology to adapt to PWD's and carers' preferences and performance. Conceptual and methodological tools emerged as outcomes of the analytical process, representing an important contribution to understanding the role of technologies to increase PwD's wellbeing and orient future research.University of Huddersfield, under grants URF301-01 and URF506-01

A fuzzy temporal data-mining model for activity recognition in smart homes

At present time, aging of the population is one of the main challenges of the 21st century. The current situation is leading to an increased number of people afflicted with cognitive disorders such as Alzheimer's disease. This group of people suffers from a progressive decline in their abilities to perform what are called the activities of the daily living (ADLs).The consequence of this reality is the urgent need for more home assistance services, as these people desire to continue living independently at home. To address this important issue, Smart Home laboratories such as LIARA, DOMUS and MavHome perform research in order to propose technological solutions for assistance provision to residents of the Smart Home. Assisting people in carrying out their ADLs, increasing quality of life and optimizing spent energy are some of the goals in Smart Home design. Technically speaking, a Smart Home is an ambient environment which, through its embedded sensors, captures data resulting from the observation of activities carried out in this environment. This data is then analyzed by artificial intelligence techniques in order to provide information about home state normality and needed assistance. In the end, the system aims to intervene by providing guidance through its actuators. In this context, activity recognition becomes a key element in order to be able to provide adequate information services at the right moment. This thesis aims to contribute to this important challenge relating to activity recognition in the Smart Home designed for cognitive assistance. This contribution follows in the footsteps of temporal data mining and activity recognition approaches, and proposes a new way to automatically recognize and memorize ADLs from low-level sensors. From a formal point of view, the originality of the thesis relies on the proposition of a new unsupervised temporal datamining model for activity recognition addressing the problem of current temporal approaches based on Allen's framework. This new model incorporates some applications of fuzzy logic in order to take into account the uncertainty present in the realization of daily living activities by the resident. More specifically, we propose an extension of the fuzzy clustering technique in order to cluster the observations based on the degree of similarity between observations, so that activities are modeled and recognized. Moreover, anomaly recognition, decision making for assistance provision and judgment for simultaneous activities are some of the applicative contributions of this thesis. From a practical and experimental standpoint, the contribution of this research has been validated in order to evaluate how it would perform in a realistic context. To achieve this, we used MATLAB software as a simulation platform to test the proposed model. We then performed a series of tests which took the form of several case studies relating to common activities of daily living, in order to show the functionality and efficiency of the proposed temporal data-mining approach for real-life cases. This was especially relevant to the activity recognition application. We obtained very promising results which have been analyzed and compared to existing approaches. Finally, most parts of the contribution presented in this thesis have been published in documents ensuing from reputed international conferences (Springer LNCS proceedings [7], AAAI symposium and workshops [8, 9], MAICS [10], IEEE [11]) and a recognized journal (Springer Journal of Ambient Intelligence and Humanized Computing [12, 13]). This clearly constitutes recognition showing the potential of the proposed contribution

Herausforderungen des 'Mild Cognitive Impairment'-Konzepts: Die Beeinträchtigung von Aktivitäten des täglichen Lebens und die Klassifikation von Subtypen

