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How to Save a Life: Could Real-Time Sensor Data Have Saved Mrs Elle?

AUSTRALIAN E-HEALTH RESEARCH CENTRE

Dana Kai Bradford | Senior Research ScientistMay 2018

Bradford D. and Zhang Q. 2016 How to save a life: could real-time sensor data have saved Mrs Elle? CHI ‘16 Extended Abstracts 910-920.

Why Smart Homes?

• Ageing on the rise• 14% of the population 65+ (~3.34 million) (ABS 2013)

• Number of residential care places ~270,000 (www.AIHW.gov.au)

• Age in place (Menzies Centre 2012)

Home monitoring fosters increased independence, sense of security and lowered frustration

(IBES 2012; Doyle et al. 2009; Wild et al. 2008; Smith et al. 2007

Bradford et al. 2017 Watching over me: Positive, negative and neutral perceptions of in home monitoring held by independent

living elderly residents in an Australian pilot study. Ageing & Society)

How to Save a Life | Dana Kai Bradford


Overview of the Smart Home System

Components of the Smart Home System


Type Data gathered Place of installation Sensors Motion sensor Motion within 5m Ceiling in all rooms

Power meters Power use of appliances Wall power outletsAcoustic sensor Water flow Kitchen downpipeAmbient sensor Temperature, Humidity Kitchen, bathroomAccelerometer Object movement Bed, chairReed switch Doors open/close Entrance, wardrobe, kitchen cupboard, fridgeCircuit meter Stove use Meter box

Medical devices

Blood pressure monitor

Systolic/diastolic pressure, heart rate

At resident’s discretionWeight scale Body weight Thermometer Body temperatureType Data shown Location

Tablet iPad with SSH app

Health measurements (graphs)Movement dataVideo conferencingDaily and weekly diary

Portable

Family Portal Website Varied depending on level of access

approved URL provided to approved family members

Clinical Portal Website Medical data and ADLs URL provided to approved practitioners

Activities of daily living


Zhang et al. 2014. Activities of daily living assessment through wireless sensor data. Proceedings of the 36th IEEE Engineering in Medicine and Biology Society Conference (EMBC), Chicago USA.

Single sensor

Activity Recognition - mobilitySteps


Activities of daily living



Two sensors

Hygiene, Dressing, Transfer

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Dressing


Hygiene


Transfer

Scor

es


Activities of daily living − Meal preparation



Multiple sensors

Mon Tue Wed17:52 – 18:49 20:46 – 21:04 11:07 – 12:55

08:40 – 09:13 13:40 – 15:07 20:30 ~ 22:00

High

Low In

fere

nce

Activity Recognition – meal preparation


0

10

20

30

40

50

60

70

80

90

100


Mobility Meal Hygiene Dressing Transfer

OADL score representing health status


Estimated Vs. Self-reported health status

Home A

Home B

OADL estimation ADL estimation Self-reported


Family portal


Clinical portal


The delightful Mrs Elle


Components of the Smart Home System


Type Data gathered Place of installation Sensors Motion sensor Motion within 5m Ceiling in all rooms

Power meters Power use of appliances Wall power outletsAcoustic sensor Water flow Kitchen downpipeAmbient sensor Temperature, Humidity Kitchen, BathroomReed switch Doors open/close Entrance, wardrobe, kitchen cupboard,

refrigerator Circuit meter Stove use Meter box

Medical devices

Blood pressure monitor

Systolic/diastolic pressure, heart rate

At resident’s discretionWeight scale Body weight Thermometer Body temperatureType Data shown Location

Tablet iPad with SSH app

Health measurements (graphs)Movement dataVideo conferencingDaily and weekly diary

Portable

Family Portal Website Varied depending on level of access

approved URL provided to approved family members

Clinical Portal Website Medical data and ADLs URL provided to approved practitioners

Motion Sensor Firing Ceases


When the curtains remained closed two days running, the neighbor alerted facility staff and Mrs Elle was found unconscious in bed at around 9.30am on 5 November.

