Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.Įlectronic pedometers are relatively inexpensive devices to monitor physical activity. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2).

Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR).

The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. Wearable technologies are known to improve our quality of life. These initial empirical findings should be further refined to ultimately establish harmonized best practices for validating wearable technologies. Eleven studies provided MAPE values at treadmill speeds of 1.1 to 1.8 m/s their weighted median MAPE values were 7% to 11% for wrist-worn, 1% to 4% for waist-worn, and ≤1% for thigh-worn devices.ĭespite divergent study methodologies, the authors identified common practices and summarized MAPE values representing device step-count accuracy during treadmill walking. Seventy-seven eligible studies were identified: most had samples averaging 54% (SD = 5%) female and 27 (5) years of age, treadmill protocols consisting of 3 to 5 bouts at speeds of 0.8 (0.1) to 1.6 (0.2) m/s, and reported measures of bias. Weighted median MAPE values were calculated by wear location and a 0.2-m/s speed increment. Where reported, speed- and wear location-specific mean absolute percentage error (MAPE) values were tabulated. The authors searched PubMed and SPORTDiscus in August 2019 to identify treadmill-based validation studies that employed the criterion of directly observed (including video recorded) steps and cataloged study sample characteristics, protocol details, and analytical procedures. To catalog studies validating step-counting wearable technologies during treadmill ambulation. The authors conducted a scoping review as a first step toward establishing harmonized (ie, consistent and compatible), empirically based best practices for validating step-counting wearable technologies. These results provide the basis for defining moderate-intensity physical activity based on energy expenditure and step counts and may lead to an appropriate steps/day recommendation. Males met 3 criteria and females met 2 criteria for moderate-intensity physical activity at SF of 110 to 120 steps/min. The pedometers' inability to accurately estimate energy expenditure cannot be attributed to stride length entered into the pedometer or its ability to measure step counts. Compared with energy expenditure as measured by a metabolic cart, the pedometer significantly underestimated energy expenditure at 80 steps/min and significantly overestimated measured energy expenditure at 90, 100, 110, and 120 steps/min.

The pedometer accurately measured step counts at SFs of 100, 110, and 120 steps/min, but not 80 and 90 steps/min. One hundred males (n = 50) and females (n = 50) walked at stride frequencies (SF) of 80, 90, 100, 110, and 120 steps/min, during which time step counts and energy expenditure were estimated with a Walk4Life Elite pedometer. The purposes of this study were to determine the accuracy and reliability of step counts and energy expenditure as estimated by a pedometer during treadmill walking and to clarify the relationship between step counts and current physical activity recommendations.

Step counter 1 stride 2 steps