Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

Automatic detection of disorientation among people with dementia

Ageing is characterized by decline in cognition including visuospatial function, necessary for independently executing instrumental activities of daily living. The onset of Alzheimer’s disease dementia exacerbates this decline, leading to major challenges for patients and increased burden for caregivers. An important function affected by this decline is spatial orientation. This work provides insight into substrates of real-world wayfinding challenges among older adults, with emphasis on viable features aiding the detection of spatial disorientation and design of possible interventions

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

A study of virtual reality-mediated affective state and cognitive decline in Alzheimer’s disease

NeuroscienceLa démence de type d’Alzheimer est la plus commune des démences. Elle entraîne un déclin dans les capacités cognitives et fonctionnelles, se traduisant dans des difficultés au niveau de la prise de décision, de l’accomplissement de tâches quotidiennes, de la communication ainsi qu’au niveau de la mémoire et de l’attention. On remarque également une diminution de l’état émotionnel et une apathie chez ces patients. Ce mémoire explore une nouvelle approche pour atténuer les effets psychologiques et cognitifs de la maladie. Les recherches effectuées dans ce mémoire explorent les impacts cognitifs et les effets sur le bien-être d'une intervention utilisant la réalité virtuelle sur les personnes souffrant de déclin cognitif subjectif. Deux environnements virtuels ont été testés : le premier étant un environnement dans lequel le participant voyage en train à travers différents climats, et le second étant un environnement de musicothérapie qui s’adapte en fonction de la réponse émotionnelle du participant. Pour mesurer les impacts sur l'état affectif, des lectures électroencéphalographiques ont été prises et analysées afin de déduire l'émotion ressentie par le participant avant, pendant et après l'expérience. Les résultats montrent une amélioration générale de l'état émotionnel pour les deux environnements. Quant à la mesure des effets sur les fonctions cognitives, des tâches d'attention et de mémoire ont été effectuées par les participants avant et après l'immersion. Les résultats montrent une légère amélioration des capacités d'attention et une meilleure amélioration de la mémoire. Nous approprions cet écart dans l'expérience de musicothérapie à l'activation musicale d'un réseau de structures cérébrales impliquées dans les expériences agréables : le circuit de récompense. Nous proposons que la musique facilite la rétention de la mémoire chez les personnes souffrant de démence. En effet, les résultats de l’amélioration des fonctions cognitives pour les deux expériences précédentes dépendent fortement de la précision de l'outil de mesure cognitive utilisé pour évaluer les performances d'attention et de mémoire avant et après l'intervention. Pour assurer cette précision, ce mémoire présente un outil de mesure des performances cognitives basé sur des tâches cognitives qui ont montré à plusieurs reprises leur fiabilité. Cet outil d’adresse aux personnes atteintes de la maladie d'Alzheimer pré-clinique et diagnostiquée.Alzheimer’s disease is an irreversible disease which causes progressive memory loss and cognitive decline, eventually leading to severe inability to perform basic day-to-day tasks. The urgency to find an effective cure to the disease is crucial, as the medical and economical spin-offs could be disastrous. The present thesis explores a novel approach to help attenuate the psychological and cognitive effects of the disease. The research carried out for this thesis explored cognitive effects and impacts on overall well-being of a virtual reality intervention on people suffering from subjective cognitive decline. Two virtual environments were tested: the first being an environment in which the participant travels through different climates by train, and the second being a music therapy environment modified as a function of emotional response. To measure the effects on affective state, electroencephalography readings were taken and analyzed to infer the emotion felt by the participant before, during the experiment. Results show a general improvement in emotional state. To measure the effects of the environments on cognitive functions, attention and memory tasks were carried out by the participants before and after the immersion. Results show a small improvement in attention skills and a more substantial improvement in memory skills. We appropriate this discrepancy in the music therapy experiment to the musical activation of a network of brain structures involved in rewarding and pleasurable experiences. We propose that music could facilitate memory retention in people sufferance for dementia. Importantly, the results of the previous experiments rely heavily on the accuracy of the cognitive measurement tool used to evaluate attention and memory performances before and after the intervention. To provide this accuracy, this thesis presents a cognitive performance measurement tool based on cognitive tasks which have repeatedly shown to output reliable results. This tool is created to serve for people with pre-clinical Alzheimer’s disease and diagnosed Alzheimer’s disease. Additionally, this tool is designed in such a way as to minimize the effects of repetition as well as varying levels of education and language. This thesis presents a novel and promising research in the realms of computer sciences and health care

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scienc

Proceedings of the 9th international conference on disability, virtual reality and associated technologies (ICDVRAT 2012)

The proceedings of the conferenc