Secrets in the Sensors — Motion


0

50

100

150

200

250

300

350

400

Bathroom

Laundry

Lounge

Kitchen

Bedroom

Mea

n m

otio

n se

nsor

fire

s

Daily mobility prior to25 Oct

Daily mobility 25 Oct - 4Nov

Room ∆%

Bathroom -26

Laundry -53

Lounge -38

Kitchen -60

Bedroom +26

Secrets in the Sensors — Meal Preparation


0

20

40

60

80

100

120

15-A

ug17

-Aug

19-A

ug21

-Aug

23-A

ug25

-Aug

27-A

ug29

-Aug

31-A

ug02

-Sep

04-S

ep06

-Sep

08-S

ep10

-Sep

12-S

ep14

-Sep

16-S

ep18

-Sep

20-S

ep22

-Sep

24-S

ep26

-Sep

28-S

ep30

-Sep

02-O

ct04

-Oct

06-O

ct08

-Oct

10-O

ct12

-Oct

14-O

ct16

-Oct

18-O

ct20

-Oct

22-O

ct24

-Oct

26-O

ct28

-Oct

30-O

ct01

-Nov

03-N

ov

Mea

l pre

para

tion

scor

e

Secrets in the Sensors — Hygiene


0

20

40

60

80

100

120

15-Aug 22-Aug 29-Aug 05-Sep 12-Sep 19-Sep 26-Sep 03-Oct 10-Oct 17-Oct 24-Oct 31-Oct

Hygi

ene

Scor

e

Secrets in the Sensors — Medical data


• Temperature• M=35.5°C• Fluctuating < 1°C

• Weight• M=73kg• Fluctuations up to 3kg• Overall loss 2kg

• Cardiovascular• Pulse steady decline to <50 bpm• Several systolic readings of >140mmHg• Fluctuations

Fluctuations in blood pressure


0

20

40

60

80

100

120

140

160

15/08/2013 0:00

17/08/2013 0:00

19/08/2013 0:00

21/08/2013 0:00

23/08/2013 0:00

25/08/2013 0:00

27/08/2013 0:00

29/08/2013 0:00

31/08/2013 0:00

2/09/2013 0:00

4/09/2013 0:00

6/09/2013 0:00

8/09/2013 0:00

10/09/2013 0:00

12/09/2013 0:00

14/09/2013 0:00

16/09/2013 0:00

18/09/2013 0:00

20/09/2013 0:00

22/09/2013 0:00

24/09/2013 0:00

26/09/2013 0:00

28/09/2013 0:00

30/09/2013 0:00

2/10/2013 0:00

4/10/2013 0:00

6/10/2013 0:00

8/10/2013 0:00

10/10/2013 0:00

12/10/2013 0:00

Pre

ssu

re (

mm

Hg

)

Diastolic Systolic


“But as far as the high blood pressure, I did actually mention to

my doctor and she sort of slightly upped the blood pressure stuff

I was taking. And then I found I had some – I had another

condition (I can’t remember what it’s called now. Means I have

too many red blood cells.) and the specialist said no, you mustn’t

have a diuretic, so she had to go back to the other one. But my

blood pressure is going up and down like a yo-yo, if that thing’s

accurate. … I’ll mention that to the doctor, but this may be due

to the treatment I’m having for this thing that’s wrong. So I’m

not really bothered about that at the moment.”

Summary of physiological and behavioral changes


• reduced mobility • reduced meal preparation and • reduced attention to hygiene

• high blood pressure • odd fluctuations in blood pressure • low pulse rate • high red blood cell count

• low body temperature • losing weight • missed weekend activities and

intrastate drive

Indicative of fatigue

Recently visited GP with her concerns and had medication changed

Power of Smart Homes

• Harness information available from the sensors and devices to provide a predictive picture far more detailed than the glimpses currently seen by medical professionals. • Allow closer monitoring for people with medical conditions

• Dementia• Huntington’s Disease• Stroke


Challenge:to channel the accumulated information through a clinical portal to medical professionals to alert them to inform clinical decisions based on every day behaviour.

Conclusion

In-home monitoring is effective for determining activities of daily living and the algorithms are subtle enough to detect potential impending stroke.

But the prevention of such incidents will depend on coordinated action between the monitoring system, the response service and the medical health team.


Dear Mrs ElleWe would have alerted every specialist

in townHad our system known how to save a life.

Australian e-Health Research CentreDana Kai BradfordSenior Research Scientistt +61 7 3327 4404e [email protected] www.csiro.au

HEALTH AND BIOSECURITY

Thank you

Intervention

AcknowledgementsWe are very grateful to the residents who participated in the smart homes trial and the staff at the residential care facility.

We pay our deepest respects to Mrs Elle and her family.

Bradford D. and Zhang Q. How to save a life: could real-time sensor data have saved Mrs Elle? CHI ‘16 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (in press).

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