Estimation of energy expenditure in adults with accelerometry and heart rate

Bazuelo-Ruiz, Bruno; Rosario, Helios De; Durá-Gil, Juan V.

doi:10.1016/j.scispo.2021.08.007

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…The metric of MAD has been validated in multiple studies, e.g., Bazuelo-Ruiz [ 34 ] found strong associations of MAD with indirect calorimetry (r = 0.94). Additionally, the Huawei smartwatch has a reliable and sensitive acceleration sensor, which we were able to show in our previous study about kinematic analyses of ADL [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study

Schmidle

Gulde

Koster³

et al. 2023

BMC Geriatr

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Background The decline in everyday life physical activity reflects and contributes to the frailty syndrome. While especially self-reported frailty assessments have the advantage of reaching large groups at low costs, little is known about the relationship between the self-report and objective measured daily physical activity behavior. The main objective was to evaluate whether and to what extent a self-reported assessment of frailty is associated with daily physical activity patterns. Methods Daily activity data were obtained from 88 elderly participants (mean 80.6 ± 9.1 years) over up to 21 days. Acceleration data were collected via smartwatch. According to the results of a self-report frailty questionnaire, participants were retrospectively split up into three groups, F (frail, n = 43), P (pre-frail, n = 33), and R (robust, n = 12). Gait- and activity-related measures were derived from the built-in step detector and acceleration sensor and comprised, i.a., standard deviation of 5-s-mean amplitude deviation (MADstd), median MAD (MADmedian), and the 95th percentile of cadence (STEP95). Parameters were fed into a PCA and component scores were used to derive behavioral clusters. Results The PCA suggested two components, one describing gait and one upper limb activity. Mainly gait related parameters showed meaningful associations with the self-reported frailty score (STEP95: R2 = 0.25), while measures of upper limb activity had lower coefficients (MADmedian: R2 = 0.07). Cluster analysis revealed two clusters with low and relatively high activity in both dimensions (cluster 2 and 3). Interestingly, a third cluster (cluster 1) was characterized by high activity and low extent of ambulation. Comparisons between the clusters showed significant differences between activity, gait, age, sex, number of chronic diseases, health status, and walking aid. Particularly, cluster 1 contained a higher number of female participants, whose self-reports tended towards a low health status, the frequent use of a walking aid, and a higher score related to frailty questions. Conclusions The results demonstrate that subjective frailty assessments may be a simple first screening approach. However, especially older women using walking aids may classify themselves as frail despite still being active. Therefore, the results of self-reports may be particularly biased in older women.

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Section: Methodsmentioning

confidence: 99%

The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study

Schmidle

Gulde

Koster³

et al. 2023

BMC Geriatr

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“…We implemented the trained regression model in a SmartOs’ CCU board (C++, Ubuntu mate) to estimate the metabolic cost in real time every 10 s. This time window guaranteed at least two respirations and was considered adequate to estimate the instantaneous metabolic cost [ 29 ]. For this purpose, the system executes the following steps in real time: (i) measuring the 3D acceleration signals of the four mentioned on-body locations; (ii) filtering these signals with a 4th-order Butterworth low-pass filter at 20 Hz [ 22 , 24 , 25 ], which offers a good trade-off between signal attenuation and preservation of relevant characteristics; (iii) reorganizing the data into 10-second windows [ 30 ]; (iv) creating the feature vector by computing the mean absolute deviation (MAD) of each variable [ 31 ]; (v) normalizing each feature using the z-score method; and (vi) using the previously trained EGPR regressor to estimate the metabolic cost based on the input features. Figure 2 presents the sequence of the pre-processing methods, which was established based on the results of our previous study [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Human-in-the-Loop Optimization of Knee Exoskeleton Assistance for Minimizing User’s Metabolic and Muscular Effort

Monteiro,

Figueiredo,

Fonseca

et al. 2024

Sensors

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Lower limb exoskeletons have the potential to mitigate work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls adapt an exoskeleton’s assistance in real time, to optimize the user–exoskeleton interaction. This study presents a HITL control for a knee exoskeleton using a CMA-ES algorithm to minimize the users’ physical effort, a parameter innovatively evaluated using the interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user’s metabolic cost within the HITL control through a machine-learning model. The regression model estimated the metabolic cost, in real time, with a root mean squared error of 0.66 W/kg and mean absolute percentage error of 26% (n = 5), making faster (10 s) and less noisy estimations than a respirometer (K5, Cosmed). The HITL reduced the user’s metabolic cost by 7.3% and 5.9% compared to the zero-torque and no-device conditions, respectively, and reduced the interaction torque by 32.3% compared to a zero-torque control (n = 1). The developed HITL control surpassed a non-exoskeleton and zero-torque condition regarding the user’s physical effort, even for a task such as slow walking. Furthermore, the user-specific control had a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential of HITL controls in assisted walking.

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“…This time window guarantees at least two respirations and can be considered adequate to estimate the instantaneous metabolic cost [27]. For this purpose, the system executes the following steps in real-time: i) measuring the 3D acceleration signals of the four mentioned on-body locations; ii) filtering these signals with a 4 th order Butterworth low-pass filter of 20Hz [23]; iii) reorganizing the data into 10-second windows [28]; iv) creating the feature vector by computing the mean absolute deviation (MAD) of each variable [29]; (v) normalizing each feature using the z-score method; and (vi) using the previously trained EGPR regressor to estimate the metabolic cost based on the input features. Figure 2 presents the sequence of all the pre-processing methods, which were established based on the results of our previous study [27].…”

Section: Metabolic Cost Estimationmentioning

confidence: 99%

Human-In-The-Loop Optimization Of Knee Exoskeleton Assistance For Minimizing User’s Metabolic And Muscular Effort

Monteiro,

Figueiredo,

Fonseca

et al. 2024

Preprint

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Lower limb exoskeletons have the potential to reduce the prevalence of work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls can adapt an exoskeleton’s assistance in real-time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton to minimize the users’ physical effort, a parameter innovatively evaluated by their interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user’s metabolic cost within the HITL control through a machine-learning model. The regression model was able to estimate the metabolic cost, in real-time, with a root-mean-squared error of 0.66 W/kg and a mean absolute percentage error of 26%, making faster (10s) and less noisy estimations than a respirometer (K5, Cosmed) device. The HITL reduced the user’s metabolic cost and interaction torque by 7.3% and 32.3%, respectively, when compared to a zero-torque control. The developed HITL control surpassed both a non-exoskeleton and a zero-torque condition regarding the user’s physical effort, even for a simple task such as slow walking. Furthermore, the user-specific control enabled a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential for HITL controls in assisted working.

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Estimation of energy expenditure in adults with accelerometry and heart rate

Cited by 4 publications

References 28 publications

The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study

The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study

Human-in-the-Loop Optimization of Knee Exoskeleton Assistance for Minimizing User’s Metabolic and Muscular Effort

Human-In-The-Loop Optimization Of Knee Exoskeleton Assistance For Minimizing User’s Metabolic And Muscular Effort

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