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Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Skellefteå Municipality, Sweden.ORCID iD: 0000-0002-5704-4667
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
2024 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 11, no 3, p. 4414-4429Article in journal (Refereed) Published
Abstract [en]

Sleep is an essential activity that affects an individual’s health and ability to perform Activities of Daily Living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and non-intrusive environments. This paper presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using Multi-Armed Bandit methods to (i) learn normal sleep patterns, (ii) evaluate sleep quality, and (iii) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson Sampling, Random Selection, and Upper Confidence Bound MAB methods. Thompson Sampling outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 hours with 85% sleep efficiency and up to 3 awakenings per night.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 11, no 3, p. 4414-4429
Keywords [en]
Anomaly detection, Elderly healthcare, Internet of Things, Medical services, Motion detection, Multi-Armed bandits, Older adults, Reinforcement learning, Sensors, Sleep, Sleep patterns, Smart homes
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-99666DOI: 10.1109/JIOT.2023.3300015Scopus ID: 2-s2.0-85166780685OAI: oai:DiVA.org:ltu-99666DiVA, id: diva2:1787868
Projects
FraViVo—Framtidens Välfärdsteknik med Internet of Things
Funder
Vinnova, 2020-04096
Note

Validerad;2024;Nivå 2;2024-03-18 (hanlid);

Full text license: CC BY-NC-ND

Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2024-03-18Bibliographically approved
In thesis
1. Human Behaviour Recognition of Elderly in Single-Resident IoT Enabled Smart Homes: An Applied Machine Learning Approach
Open this publication in new window or tab >>Human Behaviour Recognition of Elderly in Single-Resident IoT Enabled Smart Homes: An Applied Machine Learning Approach
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In Human Activity Recognition (HAR) systems, activities of daily living/human behaviour are recognized using sensor data by applying data mining techniques and machine learning algorithms to the collected data. This allows for customised and automated services to support humans’ daily living. Along with recognizing human behaviour, HAR can provide insights into the abnormal behaviour of individuals that might link to possible health conditions. Health monitoring applications are widely used for patients with chronic diseases, especially to give feedback to the users and promote self-awareness, for example, persons with dementia who need constant monitoring to minimize the risk of undesirable events. In developing HAR applications and services along with anomaly detection, obstacles and challenges remain that need to be addressed and motivate our research focusing on older adults living in real-world conditions. The need for care has increased, especially with the demographic developments and growing population of 65 years and over. Some of the challenges identified are installing IoT devices and sensors, collecting data and maintaining the system in an uncontrolled environment, and identifying valuable features to extract from sensors while ensuring the development of a non-intrusive and privacy-preserving system. Our research uses smart home sensors to infer ADLs (e.g., eating, sleeping, and bathroom visits) for older adults in single-resident homes. The research aims to develop and validate an anomaly detection based IoT system within HAR to support older adults in living longer independently and for the relatives (caregivers) to provide the necessary support. In addition, the system will help address the challenges of the ageing population and the increased burden on healthcare resources. This thesis identifies technologies and datasets suitable for IoT-enabled smart healthcare applications to recognize near real-time and short- and long-term behavioural changes.

We propose to develop a life conditions model for each individual by understanding the routines and activities of daily living based on interviews with older adults and their caregivers and collecting datasets with a focus on motion sensors. The developed life condition model for each individual is used to recognize human behaviour (based on context information such as time and location) and to analyze overall behavioural change. Hence, we develop and build an anomaly detection system that preserves privacy for near real-time behavioural changes and supports large-scale deployment. Our research methodology follows a quantitative research methodology as well as a qualitative approach based on interviews. We defined activity models based on contextual information such as time and location to extract features suitable for inferring daily living activities, model behavioural patterns, and, after that, detect abnormal activities in each daily routine by utilizing motion sensors as suitable sensing devices for non-intrusive IoT enabled smart healthcare applications in single-resident homes. For example, we applied a statistical method to build a routine or habit model for each older adult. We utilized unsupervised clustering methods K-means and LOF and reinforcement learning sleeping to recognise sleep activity patterns and detect anomalies. Further, to recognise all variations of the person’s behaviour and detect short and long-term behavioural changes in older people’s daily behaviour, we applied LSTM and VAE algorithms. In addition, we developed an anomaly detection system that preserves privacy to support large-scale deployments by utilizing federated learning to build a generalizable model that learns from different persons models.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024. p. 115
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103840 (URN)978-91-8048-470-1 (ISBN)978-91-8048-471-8 (ISBN)
Public defence
2024-03-18, A193, Luleå University of Technology, Skellefteå, 09:00 (English)
Opponent
Supervisors
Available from: 2024-01-19 Created: 2024-01-19 Last updated: 2024-03-13Bibliographically approved

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Shahid, Zahraa KhaisSaguna, SagunaÅhlund, Christer

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