Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury
Abstract
:1. Introduction
2. Methods
2.1. Subjects
2.2. Instrumentation
2.3. Procedure
2.4. Data Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Jain, A.K. ISCOS-Textbook on Comprehensive Management of Spinal Cord Injuries. Indian J. Orthop. 2016, 50, 223–224. [Google Scholar] [CrossRef]
- World Health Organization. International Perspectives on Spinal Cord Injury; WHO: Geneva, Switzerland, 2013; Volume 250. [Google Scholar]
- Serra-Añó, P.; García-Massó, X.; Pellicer, M.; González, L.-M.; López-Pascual, J.; Giner-Pascual, M.; Toca-Herrera, J.L. Force Normalization in Paraplegics. Int. J. Sports Med. 2012, 33, 452–458. [Google Scholar] [CrossRef]
- Ginis, K.A.M.; Hicks, A.L.; Latimer, A.E.; Warburton, D.E.R.; Bourne, C.; Ditor, D.S.; Goodwin, D.L.; Hayes, K.C.; McCartney, N.; McIlraith, A.; et al. The Development of Evidence-Informed Physical Activity Guidelines for Adults with Spinal Cord Injury. Spinal Cord 2011, 49, 1088–1096. [Google Scholar] [CrossRef]
- Noreau, L.; Shephard, R.J. Spinal Cord Injury, Exercise and Quality of Life. Sports Med. 1995, 20, 226–250. [Google Scholar] [CrossRef] [PubMed]
- Montesinos-Magraner, L.; López-Bueno, L.; Gómez-Garrido, A.; Gomis, M.; González, L.M.; García-Massó, X.; Serra-Añó, P. The Influence of Regular Physical Activity on Lung Function in Paraplegic People. Spinal Cord 2016, 54, 861–865. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rimaud, D.; Calmels, P.; Devillard, X. Training programs in spinal cord injury. Ann. Readapt. Med. Phys. 2005, 48, 259–269. [Google Scholar] [CrossRef] [PubMed]
- Van Straaten, M.G.; Cloud, B.A.; Morrow, M.M.; Ludewig, P.M.; Zhao, K.D. Effectiveness of Home Exercise on Pain, Function, and Strength of Manual Wheelchair Users with Spinal Cord Injury: A High-Dose Shoulder Program with Telerehabilitation. Arch. Phys. Med. Rehabil. 2014, 95, 1810–1817.e2. [Google Scholar] [CrossRef] [Green Version]
- Rekand, T.; Hagen, E.M.; Grønning, M. Spasticity Following Spinal Cord Injury. Tidsskr. Nor. Laegeforen. 2012, 132, 970–973. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Van Houtte, S.; Vanlandewijck, Y.; Gosselink, R. Respiratory Muscle Training in Persons with Spinal Cord Injury: A Systematic Review. Respir. Med. 2006, 100, 1886–1895. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Montesinos-Magraner, L.; Serra-Añó, P.; García-Massó, X.; Ramírez-Garcerán, L.; González, L.-M.; González-Viejo, M.Á. Comorbidity and Physical Activity in People with Paraplegia: A Descriptive Cross-Sectional Study. Spinal Cord 2018, 56, 52–56. [Google Scholar] [CrossRef]
- Giangregorio, L.; McCartney, N. Bone Loss and Muscle Atrophy in Spinal Cord Injury: Epidemiology, Fracture Prediction, and Rehabilitation Strategies. J. Spinal Cord. Med. 2006, 29, 489–500. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Collins, E.G.; Gater, D.; Kiratli, J.; Butler, J.; Hanson, K.; Langbein, W.E. Energy Cost of Physical Activities in Persons with Spinal Cord Injury. Med. Sci. Sports Exerc. 2010, 42, 691–700. [Google Scholar] [CrossRef] [PubMed]
- Nevin, A.N.; Steenson, J.; Vivanti, A.; Hickman, I.J. Investigation of Measured and Predicted Resting Energy Needs in Adults after Spinal Cord Injury: A Systematic Review. Spinal Cord 2016, 54, 248–253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, S.; Gao, R.X.; Freedson, P.S. Computational Methods for Estimating Energy Expenditure in Human Physical Activities. Med. Sci. Sports Exerc. 2012, 44, 2138–2146. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tsang, K.; Hiremath, S.V.; Crytzer, T.M.; Dicianno, B.E.; Ding, D. Validity of Activity Monitors in Wheelchair Users: A Systematic Review. J. Rehabil. Res. Dev. 2016, 53, 641–658. [Google Scholar] [CrossRef]
- Ainslie, P.; Reilly, T.; Westerterp, K. Estimating Human Energy Expenditure: A Review of Techniques with Particular Reference to Doubly Labelled Water. Sports Med. 2003, 33, 683–698. [Google Scholar] [CrossRef]
- Rousset, S.; Guidoux, R.; Paris, L.; Farigon, N.; Miolanne, M.; Lahaye, C.; Duclos, M.; Boirie, Y.; Saboul, D. A Novel Smartphone Accelerometer Application for Low-Intensity Activity and Energy Expenditure Estimations in Overweight and Obese Adults. J. Med. Syst. 2017, 41, 117. [Google Scholar] [CrossRef]
