Non-contact sensor based falls detection in residential aged care facilities: Developing a real-life picture
Cecily GilbertHealth and Biomedical Informatics Centre, University of Melbourne
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Falls facts in Australia
More frequent for those aged 65+:
- 30% of people living at home will have a fall each year.
- much higher rate in aged care facilities.
- leading reason for admission to hospital: 38% compared to 13% for transport related injuries.
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Study Aim and method
Objective:
to test the feasibility and acceptability of a ambient non-wearable sensor technology with older participants in a residential care facility.
Mixed method approach comprising:
a) Empiric study implemented at a residential care facility using purposive sampling
b) Evaluation and post-study interviews
c) Analysis and review of results
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Study setting and criteria
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Purpose-built aged care facility:
• 170 places, 200 staff
• Less than 8 years old
• Bed-exit and pressure mat alarms wired to nurse-call system
• IT support outsourced
Involvement in study:
• Management & senior staff supportive
• Agreed to screen occupants and approach eligible residents (or authorised family members) for consent to participate
Participant selection criteria:
• Aged 65+
• Previous falls history (e.g. two or more in past 6 months)
• Able to walk either independently or with staff assistance (+/- gait aid)
Exclusion criteria:
• Bed bound, or require hoist for transfers
• Residents currently receiving palliative care
• Residents who have had no falls in previous 12 months
4 male residents in the pilot study- average age 87 years- complex chronic diagnoses
Assessment Resident 1 Resident 2 Resident 3 Resident 4
Fall risk assessment HIGH HIGH HIGH HIGH
Previous falls? No history available 5 falls in prior 6 months
13 falls in prior 6 months
1 fall in prior 6 months
Uses gait aid? 4-wheel walker 4-wheel walker Wheelchair beyond room
4-wheel walker
Mobility assistance?
Assist X 1 Assist X 1 Transfer to wheelchair assist X 2
Supervision X 1
Level of care High Care High Care High Care High Care
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In each participant’s suite:
- one sensor in bedroom
- another sensor in en-suite bathroom.
Sensor installation
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Rolled out sequentially:
- set up, test with healthy volunteer, live-test, then moved to next suite.
- 8 sensors installed in total.
Prototype sensor adapted by industry partner
• Privacy preserving
• Optical, non-contact
• On-board cognitive processing
• Skeletal pose tracking
• Suited to indoor environment
• Designed for 24/7 operation
Depth images
Results
ITEM DATA
Sensors functioning 8 installed, only 7 operated reliably
Total days of sensor operation 122
Monitoring duration Range 5 – 22 days per participant
Data generated 18 GB – 25 GB per day per room, saved as high compression files onto secure dedicated server
Fall events One known fall occurred, but was not captured because sensor cable was faulty at the time.
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Challenges and unexpected events
Gaps in network connectivity e.g. not all room data points were cabled to the core network. Wireless workaround devised, not optimal.
Radio-frequency interference from wall-mounted TV screens in bedroom disabled sensor wireless network: Sensors were re-positioned, but this caused tracking performance to decline, increased ‘noise’. As a result, the event detection threshold setting was raised to detect only medium or high fall-like events.
Range and quality of the commercial pose-tracking component in the sensors was more limited in practice than shown in the lab testing:
i.e. pose-tracking healthy volunteers was not adequate to determine sensor effectiveness with older person. 9
Acceptability
Post-study interviews with staff indicated strong acceptance:
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Interview Question Response
Did the sensors change the amount or type of contact between residents and staff?
No, it really didn’t have any negative impacts on anybody.
Do you have any concerns with the display of visual images from the sensors?
It’s not a facial picture – just stick figures really. So that’s good for privacy and confidentiality.
How is the sensor data useful for residents with cognitive disabilities?
I think it would be useful in [the RAC] overall, for people who can’t articulate how a fall happened.
Has being part of the sensor trial changed your view about sensors for fall detection or prediction?
It’s exciting where the research is taking us…ultimately we want to keep our people safe.
Conclusions – Operational lessons learned
Unexpected technical difficulties delayed full implementation of sensors in participants’ rooms
- End-to-end live testing of hardware and networks is essential before launching.
Optimal placement of sensors is not straightforward
- Suggests need to involve clinicians in realistic calibration for sensors to balance sensitivity and specificity.
Staff and carer attitudes to the sensors were positive overall
i.e. envisaged a range of benefits if proven to work: knowing the events that lead to a fall, earlier detection of falls if sensors are linked to alarm system etc.
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Acknowledgements and team members
Thanks to:
• Study participants and their carers
• Staff at the residential facility
• Study Advisory Board members.
Funding gratefully received from the Melbourne Networked Society Institute.
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Study team:• A/Prof Ann Borda1
• Dr Cathy Said 2
• Mr Frank Smolenaers 3
• Mr Michael McGrath 4
• A/Prof Kathleen Gray 1
• Ms Cecily Gilbert 1
(1) Health and Biomedical Informatics Centre, University of Melbourne
(2) Physiotherapy Directorate, Western Health(3) Australian Centre for Health Innovation,
Alfred Health(4) Semantrix Pty Ltd
