Activity Monitoring and Outcome Measurements by Remotely Sensing Daily Mobility and Exercise
in Disabled Persons
Bruce Dobkin, MD, FRCP
Professor of Neurology
Director, Neuro-Rehabilitation Program
Geffen/UCLA School of Medicine
UCLA Wireless Health Institute
mHealthDefinition: Delivery of healthcare services via mobile communication devices.Opportunity: By 2017, more mobile phones than people on the planet; currently three-quarters of the world’s population have access to a mobile phone. Goal: Facilitate medical and health information via instantaneous communication anywhere/anytime.Reduce disparities, prevent disease, improve diagnostics & therapy, increase adherence, personalize medical advice in chronic diseases, enhance research & daily care, merge diverse data sets, lower costs.
UCLA WHI Strategies• Develop reliable, highly valued tools to improve outcomes in
healthcare.• Deploy the intellectual resources of the UCLA medical,
nursing, engineering, public health and other schools and depts.
• Encourage faculty and students to identify needs, ideas and pilot studies.
• Design and Test: Use an iterative approach to identify opportunities, develop and test user-friendly tools, and show the efficacy of sensors (or other WHI devices) within scientifically conducted clinical trials on likely users.
• Serve as an incubator for wireless health ventures.
Fitbit Flex
PerformTek
Withings Smart Activity Tracker
BodyMedia Core2 Armband & Vue Patch
Fitbug Orb Fitlinxx Pebble
Basis Band
Lots of activity sensors, but where is the beef?
MDAWN Medical Daily Activity Wireless Network
An inexpensive, wireless sensor/algorithm system that can remotely recognize and quantify purposeful behaviors such as walking, exercise, and skills practice, as well as provide feedback about performance, in persons with impaired mobility.
Our niche: within clinical trials across diseases, improve outcomes for disabled persons
Monitor skills practice, exercise, & mobility activitiesin the home and community for compliance and safety, and to audit clinical trial interventions.
Develop outcome measures with continuous, rather than ordinal scales; quantify the type, intensity, and quality of mobility. Capture gains and declines in purposeful activities in the real world, not just in the unnatural environment of a lab and not with the ambiguity of self-report scales.
Remote Sensing Systems
Sensor Fusion
Classifiers
Application
Accelerometer
Gyroscope
Magnetometer
Ambient Sound & Visuals
RFID
Contact, EMG, Goniometer
Features
Athletic Training
Neuro- Rehabilitation
Motor Control
Gait Analysis; FallsType, Quantity,
Quality of Activity Daily Activity
Monitoring & Feedback
Naïve Bayes
Hidden Markov Model
Neural Networks
Decision Tree
Nearest Neighbor
Singular Spectrum Analysis
Energy of Signal
Peak Frequency
Correlation of Axis
Mean of Signal
Std. Dev.
Ankle accelerometers can describe walking, cycling, exercises, and overall activity in the home and community at low cost.
Dobkin & Dorsch. Neurorehabil Neural Repair, 2011
MDAWN for Disabled Persons
• Based upon two 10-meter walks, machine-learning algorithms enable a template for each participant that identifies subsequent episodes of walking or exercise throughout the day.
• Gait parameters include walking speed, duration, distance, and limb asymmetries, which are calculated for each walking episode.
77 year-old with chronic left hemiplegic stroke
Walking speed is 0.1m/s
Stroke Inpatient Rehabilitation Reinforcement of ACTivity (SIRRACT)
• Can clinicians improve walking-related outcomes during hospital-based rehab?
• International RCT.• Wear ankle sensors.• Compared 2 levels of
daily feedback about performance.
• 140 subjects at 15 sites.• Showed increasing amount
of walking and walking speed from admission to discharge.
• Rather low mean daily amount of training was detected.
• Proved ease of use, accuracy, relevance of the data.
Presented at AAN, 3/13
Daily # steps Daily distance walked
Average walking speed
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Days since stroke
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SIRRACT participant in Taiwanduring inpatient stroke rehabilitation
Instrumented Devices: UCFit low cost system for bed exercise• Home or hospital
– Android smartphone with apps– Portable, battery-powered,
weighs <7lbs.– Strain gauge & MicroLEAP
sensor platform.