Die vorliegende Arbeit untersuchte die Schwierigkeiten und Herausforderungen des Mild Cognitive Impairment-Konzepts. Hierbei wurde ein Schwerpunkt auf die Aktivitäten des täglichen Lebens (ADL) sowie auf die Klassifikation von MCI-Subtypen gelegt. Dadurch sollten Möglichkeiten zur Schärfung des MCI-Konzepts aufgezeigt und somit ein Beitrag zur Verbesserung der Früherkennung demenzieller Erkrankungen geleistet werden. In Studie 1 wurde der Forschungsstand zu Beeinträchtigungen der instrumentellen ADL (IADL) bei Personen mit MCI analysiert. Es zeigte sich, dass zum Teil ausgeprägte IADL-Defizite im MCI-Stadium existieren, vor allem in den Bereichen Finanzen, Telefonbenutzung, Umgang mit Medikamenten sowie Handhabung technischer Geräte. Amnestische MCI-Subtypen hatten größere IADL-Defizite als nicht-amnestische. Zudem waren leistungsbasierte Instrumente den Fragebogenverfahren leicht überlegen. Davon ausgehend wurde in Studie 2 ein neues leistungsbasiertes Verfahren zur Messung von IADL in einer Smart Home-Umgebung entwickelt und überprüft. Die MCI-Gruppe benötigte bei der Aufgabenbearbeitung insgesamt mehr Zeit und machte mehr Fehler als die kognitiv unbeeinträchtigte Gruppe. Sowohl Bearbeitungsdauer als auch Fehler bei der Aufgabenbearbeitung korrelierten moderat mit dem kognitiven Status der Probanden und auch mit traditionellen ADL-Maßen (Bayer-ADL, ADCS-MCI-ADL). Da sich in Studie 1 zeigte, dass ein großer Problempunkt des MCI-Konzepts in der fehlenden Operationalisierung der Kriterien liegt – sowohl hinsichtlich der IADL-Beeinträchtigungen als auch der kognitiven Defizite – wurde in Studie 3 eine datenbasierte Methode zur MCI-Subtypklassifikation erprobt. Dabei wurde eine 4-Cluster-Lösung ermittelt, die nicht ganz deckungsgleich mit den konventionellen Subtypen nach Petersen war. Der amnestische MCI-Subtyp zeigte das höchste Konversionsrisiko zur Alzheimer-Demenz, auch dann, wenn die kognitiven Defizite nur sehr leicht ausgeprägt waren. Die Befunde wurden durch Biomarker-Analysen unterstützt. Insgesamt konnten durch die vorliegende Arbeit Ansätze zur Verbesserung der MCI-Kriterien aufgezeigt werden. Zum einen sollten IADL, die besonders sensitiv bezüglich kognitiver Defizite sind, in den MCI-Kriterien genauer spezifiziert werden. Zudem sollten zur IADL-Erfassung eher leistungsbasierte Messverfahren eingesetzt werden. Zur präziseren Operationalisierung der Kriterien können datenbasierte Ansätze einen wertvollen Beitrag leisten

Early diagnosis of disorders based on behavioural shifts and biomedical signals

There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life. Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing. This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.Jendearen portaeran eragin zuzena duten gaixotasun ugari daude. Hala ere, askotan, gaixotasuna pairatzen duten pertsonak ez dira euren egoerataz ohartzen, eta familiarteko edo lankideek identifikatu ohi dute berau jokabide aldaketetaz ohartzean. Portaera aldaketa hauek nabarmentzean, ordea, beranduegi izan ohi da eta atzerazeinak diren kalteak eraginda egon ohi dira. Osasun kalte larriak eta gehiegizko kostuak ekiditeko eta gaixoen bizi kalitatea hobetzeko gakoa, gaixotasuna garaiz detektatzea da. Gaur egun, etengabe zabaltzen ari den Nonahiko Konputazioaren paradigmari esker, erabiltzaileen portaera ereduak era diskretu batean monitorizatu daitezke, gailu teknologikoekin izandako interakzioari esker. Eskuartean dugun arazoari konponbidea emateko teknologi hau erabiltzeak gaixotasunen sorrera eta aurrerapena nonahi eta era diskretu batean monitorizatzeko gai diren sistemak garatzea ekarriko luke, hauek garaiz hautematea ahalbidetuz. Gaixotasun konkretu batzuentzat soluzioak proposatu izan dira teknologi honetan oinarrituz, baina metodologia orokor bat, gaixotasun sorta zabal baten detekzio goiztiarrerako erabilgarria izango dena, oraindik ez da aurkeztu. Tesi honek hutsune hori betetzea du helburu, mota honetako gaixotasunak garaiz hautemateko, era diskretu batean atzitutako datu fisiologiko eta konportamentalen erabileran oinarritzen den behaketa sistema orokor bat proposatuz. Proposatutako metodologia bi balidazio egoera desberdinen arteko analisi gurutzatu baten emaitza da: estresa lantokian eta Alzheimerra etxean, balidazio egoera bakoitzari dagozkion ekarpenak ere ondorioztatu ahal izan direlarik. Bi kasuen arteko antzekotasun eta desberdintasunen analisiak, gaixotasun berri bat nonahiko konputazioan oinarrituta detektatzeko jarraitu beharreko pausoak bere osotasunean eta era orokor batean definitzea ahalbidetu du

