Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury

This study aimed to investigate the validity of using built-in smartphone accelerometers to estimate the active energy expenditures of full-time manual wheelchair users with spinal cord injury (SCI). Twenty participants with complete SCI completed 10 5-min daily activities that involved the upper limbs, during which their oxygen consumption and upper limb activity were registered using a portable gas analyzer and a smartphone (placed on the non-dominant arm), respectively. Time series of 1-min averaged oxygen consumption and 55 accelerometer variables (13 variables for each of the four axes and three additional variables for the correlations between axes) were used to estimate three multiple linear models, using a 10-fold cross-validation method. The results showed that models that included either all variables and models or that only included the linear variables showed comparable performance, with a correlation of 0.72. Slightly worse general performance was demonstrated by the model that only included non-linear variables, although it proved to be more accurate at estimating the energy expenditures (EE) during specific tasks. These results suggest that smartphones could be a promising low-cost alternative to laboratory-grade accelerometers to estimate the energy expenditure of wheelchair users with spinal cord injury during daily activities.

[1]  Tao Liu,et al.  Characterization of wheelchair maneuvers based on noisy inertial sensor data: A preliminary study , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Tao Liu,et al.  A novel mobile-cloud system for capturing and analyzing wheelchair maneuvering data: A pilot study , 2016, Assistive technology : the official journal of RESNA.

[3]  R. Davis,et al.  Spasticity following spinal cord injury. , 1975, Clinical orthopaedics and related research.

[4]  Nigel H. Lovell,et al.  Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement , 2015, Sensors.

[5]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[6]  P. London Injury , 1969, Definitions.

[7]  D. Wolfe,et al.  The development of evidence-informed physical activity guidelines for adults with spinal cord injury , 2011, Spinal Cord.

[8]  J. Toca-Herrera,et al.  Force Normalization in Paraplegics , 2012, International Journal of Sports Medicine.

[9]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[10]  Paula M Ludewig,et al.  Effectiveness of home exercise on pain, function, and strength of manual wheelchair users with spinal cord injury: a high-dose shoulder program with telerehabilitation. , 2014, Archives of physical medicine and rehabilitation.

[11]  Lora Giangregorio,et al.  Bone Loss and Muscle Atrophy in Spinal Cord Injury: Epidemiology, Fracture Prediction, and Rehabilitation Strategies , 2006, The journal of spinal cord medicine.

[12]  D. Ding,et al.  COMPARATIVE VALIDITY OF ENERGY EXPENDITURE PREDICTION ALGORITHMS USING WEARABLE DEVICES FOR PEOPLE WITH SPINAL CORD INJURY , 2020, Spinal Cord.

[13]  A. Domingo,et al.  Validity of Caloric Expenditure Measured from a Wheelchair User Smartwatch , 2020, International Journal of Sports Medicine.

[14]  T. Nightingale,et al.  Predicting physical activity energy expenditure in manual wheelchair users. , 2014, Medicine and science in sports and exercise.

[15]  X. García-Massó,et al.  Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury , 2013, Spinal Cord.

[16]  I. Hickman,et al.  Investigation of measured and predicted resting energy needs in adults after spinal cord injury: a systematic review , 2015, Spinal Cord.

[17]  A. Domingo,et al.  Accuracy of Apple Watch fitness tracker for wheelchair use varies according to movement frequency and task. , 2020, Annals of physical and rehabilitation medicine.

[18]  Damien Saboul,et al.  A Novel Smartphone Accelerometer Application for Low-Intensity Activity and Energy Expenditure Estimations in Overweight and Obese Adults , 2017, Journal of Medical Systems.

[19]  SHAOPENG LIU,et al.  Computational methods for estimating energy expenditure in human physical activities. , 2012, Medicine and science in sports and exercise.

[20]  Haemi Jee,et al.  Review of researches on smartphone applications for physical activity promotion in healthy adults , 2017, Journal of exercise rehabilitation.

[21]  John Staudenmayer,et al.  An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. , 2009, Journal of applied physiology.

[22]  W. E. Langbein,et al.  Energy cost of physical activities in persons with spinal cord injury. , 2010, Medicine and science in sports and exercise.

[23]  A. Jain ISCOS - Textbook on comprehensive management of spinal cord injuries , 2016, Indian Journal of Orthopaedics.

[24]  Jurandir Nadal,et al.  Cross-correlation between head acceleration and stabilograms in humans in orthostatic posture , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Cagatay Catal,et al.  On the use of ensemble of classifiers for accelerometer-based activity recognition , 2015, Appl. Soft Comput..

[26]  Dan Ding,et al.  Validity of activity monitors in wheelchair users: A systematic review. , 2016, Journal of rehabilitation research and development.

[27]  X. García-Massó,et al.  The influence of regular physical activity on lung function in paraplegic people , 2016, Spinal Cord.

[28]  R. Shephard,et al.  Spinal Cord Injury, Exercise and Quality of Life , 1995, Sports medicine.

[29]  X. García-Massó,et al.  Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers , 2015, Spinal Cord.

[30]  Libo Ren,et al.  Use of Smartphone Accelerometers and Signal Energy for Estimating Energy Expenditure in Daily-Living Conditions , 2015 .

[31]  Dylan Thompson,et al.  Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users , 2015, PloS one.

[32]  James L. J. Bilzon,et al.  Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions , 2017, Sports Medicine - Open.

[33]  O. Verschuren,et al.  Is Fitbit Charge 2 a feasible instrument to monitor daily physical activity and handbike training in persons with spinal cord injury? A pilot study , 2018, Spinal Cord Series and Cases.

[34]  P Calmels,et al.  [Training programs in spinal cord injury]. , 2005, Annales de readaptation et de medecine physique : revue scientifique de la Societe francaise de reeducation fonctionnelle de readaptation et de medecine physique.

[35]  R. Gosselink,et al.  Respiratory muscle training in persons with spinal cord injury: a systematic review. , 2006, Respiratory medicine.

[36]  Kenton R Kaufman,et al.  Tri-axial accelerometer analysis techniques for evaluating functional use of the extremities. , 2013, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[37]  X. García-Massó,et al.  Comorbidity and physical activity in people with paraplegia: a descriptive cross-sectional study , 2017, Spinal Cord.