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Forecasting and Detecting Anomalies in ADLs in Single-Resident Elderly Smart Homes
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-5704-4667
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0001-8561-7963
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-8681-9572
2023 (engelsk)Inngår i: RACS '23: Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems, New York: Association for Computing Machinery , 2023, artikkel-id 20Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

As the ageing population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable the detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we developed and evaluated a Multivariate long short-term memory (LSTM) to learn to forecast activities of daily living and detect anomalous behaviour using motion sensor data in 6 single-resident smart homes of the elderly. We use Mahalanobis distance to identify anomalies based on distance scores to build thresholds. The model's performance in terms of NMAE error values ranges between 2\% and 6\%. The experimental results show that the performance of LSTM for predicting the direct next activity versus the multiple forecasts is close. The method could identify participants' changing health conditions through the used predictive model and unsupervised anomaly detection method.

sted, utgiver, år, opplag, sider
New York: Association for Computing Machinery , 2023. artikkel-id 20
Serie
ACM Conferences
Emneord [en]
Unsupervised learning, Predictive health analytics, Multivariate-LSTM, IoTs, Health/wellbeing applications, HAR, Forecasting, Anomaly Detection
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-103819DOI: 10.1145/3599957.3606216Scopus ID: 2-s2.0-85174221774ISBN: 979-8-4007-0228-0 (tryckt)OAI: oai:DiVA.org:ltu-103819DiVA, id: diva2:1829277
Konferanse
2023 International Conference on Research in Adaptive and Convergent Systems, RACS 2023, Gdansk, Poland, August 6 - 10, 2023
Merknad

Full text: CC BY 4.0 License

Tilgjengelig fra: 2024-01-18 Laget: 2024-01-18 Sist oppdatert: 2024-02-08bibliografisk kontrollert
Inngår i avhandling
1. Human Behaviour Recognition of Elderly in Single-Resident IoT Enabled Smart Homes: An Applied Machine Learning Approach
Åpne denne publikasjonen i ny fane eller vindu >>Human Behaviour Recognition of Elderly in Single-Resident IoT Enabled Smart Homes: An Applied Machine Learning Approach
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2024. s. 115
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-103840 (URN)978-91-8048-470-1 (ISBN)978-91-8048-471-8 (ISBN)
Disputas
2024-03-18, A193, Luleå University of Technology, Skellefteå, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2024-01-19 Laget: 2024-01-19 Sist oppdatert: 2024-03-13bibliografisk kontrollert

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Shahid, Zahraa KhaisSaguna, SagunaÅhlund, Christer

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