Wearable accelerometer-based assessment of physical activity intensity has become a trend among researchers to classify and evaluate the intensity of physical activity of a person. Providing ease and simplicity in experimental setup is part of motivation why accelerometer is more preferable than the conventional method, such as questionnaires. Additionally, the dimension size of the accelerometer is intensively smaller than existing laboratory equipment which mostly required larger space, and making the free-living observation is limited. On the other hand, an appropriate signal processing method may be challenging to be defined due to the presence of the true acceleration of gravity. Several studies had reported several signal processing methods to separate the gravity and movement component of acceleration apart. Additionally, selecting suitable cutoff could be tricky to justify accelerometer signal representing physical activity intensity. However, the lack of validity to the infrared camera-based motion capture system and the lack of comparison of cutoff selection have led our motivation to provide an appropriate signal processing method validated to the motion capture system. Moreover, the existing signal processing method used in various physical activity intensity studies yield the lack of axis-wise orientation, which led to observation in each axis level is negligible. Better interpretation of physical activity intensity measured from appropriate signal processing may be applied to related medical field, such as cardiopulmonary.
The current study aimed to compare gravity removal processing methods and validate them to infrared camera-based motion capture system. Ten healthy young adults participated in the study. Each participant wore 7 wearable accelerometers on 7 places: both upper tibias, both thigh, both wrists, and lumbar vertebrae close to the center of mass. Each participant was instructed to walk in 3 different speed conditions: fast, normal, and slow. There were 3 processing methods compared in the study: axis-wise high-pass filtering followed by the Euclidean Norm of the 3-axes (SVM1), the Euclidean Norm of the raw acceleration of 3-axes followed by minus one (SVM2), the Euclidean Norm of the raw acceleration of 3-axes followed by high-pass filtering (SVM3). A set of six cutoffs has been observed: 6, 8, 10, 15, 20 Hz, and no cutoff to evaluate filtering effect for noise of high frequencies. Each accelerometer has each method-cutoff pairwise variable. A retro-reflective spherical marker was attached to the top of each accelerometer as the validator. Fourth-order Butterworth 6 Hz low-pass filter was applied to all markers in an axis-wise manner before the Euclidean Norm was taken into account. The sum of each method-cutoff pairwise variable from each accelerometer has been compared to the sum of each marker. Several metrics have been considered to evaluate them: mean absolute error (accuracy), 95% level of agreement, the root of mean squared error (precision), and coefficient of variance. The metrics have shown that SVM1 outperformed the others across any placement, any speed, and any cutoff. Further evaluation has shown that the best SVM1-cutoff pairwise varies among placement. After the best signal processing method have been validated, another aim of the study was to provide cut-points for classifying physical activity intensity from the best signal processing method in various placement, compared to MET values obtained from the Spiro ergometer measurement tool. Fifteen young adult professional marathoners participated in the study. Each participant wore 7 accelerometers on 7 places: both upper tibias, both thighs, both wrists, and lumbar vertebrae close to the center of mass. Breath-by-breath oxygen consumption was measured by the Spiro ergometer. Each participant had performed 4-minute stage-wise treadmill running with an increment of speed 1.5 km/h between each stage. The slowest speed was 10 km/h. Each participant was instructed to proceed to the next stage until they felt exhausted and unable to continue. An existing set of cut-points has been selected for comparison of energy prediction obtained from axis-wised acceleration data classified by the self-developed set of cut-points and those non-axis specific acceleration data classified by the existing cut-points. Future application of the output from the study may enhance observation of energy expenditure in axis level, especially in antero-posterior and vertical axis, since most of physical activities occur in these axes.

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