wh2014 session: quantifying energy expenditure of wheelchair-based physical
DESCRIPTION
Wireless Health 2014 Conference Technical Session 2 featuring speaker Shivayogi Hiremath.TRANSCRIPT
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QUANTIFYING ENERGY EXPENDITURE OF
WHEELCHAIR-BASED PHYSICAL ACTIVITIESIN FREE-LIVING ENVIRONMENTS
SHIVAYOGI HIREMATH
UNIVERSITY OF PITTSBURGH
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Quantifying Energy Expenditure of Wheelchair-based Physical Activities in Free-living Environments
Shivayogi Hiremath, Stephen Intille Rory Cooper, Dan Ding Wireless Health 2014 Bethesda, MD, USA
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Introduction • Lack of regular physical activity (PA) is a major
public health concern
• Regular PA is crucial in wheelchair users as it is associated with
– Aerobic Capacity and Flexibility – Muscular strength and Endurance – Improved psychological well-being – Risk of cardiovascular disease and other chronic
conditions • Regular PA levels in wheelchair users are low
– Environmental barriers – Lack of accessible equipment – Physiological changes
© Shivayogi Hiremath 2010-14
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Introduction • Research has shown that it is possible for
wheelchair users to attain PA levels recommended by the ACSM1
– Wheelchair basketball, tennis, Handcycling
• Self-monitoring of diet, PA and body weight can assist in maintaining a healthy lifestyle
• Availability of an activity monitor will allow wheelchair users to achieve optimal regular PA leading to a healthy and active lifestyle
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Background • Sensor-based PA monitors have been used
to track and quantify PAs among wheelchairs users 2-6
– None of these monitors can capture and provide real-time feedback to the user
Approach • Real-time feedback enabled by
advancements in m-Health • Meaningful feedback to wheelchair users
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Physical Activity Monitoring System
Wocket7
G-WRM8
PAMS
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Testing of PAMS in MWUs • 45 MWUs with SCI
– Laboratory (n=25) & NVWG 2012 (n=20) – Home (n=20)
• Activities Performed – Resting – Arm-Ergometry – Darts, Basketball – Deskwork, Watching TV – Folding Clothes, Laundry – Food Preparation, Eating Simulation – Propulsion: Carpet, Tile, Ramp, Home – Resistance: Band, Dumbbell, Handgrip – Making Bed, Floor Sweeping – Cleaning Room, Vacuuming – Wheelchair Pushups
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Model Development • Two step classification algorithm to detect
wheelchair based PAs • Activity-specific energy expenditure
estimation models – Resting – Arm-ergometry – Household activities – PAs that might involve wheelchair movement – Wheelchair propulsion – Caretaker pushing – Basketball
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Results
-1
-0.5
0
0.5
1
1.5
2
-3
-2
-1
0
1
2
3
4
5
6
1 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 11 11 11 11 11
Dis
tan
ce T
rave
lled
in m
/10
s
Acc
eler
atio
n in
m/s
2
Activity Trials
Mean Acceleration and Distance Travelled WocketArmDistTravelled
Resting Arm-Ergometry Darts Deskwork Folding Propulsion Invest Resist- Clothes Carpet Moderate Slow Pushing ance
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Results Demographic Characteristics
Age 41.0 ± 12.6 years
Gender 39 males; 6 females
Weight 78.1 ± 18.1 kg
Height 1.8 ± 0.1 m
SCI Level C5 to L5
Completeness 22 (Complete); 23(Incomplete)
Wheelchair use 12.6 ± 8.6 years
Smokers 14
PA participation 36 regular; 5 occasional; 4 no regular PA
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Results
10
00.5
11.5
22.5
33.5
44.5
5
EE
Energy Expenditure for Various Activities
EE in kcal/min
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Results: Classification 80-20CV
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Training Accuracy Testing Accuracy Models
0.9356 0.9801 SVM
• First level classification
M/NM Training Accuracy Testing Accuracy Model
NM 0.8678 0.8495 J48
M 0.9557 0.9340 SVM
• Second level classification
• Classification into seven PAs showed that PAMS had an overall accuracy of 89.3%
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Results: Confusion Matrix
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True\Predict R AE NM Prop Push Bask MM
Resting 22 0 24 0 0 0 1
AE 0 85 6 0 0 0 1
PAs NM 3 2 135 0 0 0 0
Propulsion 0 0 0 134 1 0 0
Pushing 0 0 0 2 44 0 1
Basketball 0 0 1 5 1 17 6
MM 0 0 0 0 0 0 12 12
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Results: Mean Signed Error
13
-50-40-30-20-10
01020304050
Per
cen
tage
Err
or (
%)
Wheelchair based Physical Activities
Plot of Mean Signed Error for PAMS
PAMS
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EE estimation error post-classification showed that the overall EE error for PAMS was -9.8% (0.1%).