- Duclos, M.; Fleury, G.; Guidoux, R.; Lacomme, P.; Lamaudiere, N.; Manenq, P.-H.; Paris, L.; Ren, L.; Rousset, S. Use of Smartphone Accelerometers and Signal Energy for Estimating Energy Expenditure in Daily-Living Conditions. Curr. Biotechnol. 2015, 4, 4–15. [Google Scholar] [CrossRef]
- Nightingale, T.E.; Walhim, J.-P.; Thompson, D.; Bilzon, J.L.J. Predicting Physical Activity Energy Expenditure in Manual Wheelchair Users. Med. Sci. Sports Exerc. 2014, 46, 1849–1858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Garciá-Massó, X.; Serra-Anõ, P.; Gonzalez, L.M.; Ye-Lin, Y.; Prats-Boluda, G.; Garcia-Casado, J. Identifying Physical Activity Type in Manual Wheelchair Users with Spinal Cord Injury by Means of Accelerometers. Spinal Cord 2015, 53, 772–777. [Google Scholar] [CrossRef] [PubMed]
- Nightingale, T.E.; Walhin, J.-P.; Thompson, D.; Bilzon, J.L.J. Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users. PLoS ONE 2015, 10, e0126086. [Google Scholar] [CrossRef]
- Maijers, M.C.; Verschuren, O.; Stolwijk-Swüste, J.M.; van Koppenhagen, C.F.; de Groot, S.; Post, M.W.M. Is Fitbit Charge 2 a Feasible Instrument to Monitor Daily Physical Activity and Handbike Training in Persons with Spinal Cord Injury? A Pilot Study. Spinal Cord Ser. Cases 2018, 4, 1–10. [Google Scholar] [CrossRef]
- Moreno, D.; Glasheen, E.; Domingo, A.; Panaligan, V.B.; Penaflor, T.; Rioveros, A.; Kressler, J. Validity of Caloric Expenditure Measured from a Wheelchair User Smartwatch. Int. J. Sports Med. 2020, 41, 505–511. [Google Scholar] [CrossRef] [PubMed]
- Glasheen, E.; Domingo, A.; Kressler, J. Accuracy of Apple Watch Fitness Tracker for Wheelchair Use Varies According to Movement Frequency and Task. Ann. Phys. Rehabil. Med. 2020, 101382. [Google Scholar] [CrossRef]
- Newzoo Global Mobile Market Report 2019|Light Version [Internet]. Newzoo. Available online: https://newzoo.com/insights/trend-reports/newzoo-global-mobile-market-report-2019-light-version/ (accessed on 9 February 2021).
- García-Massó, X.; Serra-Añó, P.; García-Raffi, L.M.; Sánchez-Pérez, E.A.; López-Pascual, J.; Gonzalez, L.M. Validation of the Use of Actigraph GT3X Accelerometers to Estimate Energy Expenditure in Full Time Manual Wheelchair Users with Spinal Cord Injury. Spinal Cord 2013, 51, 898–903. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Staudenmayer, J.; Pober, D.; Crouter, S.; Bassett, D.; Freedson, P. An Artificial Neural Network to Estimate Physical Activity Energy Expenditure and Identify Physical Activity Type from an Accelerometer. J. Appl. Physiol. 2009, 107, 1300–1307. [Google Scholar] [CrossRef] [PubMed]
- Preece, S.J.; Goulermas, J.Y.; Kenney, L.P.J.; Howard, D. A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities from Accelerometer Data. IEEE Trans. Biomed. Eng. 2009, 56, 871–879. [Google Scholar] [CrossRef] [PubMed]
- Hurd, W.J.; Morrow, M.M.; Kaufman, K.R. Tri-Axial Accelerometer Analysis Techniques for Evaluating Functional Use of the Extremities. J. Electromyogr. Kinesiol. 2013, 23, 924–929. [Google Scholar] [CrossRef] [Green Version]
- Teixeira, F.G.; Jesus, I.R.T.; Mello, R.G.T.; Nadal, J. Cross-Correlation between Head Acceleration and Stabilograms in Humans in Orthostatic Posture. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA, 28 August–1 September 2012; Volume 2012, pp. 3496–3499. [Google Scholar] [CrossRef]
- Catal, C.; Tufekci, S.; Pirmit, E.; Kocabag, G. On the Use of Ensemble of Classifiers for Accelerometer-Based Activity Recognition. Appl. Soft Comput. 2015, 37, 1018–1022. [Google Scholar] [CrossRef]
- Hawkins, D.M. The Problem of Overfitting. J. Chem. Inf. Comput. Sci. 2004, 44, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Shwetar, Y.J.; Veerubhotla, A.L.; Huang, Z.; Ding, D. Comparative Validity of Energy Expenditure Prediction Algorithms Using Wearable Devices for People with Spinal Cord Injury. Spinal Cord 2020, 1–10. [Google Scholar] [CrossRef]
- Nightingale, T.E.; Rouse, P.C.; Thompson, D.; Bilzon, J.L.J. Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions. Sports Med. Open 2017, 3, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- del Rosario, M.B.; Redmond, S.J.; Lovell, N.H. Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement. Sensors 2015, 15, 18901–18933. [Google Scholar] [CrossRef] [Green Version]
