Could In-home Sensors Surpass Human Observation of People at High Risk of Falling? An Ethnographic Study

Emma Stack, Rachel King, Balazs Janko, M Burnett, N Hammersley , Veena Agarwal, Sion L Hannuna, Alison B Burrows, Ann M Ashburn

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)
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Self-report underpins our understanding of falls among people with Parkinson’s (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigate which in-home sensors, in which locations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson’s were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and potentially guide intervention. Monitoring the route between chair and stairs is likely to give information without invading the privacy of people at high risk of falling, with very limited mobility, who spend most of the day in their sitting rooms.
Original languageEnglish
Article number3703745
Number of pages10
JournalBioMed Research International
Publication statusPublished - 14 Feb 2016

Structured keywords

  • Digital Health


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