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Results: Mean Absolute Error
14
05
101520253035404550
Per
cen
tage
Err
or (
%)
Wheelchair based Physical Activities
Plot of Mean Absolute Error for PAMS
PAMS
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Results: Metabolic Equivalent Tasks
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METs based Intensity Actual mins Estimated mins K4b2 PAMS
Light Intensity 387 406 Moderate Intensity 116 97 High Intensity 5 3
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Discussion
• Frequency based features were picked for classification algorithms for certain PAs
• Majority of the regression equations chose demographic characteristics to estimate EE
• Multi-modal information from PAMS can be utilized to quantify wheelchair-based activities in laboratory and community
• Limitations –Active population –Movement based variables
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Future Work
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• Evaluation of PAMS in community settings
• Evaluation of PAMS in community along with DLW
• Expand to other population • Use social networks such as Facebook
– Combine self-monitoring with social support
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1. American College of Sports Medicine, Mitchell H. Whaley, Peter H. Brubaker, Robert Michael Otto, Lawrence E. Armstrong ACSM's guidelines for exercise testing and prescription, 7, illustrated ed.: Lippincott Williams & Wilkins, 2005.
2. C. A. Warms and B. L. Belza, "Actigraphy as a measure of physical activity for wheelchair users with spinal cord injury," Nursing Research, vol. 53, pp. 136-43, 2004.
3. S. E. Sonenblum, S. Sprigle, J. Caspall, and R. Lopez, "Validation of an accelerometer-based method to measure the use of manual wheelchairs," Medical Engineering and Physics, vol. 34, pp. 781-86, 2012.
4. M. L. Tolerico, D. Ding, R. A. Cooper, D. M. Spaeth, S. G. Fitzgerald, R. Cooper, et al., "Assessing mobility characteristics and activity levels of manual wheelchair users," Journal of Rehabilitation R and D, vol. 44, pp. 561-72, 2007.
5. M. Lee, W. Zhu, B. Hedrick, and B. Fernhall, "Estimating MET values using the ratio of HR for persons with paraplegia," Med Sci Sports Exerc, vol. 42, pp. 985-90, 2010.
6. E. H. Coulter, P. M. Dall, L. Rochester, J. P. Hasler, and M. H. Granat, "Development and validation of a physical activity monitor for use on a wheelchair," Journal of Spinal Cord, vol. 49, pp. 445-450, 2011.
7. S. S. Intille, F. Albinali, S. Mota, B. Kuris, P. Botana, and W. L. Haskell, "Design of a Wearable Physical Activity Monitoring System using Mobile Phones and Accelerometers," in Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. , Boston, MA, 2011, pp. 3636-39.
8. S. V. Hiremath, D. Ding, and R. A. Cooper, "Development and evaluation of a gyroscope based wheel rotation monitor for manual wheelchair users.," Spinal Cord Medicine, vol. 36, pp. 347-356, 2013.
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References
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Acknowledgements
• Switzer Research Fellowship (H133Fl10032), NIDRR, Department of Education
• Department of Defense (W81XWH-10-1-0816) • RERC on Interactive Exercise Technologies
and Exercise Physiology for Persons with Disabilities (H133E070029), NIDRR
• VA Center of Excellence for Wheelchairs and Associated Rehabilitation Engineering (B3142C)
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