• Data acquisition automatic– UCFit Server’s secure systems
acquire, archive, present data, and provide feedback graphics
UCFit
BluetoothWireless
SmartphoneApp
Internet
MDAWN ServerUser Group’s Database
UCFit light resistance cycling for disabled persons
Distance Traveled (27" wheel)
0.0
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Date
Dis
tan
ce
(m
iles
)
Average Power Output
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Total Time Spent Pedaling
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Date
Tim
e (
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ute
s)
Average Cadence (RPMs)
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Date
RP
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POD
Pe
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2.8
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rqu
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Nm
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time
torque
UCFit daily time/torque for post-op liver transplantpatient in ICU: physiological data and insight for care
Sensors for daily medical care• Monitor hourly or day to day fluctuations in responses to medications, as for Parkinson’s, epilepsy, spasms, dyskinesias.• Monitor compliance with activity-related instructions for practice or exercise to reduce risk factors and improve function.•Monitor for changes in activities that may reflect a decline in functioning, due to disease exacerbation, new complications, side effects of drugs, mood disorders.•Provide feedback about performance to progressively improve specific outcomes.•Feedback and monitoring to motivate goal-setting and compliance.•Establish new types of measurable activity-related outcomes and goals.•Reduce number of visits, and cost, for care of chronic disability.
Sensors for clinical research trials• Develop ecologically sound outcome measures of activity to
augment questionnaires and ordinal scales of disability and physical functioning.
• Obtain continuous measures of daily activities – type, quantity, quality. Also enables trialists to phase in an intervention so that a baseline behavioral plateau is assured.
• Reduce the cost and increase the validity of clinical trials by being able to remotely assess what is practiced, how much, and how well, during a trial.
• If subjects at multiple sites or at home are being trained in a skill, such as walking or using an affected arm and hand, monitor the integrity of the intervention.
• Observe the effects of adverse events, such as drugs, pain or falls, on activity.
• Increase the number of interim outcome measurements to better develop dose-response curves.
Type, quantity & quality of activity in relation to physiologic variables, images, social interaction, environmental toxins, cues & feedback
Alzheimer’s Vital signs, location, balanceAsthma RR, FEV1, oximetry, air quality, pollenCOPD “ “ “Cancer Adverse effects of meds & diseaseDepression Drug compliance, communicationDiabetes Glucose, HgbA1c, drug use, exerciseCHF Pulm artery pressure, weight, VS, fluidHypertension Continuous BP, drug complianceObesity Smart scales, calories in/out, behaviorSleep Sleep stages, quality, apnea
High throughput, multi-streamed, longitudinal data sets to facilitate disease prevention, management and behavioral changes.
Requirements for mHealth data
• 1. Collect data from technologies along with self-reported behavioral, psychosocial, environmental, and contextual measures. Analytics for BIG DATA.
• 2. Integrate various wireless physiologic and bio sensors on open platforms.
• 3. Appropriately secure data at each stage of collection, transfer, and storage.
• 4. Visualize data using customizable tools.• 5. Analyze and report on individual or group level data
using customizable tools and reporting systems.• 6. Maintain compliance with HIPAA, IRB and FDA.• 7. Demonstrate efficacy and effectiveness of real-
world data.
Address patient-centered outcomes research:NIH and Medicare priority
• “Given my personal characteristics, conditions, and preferences,…..
• “What should I expect will happen to me?”• “What are my options, and what are the benefits and
harms of those options?”• “What can I do to improve the outcomes that are most
important to me?”• “How can the health care system improve my chances of
achieving the outcomes that I prefer?”
Personal activity logging and feedback
Washington, NEJM, 2011
UCLA Wireless Health Institutewww.wirelesshealth.ucla.edu
• Bill Kaiser, Majid Serrafzadeh, Deborah Estrin, Greg Pottie, Chris Cooper
UCLA Medical and Engineering Campus
www.Wirelesshealth.ucla.eduWilliam Kaiser, Greg Pottie, Andrew Dorsch, Seth Thomas,
Celia Xu, Lam Yeung, Eric Yeun, James Xu, Yan Wang,
