Smart home in a box: usability study for a large scale self-installation of smart home technologies

Hu, Yang; Tilke, Dominique; Adams, Taylor; Crandall, Aaron S.; Cook, Diane J.; Schmitter‐Edgecombe, Maureen

doi:10.1007/s40860-016-0021-y

Cited by 48 publications

(29 citation statements)

References 39 publications

(37 reference statements)

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“…The evaluation results of elderly people who have installed SHiB kits in their homes by themselves [ 52 ] have generally been favorable. This indicates that it is possible to introduce such kits into ordinary homes that are occupied by elderly people.…”

Section: Related Workmentioning

confidence: 99%

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

Matsui

Onishi

Misaki

et al. 2020

Sensors

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As aging populations continue to grow, primarily in developed countries, there are increasing demands for the system that monitors the activities of elderly people while continuing to allow them to pursue their individual, healthy, and independent lifestyles. Therefore, it is required to develop the activity of daily living (ADL) sensing systems that are based on high-performance sensors and information technologies. However, most of the systems that have been proposed to date have only been investigated and/or evaluated in experimental environments. When considering the spread of such systems to typical homes inhabited by elderly people, it is clear that such sensing systems will need to meet the following five requirements: (1) be inexpensive; (2) provide robustness; (3) protect privacy; (4) be maintenance-free; and, (5) work with a simple user interface. In this paper, we propose a novel senior-friendly ADL sensing system that can fulfill these requirements. More specifically, we achieve an easy collection of ADL data from elderly people while using a proposed system that consists of a small number of inexpensive energy harvesting sensors and simple annotation buttons, without the need for privacy-invasive cameras or microphones. In order to evaluate the practicality of our proposed system, we installed it in ten typical homes with elderly residents and collected the ADL data over a two-month period. We then visualized the collected data and performed activity recognition using a long short-term memory (LSTM) model. From the collected results, we confirmed that our proposed system, which is inexpensive and non-invasive, can correctly collect resident ADL data and could recognize activities from the collected data with a high recall rate of 72.3% on average. This result shows a high potential of our proposed system for application to services for elderly people.

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Section: Related Workmentioning

confidence: 99%

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

Matsui

Onishi

Misaki

et al. 2020

Sensors

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“…More recent examples leverage advances in sensor technology, AI and machine learning to support cooking [31], dressing [32] and reduce demands for caregiving [29]. In addition, smart home technologies are becoming available for individuals to install themselves [33], creating huge potential for conducting scalable clinical trials at home [34,35].…”

Section: Maintenance Of Functionmentioning

confidence: 99%

Technology and Dementia: The Future Is Now

Astell¹

2019

Dement Geriatr Cogn Disord

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Background: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. Objectives: To summarise key areas of technology development in dementia and identify future directions and implications. Method: Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. Results: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.

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“…[4,6]). The Centre for Advanced Studies in Adaptive Systems (CASAS) project, based at Washington State University, has instrumented and published data for more than 50 smart environments (homes and offices) using a re-usable methodology [20]. The CASAS research group focus on many aspects of Activity Recognition (AR) in smart environments, and provide a number of annotated datasets 1 .…”

Section: Related Workmentioning

confidence: 99%

Releasing eHealth Analytics into the Wild

Diethe

Holmes

Kull

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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The SPHERE project is devoted to advancing eHealth in a smarthome context, and supports full-scale sensing and data analysis to enable a generic healthcare service. We describe, from a datascience perspective, our experience of taking the system out of the laboratory into more than thirty homes in Bristol, UK. We describe the infrastructure and processes that had to be developed along the way, describe how we train and deploy Machine Learning systems in this context, and give a realistic appraisal of the state of the deployed systems. CCS CONCEPTS • Computing methodologies → Machine learning; • Applied computing → Health informatics; • Hardware → Sensor applications and deployments; • Information systems → Sensor networks; Data streaming; • Social and professional topics → Remote medicine;

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Smart home in a box: usability study for a large scale self-installation of smart home technologies

Cited by 48 publications

References 39 publications

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

Technology and Dementia: The Future Is Now

Releasing eHealth Analytics into the Wild

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