- Fu, J.; Jones, M.; Liu, T.; Hao, W.; Yan, Y.; Qian, G.; Jan, Y.-K. A Novel Mobile-Cloud System for Capturing and Analyzing Wheelchair Maneuvering Data: A Pilot Study. Assist. Technol. 2016, 28, 105–114. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fu, J.; Liu, T.; Jones, M.; Qian, G.; Jan, Y.-K. Characterization of Wheelchair Maneuvers Based on Noisy Inertial Sensor Data: A Preliminary Study. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014, 2014, 1731–1734. [Google Scholar] [CrossRef] [Green Version]
- Jee, H. Review of Researches on Smartphone Applications for Physical Activity Promotion in Healthy Adults. J. Exerc. Rehabil. 2017, 13, 3–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Order | Activity | Type | Description |
---|---|---|---|
1 | Lying down | Sedentary | Participants are required to lie in the lateral decubitus position on a stretcher. |
2 | Watching TV | Sedentary | Participants are required to sit on their wheelchair and watch TV programs. |
3 | Working on a computer * | Sedentary | Participants are required to transcribe a text from a news website into a word processing document. |
4 | Moving items * | Housework | Participants are required to move boxes of different weights (1, 2, and 3 kg) from a shelf on one side of the laboratory to a shelf on the opposite side of the laboratory. |
5 | Mopping the floor * | Housework | Participants are required to mop the floor of the laboratory at a self-paced speed. |
6 | Cleaning the windows * | Housework | Participants are required to wipe the windows of the laboratory with a piece of cloth. |
7 | Ironing * | Housework | Participants are required to iron a set of t-shirts with an iron over an ironing board. |
8 | Arm-ergometry exercise * | Locomotion | Participants are required to crank an arm ergometer with an intensity that would correspond to a perception of eight points on the OMNI-Res perception scale. |
9 | Slow propulsion | Locomotion | Participants are required to propel their wheelchair at a comfortable self-selected speed along a long corridor. |
10 | Fast propulsion | Locomotion | Participants are required to propel their wheelchair at a fast self-selected speed along a long corridor. |
Models | Equation | Dataset | Correlation | Mean Square Error (mL·kg−1·min−1)2 | Mean Absolute Error (mL·kg−1·min−1) |
---|---|---|---|---|---|
All variables | VO2 = 3.4921 + 10.784RV75–25 − 25.4524YVAR + 21.0447YSD | Training | 0.72 | 6.08 | 1.76 |
Validation | 0.72 | 6.16 | 1.76 | ||
Linear variables | VO2 = 3.4921 + 10.7083RV75–25 − 25.4524YVAR + 21.04487YSD | Training | 0.72 | 6.08 | 1.76 |
Validation | 0.72 | 6.16 | 1.76 | ||
Non-linear variables | VO2 = −343.0891 + 503.1303RVDYN + 1.6797RVND1 − 156.1103YDYN | Training | 0.71 | 6.42 | 1.85 |
Validation | 0.71 | 6.48 | 1.85 |
All Variables | Linear Variables | Non-Linear Variables | ||||
---|---|---|---|---|---|---|
Mean Squared Error | Mean Absolute Error | Mean Squared Error | Mean Absolute Error | Mean Squared Error | Mean Absolute Error | |
Lying down | 9% | 23% | 9% | 23% | 9% | 24% |
Watching TV | 11% | 26% | 11% | 26% | 11% | 30% |
Working on a computer | 7% | 22% | 7% | 22% | 11% | 26% |
Moving items | 6% | 19% | 6% | 19% | 8% | 21% |
Mopping the floor | 5% | 18% | 5% | 18% | 5% | 17% |
Cleaning the windows | 9% | 24% | 9% | 24% | 8% | 22% |
Ironing | 7% | 20% | 7% | 20% | 8% | 21% |
Arm-ergometry exercise | 11% | 26% | 11% | 26% | 14% | 32% |
Slow propulsion | 10% | 27% | 10% | 27% | 9% | 25% |
Fast propulsion | 7% | 19% | 7% | 19% | 7% | 21% |
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Marco-Ahulló, A.; Montesinos-Magraner, L.; Gonzalez, L.-M.; Llorens, R.; Segura-Navarro, X.; García-Massó, X. Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury. Sensors 2021, 21, 1498. https://doi.org/10.3390/s21041498
Marco-Ahulló A, Montesinos-Magraner L, Gonzalez L-M, Llorens R, Segura-Navarro X, García-Massó X. Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury. Sensors. 2021; 21(4):1498. https://doi.org/10.3390/s21041498
Chicago/Turabian StyleMarco-Ahulló, Adrià, Lluïsa Montesinos-Magraner, Luis-Millán Gonzalez, Roberto Llorens, Xurxo Segura-Navarro, and Xavier García-Massó. 2021. "Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury" Sensors 21, no. 4: 1498. https://doi.org/10.3390/s21041498