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Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Information Technology Department, Skellefteå Municipality, Skellefteå, SE.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
2022 (English)In: JMIR Aging, E-ISSN 2561-7605, Vol. 5, no 2, article id e28260Article in journal (Refereed) Published
Abstract [en]

Background: One of the main challenges of health monitoring systems is the support of older persons in living independently in their homes and with relatives. Smart homes equipped with internet of things devices can allow older persons to live longer in their homes. Previous surveys used to identify sensor-based data sets in human activity recognition systems have been limited by the use of public data set characteristics, data collected in a controlled environment, and a limited number of older participants.

Objective: The objective of our study is to build a model that can learn the daily routines of older persons, detect deviations in daily living behavior, and notify these anomalies in near real-time to relatives.

Methods: We extracted features from large-scale sensor data by calculating the time duration and frequency of visits. Anomalies were detected using a parametric statistical approach, unusually short or long durations being detected by estimating the mean (μ) and standard deviation (σ) over hourly time windows (80 to 355 days) for different apartments. The confidence level is at least 75% of the tested values within two (σ) from the mean. An anomaly was triggered where the actual duration was outside the limits of 2 standard deviations (μ−2σ, μ+2σ), activity nonoccurrence, or absence of activity.

Results: The patterns detected from sensor data matched the routines self-reported by users. Our system observed approximately 1000 meals and bathroom activities and notifications sent to 9 apartments between July and August 2020. A service evaluation of received notifications showed a positive user experience, an average score of 4 being received on a 1 to 5 Likert-like scale. One was poor, two fair, three good, four very good, and five excellent. Our approach considered more than 75% of the observed meal activities were normal. This figure, in reality, was 93%, normal observed meal activities of all participants falling within 2 standard deviations of the mean.

Conclusions: In this research, we developed, implemented, and evaluated a real-time monitoring system of older participants in an uncontrolled environment, with off-the-shelf sensors and internet of things devices being used in the homes of older persons. We also developed an SMS-based notification service and conducted user evaluations. This service acts as an extension of the health/social care services operated by the municipality of Skellefteå provided to older persons and relatives.

Place, publisher, year, edition, pages
JMIR Publications , 2022. Vol. 5, no 2, article id e28260
Keywords [en]
Activities of daily living, smart homes, elderly care, anomaly detection, IoT devices, smart device, elderly, sensors, digital sensors, Internet of things
National Category
Nursing
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-90264DOI: 10.2196/28260ISI: 000798395200002PubMedID: 35404260Scopus ID: 2-s2.0-85128460394OAI: oai:DiVA.org:ltu-90264DiVA, id: diva2:1652821
Note

Validerad;2022;Nivå 1;2022-04-20 (joosat);

A correction is available for this publication, please see: Shahid ZK., Saguna S., Åhlund C. Correction: Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study. JMIR Aging 7, (2024). DOI: 10.2196/58394

Available from: 2022-04-20 Created: 2022-04-20 Last updated: 2024-05-20Bibliographically 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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