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Objective: The NEX project has developed an integrated Internet of Things (IoT) system coupled with data analytics to offer unobtrusive health and wellness monitoring supporting older adults living independently at home. Monitoring {currently} involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. The detection of ADLs is achieved {} to allow the incorporation of additional participants whose ADLs are detected without re-training the system. Methods: Following an extensive User Needs and Requirements study involving 426 participants, a pilot trial and a friendly trial of the deployment, an Action Research Cycle (ARC) trial was completed. This involved 23 participants over a 10-week period each with c.20 IoT sensors in their homes. During the ARC trial, participants each took part in two data-informed briefings which presented visualisations of their own in-home activities. The briefings also gathered training data on the accuracy of detected activities. Association rule mining was then used on the combination of data from sensors and participant feedback to improve the automatic detection of ADLs. Results: Association rule mining was used to detect a range of ADLs for each participant independently of others and was then used to detect ADLs across participants using a single set of rules {for each ADL}. This allows additional participants to be added without the necessity of them providing training data. Conclusions: Additional participants can be added to the NEX system without the necessity to re-train the system for automatic detection of the set of their activities of daily living., 11 pages, 7 Figures, 2 tables

Background: In a rapidly ageing population new and efficient ways of providing health and social support to older adults are required that not only preserve independence but also maintain quality of life and safety. Objective: The NEX project aims to develop an Internet of Things integrated system coupled with Artificial Intelligence to offer unobtrusive health and wellness monitoring to support older adults to live independently in their home environment. The primary objective of this study is to develop and evaluate the technical performance and user acceptability of “The NEX system”. The secondary objective is to apply machine learning algorithms to the data collected via the NEX system to identify and eventually predict changes in the routines of older adults in their own home environment. Methods: Mixed methods research (online survey and focus groups) was conducted with 426 participants including older adults (aged 60 and above), family caregivers, health care professionals and home care workers to inform the development of the NEX system (Phase 1). The primary outcome will be evaluated in two successive trials (the Friendly Trial (Phase 2) and the Action Research Cycle trial (Phase 3). The secondary objective will be explored in the Action Research trial (Phase 3). For the Friendly Trial, 7 older adult participants aged 60 years and above and living alone in their own homes for a 10-week period were enrolled in the trial. Thirty older adult participants aged 60 years and above and living alone in their own homes will be recruited for the Action Research trial for a 10-week period (Phase 3). Results: Phase 1 of the project (n=426) participants was completed in December 2020 and Phase 2 (n=7 participants for a 10-week pilot study) was completed in September 2021. The expected completion date for the third project phase (30 participants for 10-week usability study) is June 2022. Conclusions: The NEX project has considered the specific everyday needs of older adults and other stakeholders which have contributed to the design of the integrated system. The innovation of the NEX system lies in the use of IoT technologies and AI to identify and predict changes in the routines of older adults. The findings of this overall project will contribute to the e-Health research agenda focusing on the improvement of healthcare provision and patient support in home and community environments.