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

Hiremath, Shivayogi V.; Intille, Stephen; Kelleher, Annmarie; Cooper, Rory A.

doi:10.1016/j.apmr.2016.02.016

Cited by 21 publications

(20 citation statements)

References 26 publications

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“…In general, the overall MAE is in the range of other accelerometer or IMU based models developed for the SCI population although those studies included fewer activities ( 21 , 22 , 42 ). Recently, Hiremath and coworkers presented an EE estimation model which was developed using recordings from 20 different activities, achieving a MAE of 25% ( 23 ). A possible explanation for the different results might be that Hiremath and coworkers used linear regression models, which do not account for non-linear relationships between accelerometer measurements and EE ( 23 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Hiremath and coworkers presented an EE estimation model which was developed using recordings from 20 different activities, achieving a MAE of 25% ( 23 ). A possible explanation for the different results might be that Hiremath and coworkers used linear regression models, which do not account for non-linear relationships between accelerometer measurements and EE ( 23 ). MAE in the present study was always highest for the “high intensity” class, which can be explained by the fact that weight-loading activities, such as the handbike ergometer and weight lifting, have been included in this class.…”

Section: Discussionmentioning

confidence: 99%

“…Although accelerometer-based EE estimation models were developed for subjects with SCI using a manual wheelchair ( 21 , 22 ), combining accelerometer and demographic variables, these studies focused on a restricted set of activities and were only validated in a semi-structured environment. In a more recent study, linear regression models for the estimation of EE became available based on recordings of 20 different activities, with a mean absolute error (MAE) of around 25% ( 23 ). Using methods that are more sophisticated the quality of the EE estimation may be improved.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units

et al. 2018

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A healthy lifestyle reduces the risk of cardio-vascular disease. As wheelchair-bound individuals with spinal cord injury (SCI) are challenged in their activities, promoting and coaching an active lifestyle is especially relevant. Although there are many commercial activity trackers available for the able-bodied population, including those providing feedback about energy expenditure (EE), activity trackers for the SCI population are largely lacking, or are limited to a small set of activities performed in controlled settings. The aims of the present study were to develop and validate an algorithm based on inertial measurement unit (IMU) data to continuously monitor EE in wheelchair-bound individuals with a SCI, and to establish reference activity values for a healthy lifestyle in this population. For this purpose, EE was measured in 30 subjects each wearing four IMUs during 12 different physical activities, randomly selected from a list of 24 activities of daily living. The proposed algorithm consists of three parts: resting EE estimation based on multi-linear regression, an activity classification using a k-nearest-neighbors algorithm, and EE estimation based on artificial neural networks (ANNs). The mean absolute estimation error for the ANN-based algorithm was 14.4% compared to indirect calorimeter measurements. Based on reference values from the literature and the data collected within this study, we recommend wheeling 3 km per day for a healthy lifestyle in wheelchair-bound SCI individuals. Combining the proposed algorithm with a recommendation for physical activity provides a powerful tool for the promotion of an active lifestyle in the SCI population, thereby reducing the risk for secondary diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units

et al. 2018

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“…Research evidence suggests a strong relationship between wrist acceleration and physical activity and energy expenditure in free-living adults [38]. Accelerometer device worn on the upper arm or on the wrist can also assist manual wheelchair users, including individuals with SCI, to estimate physical activity and energy expenditure [39,40]. Based on wrist actigraphy measures, we found no significant differences in parameters of physical activity (M10 and L5) between SCI and healthy individuals, supporting the use of actigraphy in individuals with SCI below Th2.…”

Section: Physical Activity Pain and Sleep Qualitymentioning

confidence: 99%

Actigraphy-based evaluation of sleep quality and physical activity in individuals with spinal cord injury

Albu

Umemura

Forner-Cordero

2019

Spinal Cord Ser Cases

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Study design Cross-sectional study. Background Sleep disturbances are frequently reported by individuals with spinal cord injury (SCI) and are associated both with poor quality of life and reduced ability to participate in rehabilitation and daily life activities.Objectives This study investigated sleep quality based on self-reports and actigraphy in individuals with SCI as compared to able-bodied. We also explored the relationship between sleep quality, physical activity, and neuropathic pain. Setting Institute Guttmann, Neurorehabilitation Hospital, Badalona, Barcelona, Spain. Methods Fourteen SCI patients (12 males, 43.10 ± 10.59 y.o.) and 10 healthy individuals (7 males, mean age 46.21 ± 12.58 y.o.) were enrolled in the study. Participants wore wrist actigraphs for 7 consecutive days to characterize their sleep-wake cycle, rest-activity circadian rhythm and physical activity. Sleep quality, chronotype, daytime sleepiness, neuropathic pain severity and interference were assessed based on questionnaires. Results SCI individuals reported poorer sleep quality compared to healthy individuals. Actigraphy-based sleep measurements revealed that patients woke up later, spent more time in bed and slept longer compared to the healthy controls but did not differ significantly in the estimated sleep efficacy and number of awakenings from the able-bodied controls. In individuals with SCI greater physical activity predicted higher sleep efficacy and less awakening episodes as well as shorter sleep latency and lower sleep disturbance. Conclusions The actigraphy-based sleep estimates indicate that patients with SCI spent more time in bed and slept longer but their sleep efficacy was similar to able-bodied controls. Maintaining regular physical activity could improve pain control and sleep quality.

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“…Furthermore, Hiremath et al assessed the physical activity of manual wheelchair users with a gyroscope-based wheel rotation monitor and an arm accelerometer or a wrist accelerometer. In their study, the average estimation errors were −9.8 ± 37.0% for the arm and −5.7 ± 32.6% for the wrist 18) . In our study, the absolute estimation errors ranged from 1.5 to 20.5% and from 0.6 to 11.4% for the right wrist and right upper arm, respectively.…”

Section: Discussionmentioning

confidence: 91%

Assessment of daily physical activities with sensors attached to the upper limbs in healthy adults using a manual wheelchair

Takigawa

Inayama

Yamada

et al. 2018

JPFSM

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This study examined the energy expenditure (EE) of healthy adults during typical use of a manual wheelchair by attaching sensors to the subjects' upper limbs. The aim was to determine whether the measured EE values depend on the sensor attachment site and whether the addition of angular velocity information to the acceleration value was advantageous to the EE assessment. Subjects were 11 males and 10 females. Their wrists and mid-upper arms were fitted with sensors to monitor their daily physical activities. Triaxial acceleration, triaxial angular velocity, and EE were measured while performing activities with a manual wheelchair. Coefficients of determination for estimating EE at each sensor location ranged from 0.66 to 0.79 (based on gender, the calculated three axis value of acceleration and angular velocity) and from 0.65 to 0.78 (based on gender and the calculated three axis value of acceleration, without angular velocity). Furthermore, angular velocity was not selected as a significant explanatory variable for estimating EE at the wrist. The average percent error for estimating the EE of daily physical activity in healthy adults using a manual wheelchair and factoring in gender and the calculated three axis value of acceleration at each sensor location ranged from 5.2 to 7.2%. Angular velocity information added to the calculated three axis value of acceleration at the upper arms slightly improved the estimation of EE. In addition, it was found that there was no difference in the assessment at different sensor locations.

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

Cited by 21 publications

References 26 publications

Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units

Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units

Actigraphy-based evaluation of sleep quality and physical activity in individuals with spinal cord injury

Assessment of daily physical activities with sensors attached to the upper limbs in healthy adults using a manual wheelchair

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