Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs

Technologies measuring manual wheelchair propulsion metrics: A scoping review

The aim of this review is to investigate existing and developing technologies assessing metrics of manual wheelchair propulsion. A scoping review of scientific and gray literature was performed. Five databases were searched - Medline, Scopus, CINAHL, Institute of Electrical and Electronics Engineers (IEEE), and Embase. The 38 retained articles identified 27 devices categorized into accelerometers, wheelchair-mounted devices, instrumented wheels, and wearables. The devices included in this review can be used by manual wheelchair users to monitor propulsion effort and activity goals, by clinicians to assess rehabilitation programs, and to inform and guide future research. The findings support a need for further research into the development of custom algorithms for manual wheelchair user populations as well as further validation in broader free-living environments with equitable participant populations.

Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling-A Review

More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and the optimal combination differs between sports and depends on the intended aim, determining the best setup for a given sport can be challenging. This review aims to provide an overview and discussion of the present methods to estimate in-field mechanical power in different cyclical sports. Overall, in-field mechanical power estimation can be complex, such that methods are often simplified to improve feasibility. For example, for some sports, power meters exist that use the main propulsive force for mechanical power estimation. Another non-invasive method usable for in-field mechanical power estimation is the use of inertial measurement units (IMUs). These wearable sensors can either be used as stand-alone approach or in combination with force sensors. However, every method has consequences for interpretation of power values. Based on the findings of this review, recommendations for mechanical power measurement and interpretation in kayaking, rowing, wheelchair propulsion, speed skating, and cross-country skiing are done.

Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning

Background

Calorimetry is both expensive and obtrusive but provides the only way to accurately measure energy expenditure in daily living activities of any specific person, as different people can use different amounts of energy despite performing the same actions in the same manner. Deep learning video analysis techniques have traditionally required a lot of data to train; however, recent advances in few-shot learning, where only a few training examples are necessary, have made developing personalized models without a calorimeter a possibility.

Objective

The primary aim of this study is to determine which activities are most well suited to calibrate a vision-based personalized deep learning calorie estimation system for daily living activities.

Methods

The SPHERE (Sensor Platform for Healthcare in a Residential Environment) Calorie data set is used, which features 10 participants performing 11 daily living activities totaling 4.5 hours of footage. Calorimeter and video data are available for all recordings. A deep learning method is used to regress calorie predictions from video.

Results

Models are personalized with 32 seconds from all 11 actions in the data set, and mean square error (MSE) is taken against a calorimeter ground truth. The best single action for calibration is wipe (1.40 MSE). The best pair of actions are sweep and sit (1.09 MSE). This compares favorably to using a whole 30-minute sequence containing 11 actions to calibrate (1.06 MSE).

Conclusions

A vision-based deep learning energy expenditure estimation system for a wide range of daily living activities can be calibrated to a specific person with footage and calorimeter data from 32 seconds of sweeping and 32 seconds of sitting.

Predictive factors of peak aerobic capacity using simple measurements of anthropometry and musculoskeletal fitness in paraplegic men

BACKGROUND

Protocols for evaluating cardiovascular fitness for wheelchair-dependent persons are still scarcely accessible due to the requirements such as specialized equipment and expertise in community or public healthcare settings. This study aimed to assess the potential of secondary utilization of musculoskeletal fitness components for estimating peak oxygen uptake (VO2peak) in paraplegic men.

METHODS

Thirty-three paraplegic men (T1-L3) aged 23-63 years underwent anthropometry (height, weight and calculated body mass index and body surface area) and upper-body musculoskeletal fitness tests (back-scratch, arm-curls and handgrip strength tests) and performed a graded exercise test with an arm-crank ergometer on two non-consecutive days. To determine the relationship between VO2peak and various kinanthropometric parameters and derive a regression model that predicts the VO2peak, uni- and multivariate analyses were conducted, respectively.

RESULTS

Maximal numbers of arm curls on either arm (r=0.486, P=0.004) and the back-scratch distance (r=0.426, P=0.013) were moderately correlated with VO2peak. Moreover, among the indices of handgrip strength, average handgrip strength of both hands divided by the body surface area showed a strong correlation coefficient with VO2peak (r=0.674, P<0.001). Multivariate linear regression analysis indicated that muscular endurance and strength were the main predictors for estimating VO2peak. Considering shoulder flexibility, age, and anthropometric variables, the regression model showed the highest adjusted R2 of 0.811 and lowest standard error of estimate of 3.54 mL·kg-1·min-1 (P<0.001). The Bland-Altman plots indicated good agreement between actual and estimated VO2peak. The mean absolute prediction error was 11.9%.

CONCLUSIONS

Musculoskeletal fitness and anthropometric components may be predictive factors of a new conceptual modality estimating concomitant cardiorespiratory fitness beyond their traditional health-related indications.

Validity of activity monitors in wheelchair users: A systematic review

Assessing physical activity (PA) in manual wheelchair users (MWUs) is challenging because of their different movement patterns in comparison to the ambulatory population. The aim of this review was to investigate the validity of portable monitors in quantifying PA in MWUs. A systematic literature search was performed. The data source was full reports of validation and evaluation studies in peer-reviewed journals and conference proceedings. Eligible articles between January 1, 1999, and September 18, 2015, were identified in three databases: PubMed, Institute of Electrical and Electronics Engineers, and Scopus. A total of 164 articles (158 from the databases and 6 from the citation/reference tracking) were identified, and 29 met the eligibility criteria. Two investigators independently extracted the characteristics from each selected article following a predetermined protocol and completed seven summary tables describing the study characteristics and key outcomes. In the identified studies, the monitors were used to assess three types of PA measures: energy cost, user movement, and wheelchair movement. The customized algorithms/monitors did not estimate energy cost in MWUs as well as the commercial monitors did in the ambulatory population; however, they showed fair accuracy in measuring both wheelchair and user movements.

Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions

Accurately measuring physical activity and energy expenditure in persons with chronic physical disabilities who use wheelchairs is a considerable and ongoing challenge. Quantifying various free-living lifestyle behaviours in this group is at present restricted by our understanding of appropriate measurement tools and analytical techniques. This review provides a detailed evaluation of the currently available measurement tools used to predict physical activity and energy expenditure in persons who use wheelchairs. It also outlines numerous considerations specific to this population and suggests suitable future directions for the field. Of the existing three self-report methods utilised in this population, the 3-day Physical Activity Recall Assessment for People with Spinal Cord Injury (PARA-SCI) telephone interview demonstrates the best reliability and validity. However, the complexity of interview administration and potential for recall bias are notable limitations. Objective measurement tools, which overcome such considerations, have been validated using controlled laboratory protocols. These have consistently demonstrated the arm or wrist as the most suitable anatomical location to wear accelerometers. Yet, more complex data analysis methodologies may be necessary to further improve energy expenditure prediction for more intricate movements or behaviours. Multi-sensor devices that incorporate physiological signals and acceleration have recently been adapted for persons who use wheelchairs. Population specific algorithms offer considerable improvements in energy expenditure prediction accuracy. This review highlights the progress in the field and aims to encourage the wider scientific community to develop innovative solutions to accurately quantify physical activity in this population.

Estimation of Energy Expenditure for Wheelchair Users Using a Physical Activity Monitoring System

OBJECTIVE

To develop and evaluate energy expenditure (EE) estimation models for a physical activity monitoring system (PAMS) in manual wheelchair users with spinal cord injury (SCI).

DESIGN

Cross-sectional study.

SETTING

University-based laboratory environment, a semistructured environment at the National Veterans Wheelchair Games, and the participants' home environments.

PARTICIPANTS

Volunteer sample of manual wheelchair users with SCI (N=45).

INTERVENTION

Participants were asked to perform 10 physical activities (PAs) of various intensities from a list. The PAMS consists of a gyroscope-based wheel rotation monitor (G-WRM) and an accelerometer device worn on the upper arm or on the wrist. Criterion EE using a portable metabolic cart and raw sensor data from PAMS were collected during each of these activities.

MAIN OUTCOME MEASURES

Estimated EE using custom models for manual wheelchair users based on either the G-WRM and arm accelerometer (PAMS-Arm) or the G-WRM and wrist accelerometer (PAMS-Wrist).

RESULTS

EE estimation performance for the PAMS-Arm (average error ± SD: -9.82%±37.03%) and PAMS-Wrist (-5.65%±32.61%) on the validation dataset indicated that both PAMS-Arm and PAMS-Wrist were able to estimate EE for a range of PAs with <10% error. Moderate to high intraclass correlation coefficients (ICCs) indicated that the EE estimated by PAMS-Arm (ICC3,1=.82, P<.05) and PAMS-Wrist (ICC3,1=.89, P<.05) are consistent with the criterion EE.

CONCLUSIONS

Availability of PA monitors can assist wheelchair users to track PA levels, leading toward a healthier lifestyle. The new models we developed can estimate PA levels in manual wheelchair users with SCI in laboratory and community settings.

Predicting physical activity energy expenditure in wheelchair users with a multisensor device

Aim

To assess the error in predicting physical activity energy expenditure (PAEE), using a multisensor device in wheelchair users, and to examine the efficacy of using an individual heart rate calibration (IC) method.

Methods

15 manual wheelchair users (36±10 years, 72±11 kg) completed 10 activities: resting, folding clothes, wheelchair propulsion on a 1% gradient (3456 and 7 km/h) and propulsion at 4 km/h (with an additional 8% of body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. Criterion PAEE was measured using a computerised indirect calorimetry system. Participants wore a combined accelerometer and heart rate monitor (Actiheart). They also performed an incremental arm crank ergometry test to exhaustion which permitted retrospective individual calibration of the Actiheart for the activity protocol. Linear regression analysis was conducted between criterion (indirect calorimetry) and estimated PAEE from the Actiheart using the manufacturer's proprietary algorithms (group calibration, GC) or IC. Bland-Altman plots were used and mean absolute error was calculated to assess the agreement between criterion values and estimated PAEE.

Results

Predicted PAEE was significantly (p<0.01) correlated with criterion PAEE (GC, r=0.76 and IC, r=0.95). The absolute bias ±95% limits of agreement were 0.51±3.75 and −0.22±0.96 kcal/min for GC and IC, respectively. Mean absolute errors across the activity protocol were 51.4±38.9% using GC and 16.8±15.8% using IC.

Summary

PAEE can be accurately and precisely estimated using a combined accelerometer and heart rate monitor device, with integration of an IC. Interindividual variance in cardiovascular function and response to exercise is high in this population. Therefore, in manual wheelchair users, we advocate the use of an IC when using the Actiheart to predict PAEE.

Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users

PURPOSE

To assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs).

METHODS

Seventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1) and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.

RESULTS

Accelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.

CONCLUSION

The results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.