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

A longitudinal observational study of home-based conversations for detecting early dementia:protocol for the CUBOId TV task

INTRODUCTION: Limitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the ‘TV task’, designed to track changes in ecologically valid conversations with disease progression. METHODS AND ANALYSIS: CUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 8–25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone. ETHICS AND DISSEMINATION: CUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

INCF Lithuanian Workshop on Neuroscience and Information Technology

The aim of this workshop was to give a current overview of neuroscience and informatics research in Lithuania, and to discuss the strategies for forming the Lithuanian Neuroinformatics Node and becoming a member of INCF. The workshop was organized by Dr. Aušra Saudargiene (Department of Informatics, Vytautas Magnus University, Kaunas, and Faculty of Natural Sciences, Vilnius University, Lithuania) and INCF.
The workshop was attended by 15 invited speakers, among them 4 guests and 11 Lithuanian neuroscientists, and over 20 participants. The workshop was organized into three main sessions: overview of the INCF activities including the Swedish and UK nodes of INCF; presentations on Neuroscience research carried out in Lithuania; discussion about the strategies for forming an INCF national node, and the benefits of having such a node in Lithuania (Appendix A: Program; Appendix B: Abstracts)

A rare duplication on chromosome 16p11.2 is identified in patients with psychosis in Alzheimer's disease

Epidemiological and genetic studies suggest that schizophrenia and autism may share genetic links. Besides common single nucleotide polymorphisms, recent data suggest that some rare copy number variants (CNVs) are risk factors for both disorders. Because we have previously found that schizophrenia and psychosis in Alzheimer's disease (AD+P) share some genetic risk, we investigated whether CNVs reported in schizophrenia and autism are also linked to AD+P. We searched for CNVs associated with AD+P in 7 recurrent CNV regions that have been previously identified across autism and schizophrenia, using the Illumina HumanOmni1-Quad BeadChip. A chromosome 16p11.2 duplication CNV (chr16: 29,554,843-30,105,652) was identified in 2 of 440 AD+P subjects, but not in 136 AD subjects without psychosis, or in 593 AD subjects with intermediate psychosis status, or in 855 non-AD individuals. The frequency of this duplication CNV in AD+P (0.46%) was similar to that reported previously in schizophrenia (0.46%). This duplication CNV was further validated using the NanoString nCounter CNV Custom CodeSets. The 16p11.2 duplication has been associated with developmental delay, intellectual disability, behavioral problems, autism, schizophrenia (SCZ), and bipolar disorder. These two AD+P patients had no personal of, nor any identified family history of, SCZ, bipolar disorder and autism. To the best of our knowledge, our case report is the first suggestion that 16p11.2 duplication is also linked to AD+P. Although rare, this CNV may have an important role in the development of psychosis

Digital Oculomotor Biomarkers in Dementia

Dementia is an umbrella term that covers a number of neurodegenerative syndromes featuring gradual disturbance of various cognitive functions that are severe enough to interfere with tasks of daily life. The diagnosis of dementia occurs frequently when pathological changes have been developing for years, symptoms of cognitive impairment are evident and the quality of life of the patients has already been deteriorated significantly. Although brain imaging and fluid biomarkers allow the monitoring of disease progression in vivo, they are expensive, invasive and not necessarily diagnostic in isolation. Recent studies suggest that eye-tracking technology is an innovative tool that holds promise for accelerating early detection of the disease, as well as, supporting the development of strategies that minimise impairment during every day activities. However, the optimal methods for quantitative evaluation of oculomotor behaviour during complex and naturalistic tasks in dementia have yet to be determined. This thesis investigates the development of computational tools and techniques to analyse eye movements of dementia patients and healthy controls under naturalistic and less constrained scenarios to identify novel digital oculomotor biomarkers. Three key contributions are made. First, the evaluation of the role of environment during navigation in patients with typical Alzheimer disease and Posterior Cortical Atrophy compared to a control group using a combination of eye movement and egocentric video analysis. Secondly, the development of a novel method of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test to detect oculomotor biomarkers of dementia-related cognitive dysfunction. Third, the application of unsupervised anomaly detection techniques for visualisation of oculomotor anomalies during various cognitive tasks. The work presented in this thesis furthers our understanding of dementia-related oculomotor dysfunction and gives future research direction for the development of computerised cognitive tests and ecological interventions

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

Activity-driven detection of cognitive impairment using deep learning.

While life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Cognitive impairment is a collective name for progressive brain syndromes which affect memory, cognition, behaviour and emotion. People suffering from cognitive impairment may lose their abilities to perform daily life activities and they get dependent on their caregivers. Although some medications can slow the progress of the disease, currently there is no way to stop its development. Sufferers may require special needs which increase the cost of care. Thus, detecting the indicators of cognitive decline before it gets worse would be very crucial. Current assessment methods mostly rely on queries from questionnaires or in-person examinations, which depend on recall of events that may poorly represent a person’s typical state. The aim in this thesis is to adapt deep learning techniques for analysing daily activities of elderly people and detecting abnormalities in the activity patterns. Recent studies suggest that indicators of cognitive decline can be observed in daily life activity patterns. The spatio-temporal and hierarchical relationship of activities and their intrinsic structures are important in the context of cognitive decline analysis. Existing studies treat each activity as an atomic unit and fail to capture the relationship among sub-activities. Also, existing studies rely on fixed length features to model activities, ignoring the granular level information coming from raw sensor activations. Moreover, there exists no daily activity dataset representing the behaviour of dementia sufferers because producing such datasets requires time and adequate experimental environment. Given these challenges, the present thesis addresses the following research questions: How can we cope with the scarcity of dataset reflecting on cognitive status of elderly people? How can activities be modelled taking into account their spatio-temporal neighbourhood and hierarchical information? How can we represent raw data to encode the granular level details? These research questions are addressed in the following way. Firstly, two methods are proposed to cope with the scarcity of data: (i) synthetic data generation and (ii) transfer learning adoption. Secondly, the activity recognition problem is emulated (i) as a sequence labelling problem to model spatio-temporal patterns. (ii) as a hierarchical learning problem to model sub-activities. (iii) as a graph labelling problem to encode granular level details. Thirdly, raw sensor measurements stemming from sequential data are used to model sensor activation relationships. The proposed methods are also compared against the state-of-art methods. The preliminary results obtained indicate that pro- posed data simulation and transfer learning approaches are useful to cope with the scarcity of data reflecting cognitive status of elderly people. Moreover, experiments show that the proposed deep learning methods are promising to detect abnormalities in the context of cognitive decline. Proposed methods are not only promising to detect abnormal behaviour at a fine-grained level, but some of them can also model activities hierarchically by taking sub-activities into account and then can detect abnormal behaviour occurring at granular levels