applications for furthering independence and independent living
TRANSCRIPT
What Technologies are on the horizon for providing care-giving support for older adults?Applications for furthering independence and independent living.
M. Alwan et al.
Medical Automation Research Center
What is MARC?MARC is a research, development and consulting organization providing medical and industrial clients with innovative automation solutions.
Expertise in:Eldercare Technologies Program: Low-cost in-home monitoring and assistive technologiesAutomating clinical, drug discovery, genomics, and health care deliveryCollaborative multidisciplinary research and developmentSpinning-off small businesses
Mission of Eldercare Technologies Program
Provide simple technological solutions: Enhance existing health care system, e.g.
telehealth Improve quality of life for elders / disabled Multiply caregiver ability to interact
positively Increase independence, mobility and levels
of activity for older adults / the disabled Reduce risks and potentially reduce the
costs of care
Smart House Project Adaptive, modular, low-cost, non-invasive monitoring system (suite of sensors + data management module) Service Provider Module: An Integrated Data Management System Remote data analysis to infer
activities of daily living, activity patterns and health conditions over time
Provide feedback to both informal and professional caregivers as well as health providers
System Overview
Service Provider Server
Data Manager
Sleep/Bed Exit
Falls/Gait
Motion Activity
Collects data from multiple peripheral
units
Older adult
User Monitoring
Service Provider
Caregiver / Care
Provider
Motion Sensors
Currently eight sensors placed in the house detect occupant motion generating date and time stamped data.
Motion Data: Reveals Activity & Patterns
Sensor Firing (5:57 - 6:39 a.m.)
0
1
2
3
4
5
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8
TIME
Kitchen
Laundry
Front Door
Living Room
Bathroom
Office
Bedroom
5:57 6:396:15 6:21
21
23
25
27
012345678
Sensor
12:00 AM to 8:00 AM
Date
Morning Sensor Activity from One Week
A single day (left) can be examined with other days and pattern activity analyzed (below)
Validation Results: Meal Preparation
Statistic Validation95%
Confidence Interval
0.8600 0.8800 p < 0.0001
Sensitivity 0.9063 0.7499-0.9802 Specificity 1.0000 0.7531-1.0000
The system was validated over 37 days, through comparisons to a customized PDA activity log.
No lunch or dinner events were missed by the detection algorithm in the validation or test data sets
Caregiver Feedback
Sleep MonitorThe Sleep monitor is
comprised of a suite of sensors that include:Matrix of Momentary Contact SwitchesTemperature SensorsHumidity and Carbon Dioxide SensorsLight Level SensorsPressure Sensor
Matrix maps user position
Processed vibration signal gives pulse rate and respiration
Validation Results:Heart Rate Measurements
Vibration Sensor vs. Pulse Oximeter
45
50
55
60
65
70
45 50 55 60 65 70
Pulse Oximeter Results (BPM)Vi
brat
ion
Sens
or R
esul
ts
(BPM
)
Chest, Lying on Back Chest, Lying on Side Chest, Lying on Stomach
Heart Rate Measurements
Correlation Coefficient
Standard Deviation in Beats per minute
(BPM)R2 p from best fit
line (BPM)From 45° line
BPM)
Chest, all positions
0.811 <0.0001 2.09 2.55
Abdomen, all positions
0.854 <0.0001 2.16 2.23
Overall 0.829 <0.0001 2.16 2.39
The sensor system can be mounted on the
baseboard in walkway path
Passive Unobtrusive Gait MonitorA highly sensitive gait monitor, which can be easily deployed in any home or clinical environment Small, low-cost and may wirelessly transmit gait data derived from floor vibrationsCan be used in a natural setting and does not require the user to do anything special
Preliminary Results:Normal Gait and Fall detection
Original Signal
Filtered Signal
Timing
Processed Signal
Peaks
Original Signal
Falling Person Detected
Peak amplitudes: Increasing toward sensor, decreasing away
Fall detection
Benefits of the Gait MonitorMeasure step count and estimate average paceDistinguish between normal, limping and shuffling gait modesDetect fallsDetect changes in pace and gait mode over timeLow-cost, unobtrusive and longitudinal in-home gait analysisPotential to initiate emergency calls in case of falls followed by inactivity
Passive Pulse Monitor (Pipeline)
• Detects pulse rate from barefoot person standing on bathroom scale• Blood pressure can be derived from pulse signal• Provides weight and body fat percentage
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20-1.5
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0.0
0.5
2.0
2.5
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3.5
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-1.0
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Time (Seconds)
Pulse Oximeter Signal
Empirical Technologies CorporationCharlottesville, Virginia434 296-7000
using the ETC Fiber-Optic Loop-back SensorPreliminary Heartbeat Detection using a Bathroom Scale
raw ETC Signal Signal
ETC signal FFT filtered from 0.8 - 1.5 Hz
Laser range-finder Obstacle avoidanceFront wheel steering controlPassive: shared control platformAugmented with rear wheel collision sensor and audio-visual warnings
Robotic Walker Project
BenefitsIncrease independence and improve quality of life for the elderlyMonitor daily activities and derive health indicatorsOpportunity for medical and / or family intervention before a crisis occursProvide peace of mind and minimize caregiver burdens and strainsPotentially delay admittance to a nursing homeReduce the costs of elder careCan be adopted by continued care facilities and home care providersPresents care providers with the opportunity to outreach into the community
What’s special about the MARC monitoring concepts?
Passive and unobtrusiveImplements simple, low cost sensor technology, and computationally inexpensive algorithmsAdaptive: retrofits existing home structures with minimal intrusion and modificationsEmploys technology that is available todayData mining component will yield unique information for the occupant, their medical advisors, and family members
Challenges to Adopting Technologies
ReimbursementPrivacy and the introduction of HIPAAAdaptability / AcceptanceLarger scales impact studies (including economic and social ones) on elder adults, professional caregivers and informal care providers
Thank you and Contact
Web: http://marc.med.virginia.eduInformation request: [email protected] Researchers:
Robin Felder, Ph.D. [email protected] Majd Alwan, Ph.D. [email protected] Steve Kell, AAS [email protected] David Mack, BSME [email protected] Siddharth Dalal, MSC [email protected] Beverely Turner, BS [email protected]