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Step detection and energy expenditure at different speeds by three accelerometers in a controlled environment.

ABSTRACT: Physical activity (PA) is one of the most efficient ways to prevent obesity and its associated diseases worldwide. In the USA, less than 10% of the adult population were able to meet the PA recommendations when accelerometers were used to assess PA habituation. Accelerometers significantly differ from each other in step recognition and do not reveal raw data. The aim of our study was to compare a novel accelerometer, Sartorio Xelometer, which enables to gather raw data, with existing accelerometers ActiGraph GT3X+ and activPAL in terms of step detection and energy expenditure estimation accuracy. 53 healthy subjects were divided into 2 cohorts (cohort 1 optimization; cohort 2 validation) and wore 3 accelerometers and performed an exercise routine consisting of the following speeds: 1.5, 3, 4.5, 9 and 10.5 km/h (6 km/h for 2nd cohort included). Data from optimization cohort was used to optimize Sartorio step detection algorithm. Actual taken steps were recorded with a video camera and energy expenditure (EE) was measured. To observe the similarity between video and accelerometer step counts, paired samples t test and intraclass correlation were used separately for step counts in different speeds and for total counts as well as EE estimations. In speeds of 1.5, 3, 4.5, 6, 9 and 10.5 km/h mean absolute percentage error (MAPE) % were 8.1, 3.5, 4.3, 4.2, 3.1 and 7.8 for the Xelometer, respectively (after optimization). For ActiGraph GT3X+ the MAPE-% were 96.93 (87.4), 34.69 (23.1), 2.13 (2.3), 1.96 (2.6) and 2.99 (3.8), respectively and for activPAL 6.55 (5.6), 1.59 (0.6), 0.81 (1.1), 10.60 (10.3) and 15.76 (13.8), respectively. Significant intraclass correlations were observed with Xelometer estimates and actual steps in all speeds. Xelometer estimated the EE with a MAPE-% of 30.3, activPAL and ActiGraph GT3X+ with MAPE percentages of 20.5 and 24.3, respectively. The Xelometer is a valid device for assessing step counts at different gait speeds. MAPE is different at different speeds, which is of importance when assessing the PA in obese subjects and elderly. EE estimates of all three devices were found to be inaccurate when compared with indirect calorimetry.

SUBMITTER: Stenback V

PROVIDER: S-EPMC8501125 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveEven though the validity of accelerometers for the measurement of energy expenditure (EE) has been demonstrated for normal-weight individuals, the applicability of this instrument in obese individuals remains controversial. This review aims to summarize the level of agreement between accelerometers and the gold standards (indirect calorimetry and doubly labelled water) for the measurement of energy expenditure (EE) in obese or overweight individuals.MethodsThe literature search was limited to comparison studies assessing agreement in EE determination between accelerometers and indirect calorimetry (IC) or doubly labelled water (DLW). We searched in PubMed and in Scopus until March 1, 2019. The analysis was restricted to obese or overweight adult individuals. The following descriptive information was extracted for each study: sample size, characteristics of participants (sex, age, BMI, fat mass percentage, any pathological conditions, modality of recruitment in the study, and exclusion criteria), accelerometer description (model, type and body position), and type of gold standard and validity protocol (duration, conditions, and requirements during and before the experiment). Three review authors independently screened the obtained results, and the quality of the selected articles was assessed by the QUADAS-2 tool.ResultsWe obtained seventeen eligible articles, thirteen of which showed concerns for the applicability section, due to the patient selection. Regarding the accelerometers, nine devices were validated in the included studies with the BodyMedia SenseWear® (SWA) being the most frequently validated. Although correlations between accelerometers and the gold standard were high in some studies, agreement between the two methods was low, as shown by the Bland-Altman plots.ConclusionsMost accelerometer estimations of EE were inaccurate for obese/overweight subjects, and authors advise to improve the accuracy of algorithms for SWA software, or the predicted equations for estimating EE from other accelerometers.

Project description:Active video games that require physical exertion during game play have been shown to confer health benefits. Typically, energy expended during game play is measured using devices attached to players, such as accelerometers, or portable gas analyzers. Since 2010, active video gaming technology incorporates marker-less motion capture devices to simulate human movement into game play. Using the Kinect Sensor and Microsoft SDK this research aimed to estimate the mechanical work performed by the human body and estimate subsequent metabolic energy using predictive algorithmic models. Nineteen University students participated in a repeated measures experiment performing four fundamental movements (arm swings, standing jumps, body-weight squats, and jumping jacks). Metabolic energy was captured using a Cortex Metamax 3B automated gas analysis system with mechanical movement captured by the combined motion data from two Kinect cameras. Estimations of the body segment properties, such as segment mass, length, centre of mass position, and radius of gyration, were calculated from the Zatsiorsky-Seluyanov's equations of de Leva, with adjustment made for posture cost. GPML toolbox implementation of the Gaussian Process Regression, a locally weighted k-Nearest Neighbour Regression, and a linear regression technique were evaluated for their performance on predicting the metabolic cost from new feature vectors. The experimental results show that Gaussian Process Regression outperformed the other two techniques by a small margin. This study demonstrated that physical activity energy expenditure during exercise, using the Kinect camera as a motion capture system, can be estimated from segmental mechanical work. Estimates for high-energy activities, such as standing jumps and jumping jacks, can be made accurately, but for low-energy activities, such as squatting, the posture of static poses should be considered as a contributing factor. When translated into the active video gaming environment, the results could be incorporated into game play to more accurately control the energy expenditure requirements.

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Step detection and energy expenditure at different speeds by three accelerometers in a controlled environment.

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