Robotics enabled in-home environment screening for fall risks

Du, Ruixiang; Jagtap, Vinayak; Long, Yanren; Onwuka, Oke; Padır, Taşkın

doi:10.1145/2609829.2609836

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…In these studies, patients perform physical activities and cognitively demanding tasks to determine fall risks. Du et al [ 64 ] developed a robotic system that screens the subjects’ home, for environmental fall hazards. This system is operated remotely by clinicians, to automate home assessments that are typically conducted by in-person visits.…”

Section: Fall Prevention Systemsmentioning

confidence: 99%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Rajagopalan

Litvan

Jung

2017

Sensors

127

101

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Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

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Section: Fall Prevention Systemsmentioning

confidence: 99%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Rajagopalan

Litvan

Jung

2017

Sensors

127

101

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“…Technology may advance this further. For example, a relatively new innovation includes a robot control system for in-home environment screening of falls hazards [7]. This system enables a health professional to assess a house remotely and to interact with the user using telepresence.…”

Section: Introductionmentioning

confidence: 99%

Falls Sensei: a serious 3D exploration game to enable the detection of extrinsic home fall hazards for older adults

Money

Atwal

Boyce

et al. 2019

BMC Med Inform Decis Mak

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Background: Falls are the main cause of death and injury for older adults in the UK. Many of these falls occur within the home as a result of extrinsic falls risk factors such as poor lighting, loose/uneven flooring, and clutter. Falls education plays an important role in self-management education about extrinsic hazards and is typically delivered via information leaflets, falls apps, and educational booklets. Serious games have the potential of delivering an engaging and informative alternative to traditional methods but almost exclusively, these are currently delivered as exergaming applications that focus solely on intrinsic falls risk factors. This study presents 'Falls Sensei' a first-person 3D exploration game that aims to educate older adults about extrinsic falls risk factors within the home environment. After presenting Falls Sensei, game usability and older adults' perceptions and attitudes towards using the game in practice are explored. Methods: This study involved 15 community dwelling older adults. After playing the Falls Sensei game, participants completed a Systems Usability Scale (SUS) questionnaire and post task interview, and follow-up interviews three weeks later. Inductive and deductive thematic template analysis, informed by the Unified Theory of Acceptance and Use of Technology model, was used to analyse the think-aloud, post-task and follow-up interview transcripts. Descriptive statistical analysis and one-sampled t-tests were used to analyse log-file data and SUS responses. Results: Three high-level themes emerged from the analysis of transcriptions: Performance Expectancy; Effort Expectancy; Social Influence. The SUS score was 77.5/100 which indicates 'Good' levels of usability. Interestingly, reported usability of the game increased with participant age. Participants were positive about the usability of the game (p < = 0.05 for 9/10 items). The most memorable fall hazards were those most commonly encountered in the game or those most challenging to participants. Conclusions: The results support the use of serious games as an engaging tool for educating older adults about extrinsic falls risk factors. Awareness of home hazard detection was raised by the game, and some older adults became more aware for the need to adapt their own homes after gameplay. Further research would be needed to draw comparisons with established interventions.

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“…Where extrinsic factors are considered, many apps still require a clinician’s input to provide advice or interpret results. For example, Hamm et al (2019) reported an app that allowed carers to conduct assessments for assistive technologies to improve home safety, and two papers described the use of technology to allow clinicians to remotely conduct safety and hazard assessments via video (Romero et al , 2018) and robotic technology (Du et al , 2014). Whilst these papers reported good usability and acceptability from participants, these apps still require the involvement of a clinician, which leaves such services out of reach for those who do not meet the relevant support, service criteria.…”

Section: Introductionmentioning

confidence: 99%

Web-based home-hazard modification app for falls prevention: the views of those at risk of falling and their carers

Holliday

Ward

Walker-Clarke

et al. 2020

JET

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Purpose The purpose of the study is to assess the usability and acceptability of FallCheck, a Web app that allows users to complete home-hazard assessments within their own home, with a group of people at risk of falling and their carers. Design/methodology/approach This mixed method study used an online survey followed by semi-structured telephone interviews to collect both qualitative and quantitative data. A Think-Aloud study was used to test usability of the Web app through structured tasks. Findings Findings showed that FallCheck was easy to use with few usability issues. The Web app was deemed appropriate to use by people at risk of falling (young or old) or by carers if appropriate. The depth of knowledge provision and breadth of content was acceptable, and many participants reported subsequently making changes to their home environment to reduce their risk of falling. Overall, the majority of participants reported feelings of improved confidence and safety with an increased awareness of fall risks and a reduction in fear of falling at home. Practical implications FallCheck has good acceptability and usability with people at risk of falling and their carers and has the potential to improve access to home-hazard assessment and affect behavioural change regarding fall risk hazards and behaviour. Originality/value This study describes successful use of an app that may be helpful in identifying home-hazards and making changes to reduce risk of falls, particularly in the absence of occupational therapy intervention and has the potential for integration into falls care pathways.

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Robotics enabled in-home environment screening for fall risks

Cited by 8 publications

References 9 publications

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Falls Sensei: a serious 3D exploration game to enable the detection of extrinsic home fall hazards for older adults

Web-based home-hazard modification app for falls prevention: the views of those at risk of falling and their carers

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