Unsupervised monitoring of an elderly person\u27s activities of daily living using Kinect sensors and a power meter

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored. Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly. To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel. A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value. Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns. As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours. The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs

Efficient human situation recognition using Sequential Monte Carlo in discrete state spaces

This dissertation analyses these challenges and provides solutions for SMC methods. The large, categorical and causal state-space is the largest factor for the inefficiency of current SMC methods. The marginal filter is analysed in detail for its advantages in categorical states over the particle filter. An optimal pruning strategy for the marginal filter is derived that limits the number of samples.Diese Dissertation analysiert diese Herausforderungen und entwickelt Lösungen für SMC-Methoden. Der große, kategorische und kausale Zustandsraum ist der größte Faktor für die Ineffizienz von aktuellen SMC-Methoden. Die Vorteile des Marginalen Filters in kategorischen Zustandsräumen gegenüber dem Partikelfilter werden detailliert analysiert. Eine optimale Pruning-Strategie wird für den Marginal Filter entwickelt

Building Bridges, Blurring Boundaries: The Milwaukee School in Environment-Behavior Studies

Along with the 40th anniversary of the School of Architecture and Urban Planning at the University of Wisconsin-Milwaukee, the 30th anniversary of our Ph.D. Program in Architecture represents another important benchmark in educational excellence. As one of the pioneering PhD programs in architecture dedicated to understanding the relationship between people and place, its influence has been considerable. Its 51 (and counting) graduates teach in architectural design and allied fields at major institutes and practice throughout the world. Their intellectual contributions, and those of the faculty, continue to shape Environment-Behavior Studies and the discipline of architecture as a whole. This book, a tribute to the many excellent students who have shared the Milwaukee experience, is a testament to their collective input for the design of settings for health care, education, the workplace, older people, and communities, and their insights about the role well-designed environments contribute to the quality of people’s lives.https://dc.uwm.edu/sarup_facbooks/1000/thumbnail.jp

Goal reasoning for autonomous agents using automated planning

Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely a plan, which achieves a goal from a given initial state. Most planning research consider goals are provided by a external user, and agents just have to find a plan to achieve them. However, there exist many real world domains where agents should not only reason about their actions but also about their goals, generating new ones or changing them according to the perceived environment. In this thesis we aim at broadening the goal reasoning capabilities of planningbased agents, both when acting in isolation and when operating in the same environment as other agents. In single-agent settings, we firstly explore a special type of planning tasks where we aim at discovering states that fulfill certain cost-based requirements with respect to a given set of goals. By computing these states, agents are able to solve interesting tasks such as find escape plans that move agents in to safe places, hide their true goal to a potential observer, or anticipate dynamically arriving goals. We also show how learning the environment’s dynamics may help agents to solve some of these tasks. Experimental results show that these states can be quickly found in practice, making agents able to solve new planning tasks and helping them in solving some existing ones. In multi-agent settings, we study the automated generation of goals based on other agents’ behavior. We focus on competitive scenarios, where we are interested in computing counterplans that prevent opponents from achieving their goals. We frame these tasks as counterplanning, providing theoretical properties of the counterplans that solve them. We also show how agents can benefit from computing some of the states we propose in the single-agent setting to anticipate their opponent’s movements, thus increasing the odds of blocking them. Experimental results show how counterplans can be found in different environments ranging from competitive planning domains to real-time strategy games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert