Computational Sleep Behaviour Analysis and Application

Sleep affects a person’s health and is, therefore, assessed if health problems arise. Sleep behaviour is monitored for abnormalities in order to determine if any treatments, such as medication or behavioural changes (modifications to sleep habits), are necessary. Assessments are typically done using two methods: polysomnography over short periods and four-week retrospective questionnaires. These standard methods, however, cannot measure current sleep status continuously and unsupervised over long periods of time in the same way home-based sleep behaviour assessment can. In this work, we investigate the ability of sleep behaviour assessment using IoT devices in a natural home environment, which potential has not been investigated fully, to enable early abnormality detection and facilitate self-management. We developed a framework that incorporates different facets and perspectives to introduce focus and support in sleep behaviour assessment. The framework considers users’ needs, various available technologies, and factors that influence sleep behaviours. Sleep analysis approaches are incorporated to increase the reliability of the system. This assessment is strengthened by utilising sleep stage detection and sleep position recognition. This includes, first, the extraction and integration of influence factors of sleep stage recognition methods to create a fine-grained personalised approach and, second, the detection of common but more complex sleep positions, including leg positions. The relations between medical conditions and sleep are assessed through interviews with doctors and users on various topics, including treatment satisfaction and technology acceptance. The findings from these interviews led to the investigation of sleep behaviour as a diagnostic indicator. Changes in sleep behaviour are assessed alongside medical knowledge using data mining techniques to extract information about disease development; the following diseases were of interest: sleep apnoea, hypertension, diabetes, and chronic kidney disease. The proposed framework is designed in a way that allows it to be integrated into existing smart home environments. We believe that our framework provides promising building blocks for reliable sleep behaviour assessment by incorporating newly developed sleep analysis approaches. These approaches include a modular layered sleep behaviour assessment framework, a sleep regularity algorithm, a user-dependent visualisation concept, a higher-granularity sleep position analysis approach, a fine-grained sleep stage detection approach, a personalised sleep parameter extraction process, in-depth understanding on sleep and chronic disease relations, and a sleep-wake behaviour-based chronic disease detection method.This work has been supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 676157

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

The file attached to this record is the author's final peer reviewed version.Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recog-nition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status and race infor-mation which are known influence factors. Moreover, we developed a personal-ized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decision-making. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore, offers a new solution for personalized sleep-based health monitoring

Context, intelligence and interactions for personalized systems

This special issue on Context, Intelligence and Interactions for Personalized Systems provides a snapshot of the latest research activities, results, and technologies and application developments focusing on the smart personalised systems in Ambient Intelligence and Humanized Computing. It is intended for researchers and practitioners from artificial intelligence (AI) with expertise in formal modeling, representation and inference on situations, activities and goals; researchers from ubiquitous computing and embedded systems with expertise in context-aware computing; and application developers or users with expertise and experience in user requirements, system implementation and evaluation. The special issue also serves to motivate application scenarios from various domains including smart homes and cities, localisation tracking, image analysis and environmental monitoring. For solution developers and providers of specific application domains, this special issue will provide an opportunity to convey needs and requirements, as well as obtain first-hand information on the latest technologies, prototypes, and application exemplars

East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series

Academic geriatric medicine in Leicester . There has never been a better time to consider joining us. We have recently appointed a Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton, who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic opportunities to support students in their academic pursuits through a well-established intercalated BSc programme, and routes on through such as ACF posts, and a successful track-record in delivering higher degrees leading to ACL post. We collaborate strongly with Health Sciences, including academic primary care. See below for more detail on our existing academic set-up. Leicester Academy for the Study of Ageing We are also collaborating on a grander scale, through a joint academic venture focusing on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the local health service providers (acute and community), De Montfort University; University of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK. Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen Harrison Dening has also recently been appointed an Honorary Chair. LASA aims to improve outcomes for older people and those that care for them that takes a person-centred, whole system perspective. Our research will take a global perspective, but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland, including building capacity. We are undertaking applied, translational, interdisciplinary research, focused on older people, which will deliver research outcomes that address domains from: physical/medical; functional ability, cognitive/psychological; social or environmental factors. LASA also seeks to support commissioners and providers alike for advice on how to improve care for older people, whether by research, education or service delivery. Examples of recent research projects include: ‘Local History Café’ project specifically undertaking an evaluation on loneliness and social isolation; ‘Better Visits’ project focused on improving visiting for family members of people with dementia resident in care homes; and a study on health issues for older LGBT people in Leicester. Clinical Geriatric Medicine in Leicester We have developed a service which recognises the complexity of managing frail older people at the interface (acute care, emergency care and links with community services). There are presently 17 consultant geriatricians supported by existing multidisciplinary teams, including the largest complement of Advance Nurse Practitioners in the country. Together we deliver Comprehensive Geriatric Assessment to frail older people with urgent care needs in acute and community settings. The acute and emergency frailty units – Leicester Royal Infirmary This development aims at delivering Comprehensive Geriatric Assessment to frail older people in the acute setting. Patients are screened for frailty in the Emergency Department and then undergo a multidisciplinary assessment including a consultant geriatrician, before being triaged to the most appropriate setting. This might include admission to in-patient care in the acute or community setting, intermediate care (residential or home based), or occasionally other specialist care (e.g. cardiorespiratory). Our new emergency department is the county’s first frail friendly build and includes fantastic facilities aimed at promoting early recovering and reducing the risk of hospital associated harms. There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we have been examining geriatric outreach to oncology and surgery as part of an NIHR funded study. We are home to the Acute Frailty Network, and those interested in service developments at the national scale would be welcome to get involved. Orthogeriatrics There are now dedicated hip fracture wards and joint care with anaesthetists, orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone disease that run clinics. Community work Community work will consist of reviewing patients in clinic who have been triaged to return to the community setting following an acute assessment described above. Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will work closely with local GPs with support from consultants to deliver post-acute, subacute, intermediate and rehabilitation care services. Stroke Medicine 24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK and along with the high standard of vascular surgery locally means one of the best performances regarding carotid intervention

Human activity pattern recognition based on continuous data from a body worn sensor placed on the hand wrist using Hidden Markov models

Zsfassung in dt. SpracheDas Ziel dieser Arbeit ist kontinuierliche und diskrete Daten in einem Algorithmus zu vereinen um komplexe Aktivitäten-Muster zu erkennen,wie Zähne putzen,Zubereitung von Essen oder Hausarbeit.Diese Muster sind oft sehr komplex,da sie aus vielen Untermustern bestehen. Das Zubereiten von Essen zum Beispiel,besteht aus Untermustern wie 'Töpfe aus dem Schrank nehmen',Essen aus dem Kühlschrank nehmen',Schneiden',Kochen'und andere.Der schwierige Teil dieser Aufgabe ist,dass die Essenszubereitung sich nicht nur in der Reihenfolge der Untermuster unterscheidet,sondern auch in den unterschiedlichen Speisen die zubereitet werden.Man stelle sich den Unterschied zwischen der Vorbereitung eines Drei-Gänge-Menüs zu einem Frühstück vor.Diese beiden Aktivitäten unterscheiden sich enorm in der Vorbereitungsdauer und den Untermustern.Das Erkennungssystem welches innerhalb dieser Arbeit konstruiert wurde und menschliche Aktivitäten erkennt,kann mit dieser Art von Unterschieden umgehen. Das InvenSense MotionFit TMSoftware Development Kit(SDK)wird verwendet um die MPU-9150Sensordaten aufzuzeichnen.Der MPU-9150Sensor ist ein neun-achsiges MotionTracking Gerät,welches für diese Art von Anwendungen,wie in dieser Arbeit benötigt,optimiert wurde.Dieser tragbare Sensor kann Beschleunigungssensor-und Gyrometer-Daten senden,welche später herangezogen werden um menschliche Aktivitäten in realen Umgebungen zu erkennen. Die Frequenz der annotierten Daten ist in allen Experimenten auf50Hz gesetzt worden.Die aufgenommenen Aktivitäten sindHaare kämmen',Gesicht waschen',Hände waschen',Zähne putzen',Das Bett machen',Kleidung wechseln',Rollos rauf/runter ziehen',Essen zubereiten',Essen'undFenster schließen/öffnen'.Zwischen diesen Aktivitäten wird eineNULL'-Klasse durchgeführt,welche die Vorbereitung bzw.Nachbereitung der nächsten bzw.vorigen Aktivität beschreibt.Diese rohen Daten werden mithilfe eines verschiebbaren Fensters und verschiedener Features vorverarbeitet.In dieser Arbeit beträgt die Fensterlänge50,genauso wie die Frequenz beim Aufnehmen.Die Verschiebung des Fensters wird mit einer50%-Überlappung durchgeführt.Die verwendeten Features sind Mittelwert,Varianz,Korrelation und die auf schnelle Fourier-Transformation beruhenden Features,spektrale Entropie und Energie. Die Mustererkennung wird mit MATLAB und der von Murphyet al.zur Verfügung gestellten Toolbox PMTK3 [22]bewerkstelligt.Die verwendeten Klassifikations-Algorithmen sind supervised Lernmethoden,dies bedeutet,sie brauchen gelabelte Daten für das Training.Dieser Umstand wird von den Daten in dieser Arbeit erfüllt.Die Klassifikations-Algorithmen die während der Experimente verwendet werden sind einerseits kontinuierliche Hidden Markov Modelle(cHMM)und andererseits k-nächste-Nachbarn(kNN)Klassifikatoren.Die Klassifikationsmethoden werden im Detail beschrieben und ein Vergleich wird durchgeführt um Unterschiede zu erörtern.Die Experimente zeigen schlussendlich,dass das cHMM zu besseren Resultaten führt,im Gegensatz zu dem kNN Klassifikator. Die Erkennung von Alltagsaktivitäten funktioniert gut im Zusammenhang mit Ambient Assisted Living.Es kann gefolgert werden,dass cHMM die geeignetste Methode ist und zu den besten Ergebnissen führt.Verhältnismäßig ist der kNN Klassifikator viel schlechteraufgrund seiner einfachen Annahme.Deshalb ist der kNN Klassifikator nicht der beste Klassifikator,aber trotz seiner Einfachheit können annehmbare Ergebnisse erwartet werden. Nach der Validierung des Models sind verschiedene Features Kombinationen verglichen worden um die geeignetste Kombination zu finden.Andere Experimente konzentrieren sich auf die Verwendung von verschiedenen Training-und Test-Sets,die beste Anzahl von Sensoren,den Einfluss von Filtern,den Einfluss der Teilung von Aktivitäten und die Anwendung von diskreten und kontinuierlichen Daten.Die Experimente zeigen,dass die Bewegungssensor-Daten alleine,die besten Resultate liefert,während Filter und Teilung von Aktivitäten keine qualitative Verbesserung bringen. Die Kombination von diskreten und kontinuierlichen Daten verbessert die Resultate erheblich und führt zu verschiedenen Aktivitäten mit bester Genauigkeit und Trefferquote.Die Genauigkeit fürHände waschen'ist mit100%die beste Aktivität für kontinuierliche Daten undZähne putzen'100%für den kombinierten Fall.Essen'hat eine der besten Trefferquoten mit97.13%im kontinuierlichen Fall,wohingegen der kombinierte Fall eine Verbesserung nach sich zieht mit100%fürHände waschen',Das Bett machen',Rollos rauf/ runter ziehen',Essen zubereiten',Essen'undFenster öffnen/ schließen'.Im Allgemeinen liefert das System vertretbare Resultate sogar mit einem relativ kleinen Datensatz.The aim of the thesis is to combine discrete and continuous data in an algorithm to detect complex activity patterns such as tooth brushing, food preparation or household work. These patterns are of highly complex nature, meaning they consist of many sub-activities. Food preparation, for instance consists of sub-activities like 'take pans out of the cupboard','take food out of the refrigerator', 'cutting', 'cooking' and others. The complicated parts of this task are, that the food preparation not only differs in the order of the sub-activities, but also in the food which is prepared. Envision the preparation of a three-course menu in comparison to preparing a breakfast. This two activities differ a lot in needed duration and sub-activities. The human activity recognition system built in this thesis can handle these differences. The InvenSense MotionFit TM Software Development Kit (SDK) is used to record data with the MPU-9150 sensor. The MPU-9150 sensor is a nine-axis MotionTracking device, which is optimized for those kind of applications in this thesis and is normally used in mobile devices.[20] The wearable sensor MPU-9150 is able to send accelerometer and gyrometer data, which are later used to detect human activities in real environments. The frequency of the annotated data is 50Hz in all experiments. The processed activities are 'Comb hair','Wash face', 'Wash hands', 'Tooth brushing', 'Make bed', 'Change clothes', 'Put roller blinds up/down', 'Prepare food', 'Eat' and 'Open/close window'. Inbetween this activities a 'NULL'-class is performed, which describes the preparation for the next activity or the closure of the previous activity. This raw data are preprocessed via a shifted window and different features. In this thesis the window length is set to 50, equal the sampling frequency and the shift is accomplished with an 50% overlap. The used features are mean, variance, correlation and fast Fourier transformation based features. The fast Fourier transformation bases features are spectral entropy and energy. The pattern recognition is done in MATLAB using the PMTK3 toolbox from Murphy et al. [22] with real annotated data. The used classification algorithms are from supervised learning structure, meaning that they need labeled data for training. This circumstances are fulfilled for the data used in this thesis. The classification algorithms that are used during the experimentation are continuous Hidden Markov Model (cHMM) and k-nearest-neighbors (kNN) classification. The classification methods are described in detail and a comparison is done to discuss the differences between the results. In the end the cHMM leads to the more accurate outcomes in comparison to the kNN classifier. Daily activity detection works well in the context of ambient assisted living. It can be concluded that, the cHMM is the most proper method and comes to the best solutions. In contrast the kNN classification is much worse, because of its simplifying assumption. Due to that the kNN classifier is not the best classifier to use, but for the simpleness, acceptable results can be expected. After validation of the model the different features combinations are compared to each other to find the most suitable combination. Other experiments focus on different training and test sets, the best number of sensors, the impact of filters, the impact of activity division and the application of discrete and continuous data. The experiments show that the accelerometer data on their own lead to one of the best results, whereas the filters as well as activity division do not lead to a qualitative improvement. The combination of discrete and continuous data improves the results a lot and leads to different activities with highest recall and precision. The precision for 'Wash hands' is with a value of 100% the best in the continuous data case and 'Tooth brushing' in the combined case, also with 100%. 'Eat' has one of the best recall values with 97.13% in the continuous case, whereas the improvement in the combined case can be seen on the recall value 100% for 'Wash hands', 'Make bed', 'Put blinds up/down', 'Prepare food', 'Eat' and 'Open/close window'. Overall the system leads to reasonable outputs even with a relative small dataset.5

A Home-Based IoT-Enabled Framework for Sleep Behaviour Assessment

Sleep has an impact on a person's life including their health and wellbeing, thus assessing sleep behaviour is of high importance to gain insight into people's general health status. In general, current sleep behaviour assessment is restricted to a controlled scenario within a hospital environment limited by the time of monitoring and to specific factors. As healthcare is shifting from reactive to preventive and predictive care with the support of digital health and IoT technology, there is a growing demand to make sleep assessment possible at home. In this paper, we propose a sleep behaviour assessment framework considering different facets of sleep such as sleep quality, regularity, circadian rhythm, environmental conditions and sleep hygiene. Hence, we describe methodologies and techniques which can help realise home-based sleep assessment. A salient feature of the framework is that it takes into account personal preferences and influential factors as well as doctor's recommendations and clinical history, thus, allowing personalised medical and behavioural assessment. In addition, the proposed framework supports a modular service-oriented design adaptable to both doctor and user needs and availability of underpinning technologies

Fine-Grained Sleep-Wake Behaviour Analysis

Sleep stages are traditionally assessed by experts from polysomnography measurements following specific guidelines. Sleep stage behaviour is subsequently used to detect anomalies and diagnose diseases in a laboratory setting. Recently, with the development of Internet of Things, there is a trend to use everyday technologies for sleep behaviour analysis at home, having the potential to eliminate expensive in-hospital monitoring. We propose a fine-grained sleep-wake behaviour analysis approach, which takes into consideration the influences of various factors, such as gender, health status and race. In addition, we investigate the combination of multiple data sources, in particular, actigraphy and heart rate variability, for enhancing model accuracy. Initial results show the proposed approach is recognising sleep and wake stages accurately and is providing a flexible recognition approach towards personalised sleep-based health monitoring

Toward a Systematic Assessment of Sex Differences in Cystic Fibrosis

(1) Background: Cystic fibrosis (CF) is a disease with well-documented clinical differences between female and male patients. However, this gender gap is very poorly studied at the molecular level. (2) Methods: Expression differences in whole blood transcriptomics between female and male CF patients are analyzed in order to determine the pathways related to sex-biased genes and assess their potential influence on sex-specific effects in CF patients. (3) Results: We identify sex-biased genes in female and male CF patients and provide explanations for some sex-specific differences at the molecular level. (4) Conclusion: Genes in key pathways associated with CF are differentially expressed between sexes, and thus may account for the gender gap in morbidity and mortality in CF

Towards Self-management of Chronic Diseases in Smart Homes : Physical Exercise Monitoring for Chronic Obstruction Pulmonary Disease Patients

International audienc