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Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.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: The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain., New York, NY, USA,, 2024, p. -3Conference paper, Published paper (Refereed)
Abstract [en]

One of the main concerns regarding the facilitation of the elderly well-being monitoring system is to preserve the participants’ privacy, enable the older adults to live longer independently, and support caregivers. Human Activity Recognition (HAR) in smart homes allows us to foresee the residents’ needs by identifying changes in behaviour that might link to possible health conditions. We propose a Federated learning (FL) model within the health monitoring application to generalize for diverse participant populations and to achieve comparable performance without disclosing the raw data to the traditional centralized approach, which raises privacy issues. In this study, we evaluate an unsupervised variational autoencoder (VAE) in centralized, individualized, compared to federated learning settings to learn the features of normal patterns of daily activities and build an anomaly detector based on reconstructed error resulting as outcomes by the trained model. Further, we validated our proposed approach on real-world datasets collected over three years from six single-resident elderly households. The individual and centralized-based learning models were used as a baseline to compare with FL. Our results show that the personalized FedAvg models achieve RMSE of about 1%, while the Global FL models achieve RMSE of approximately. 4%. The centralized model achieves RMSE of about 0.5%, and the RMSE of individual models based on local training ranges between 1% to 6%. The FL models are relatively comparable to the centralized baseline model.

Place, publisher, year, edition, pages
New York, NY, USA,, 2024. p. -3
Series
The 39th ACM/SIGAPP Symposium On Applied Computing Avila, Spain, ISSN 979-8-4007-0243-3/24/04 ; 4
Keywords [en]
Applied computing → Health informatics; • Computing methodologies → Neural networks; Anomaly detection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ltu:diva-103826DOI: 10.1145/3605098.3636163ISBN: 979-8-4007-0243-3 (electronic)OAI: oai:DiVA.org:ltu-103826DiVA, id: diva2:1829359
Conference
The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.
Available from: 2024-01-18 Created: 2024-01-18 Last updated: 2024-02-08
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, ZahraaSaguna, SagunaÅhlund, Christer

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