Journal for the measurement of physical behaviour最新文献

{"title":"Converting Raw Accelerometer Data to Activity Counts Using Open-Source Code: Implementing a MATLAB Code in Python and R, and Comparing the Results to ActiLife","authors":"R. Brondeel, Y. Kestens, J. R. Anaraki, Kevin G. Stanley, B. Thierry, D. Fuller","doi":"10.1123/jmpb.2019-0063","DOIUrl":"https://doi.org/10.1123/jmpb.2019-0063","url":null,"abstract":"Background: Closed-source software for processing and analyzing accelerometer data provides little to no information about the algorithms used to transform acceleration data into physical activity indicators. Recently, an algorithm was developed in MATLAB that replicates the frequently used proprietary ActiLife activity counts. The aim of this software profile was (a) to translate the MATLAB algorithm into R and Python and (b) to test the accuracy of the algorithm on free-living data. Methods: As part of the INTErventions, Research, and Action in Cities Team, data were collected from 86 participants in Victoria (Canada). The participants were asked to wear an integrated global positioning system and accelerometer sensor (SenseDoc) for 10 days on the right hip. Raw accelerometer data were processed in ActiLife, MATLAB, R, and Python and compared using Pearson correlation, interclass correlation, and visual inspection. Results: Data were collected for a combined 749 valid days (>10 hr wear time). MATLAB, Python, and R counts per minute on the vertical axis had Pearson correlations with the ActiLife counts per minute of .998, .998, and .999, respectively. All three algorithms overestimated ActiLife counts per minute, some by up to 2.8%. Conclusions: A MATLAB algorithm for deriving ActiLife counts was implemented in R and Python. The different implementations provide similar results to ActiLife counts produced in the closed source software and can, for all practical purposes, be used interchangeably. This opens up possibilities to comparing studies using similar accelerometers from different suppliers, and to using free, open-source software.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86673011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twelve-Month Stability of Accelerometer-Measured Occupational and Leisure-Time Physical Activity and Compensation Effects","authors":"Jennifer L. Gay, D. Buchner","doi":"10.1123/jmpb.2021-0010","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0010","url":null,"abstract":"Introduction: Little is known about the stability of occupational physical activity (PA) and documented compensation effects over time. Study objectives were to (a) determine the stability of accelerometer estimates of occupational and nonoccupational PA over 6 months and 1 year in adults who do not change jobs, (b) examine PA stability in office workers relative to employees with nonoffice jobs who may be more susceptible to seasonal perturbations in work tasks, and (c) examine the stability data for compensation effects seen at baseline in this sample. Methods: City/county government workers from a variety of labor sectors wore an accelerometer at initial data collection, and at 6 (n = 98) and 12 months (n = 38) following initial data collection. Intraclass correlation coefficients (ICCs) were calculated for accelerometer counts and minutes by intensity, domain, and office worker status. Partial correlation coefficients were examined for compensation effects. Results: ICCs ranged from .19 to .91 for occupational and nonwork activity variables. ICCs were similar by office worker status. In both counts and minutes, greater occupational PA correlated with lower total nonwork PA. However, as minutes of occupational moderate to vigorous physical activity increased, nonoccupational moderate to vigorous physical activity did not decrease. Conclusions: There was moderate to high stability in occupational and nonoccupational PA over 6- and 12-month data collection. Occupational PA stability was greater in nonoffice workers, suggesting that those employees’ PA may be less prone to potential cyclical factors at the workplace. Confirmation of the compensation effect further supports the need for workplace intervention studies to examine changes in all intensities of activity during and outside of work time.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90078249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerometer Calibration: The Importance of Considering Functionality.","authors":"Scott J Strath,&nbsp;Taylor W Rowley,&nbsp;Chi C Cho,&nbsp;Allison Hyngstrom,&nbsp;Ann M Swartz,&nbsp;Kevin G Keenan,&nbsp;Julian Martinez,&nbsp;John W Staudenmayer","doi":"10.1123/jmpb.2020-0027","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0027","url":null,"abstract":"<p><strong>Purpose: </strong>To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions.</p><p><strong>Methods: </strong>118 adults self-identifying as healthy (<i>n</i> = 44) and those with arthritis (<i>n</i> = 23), multiple sclerosis (<i>n</i> = 18), Parkinson's disease (<i>n</i> = 17), and stroke (<i>n</i> =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit.</p><p><strong>Results: </strong>All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition.</p><p><strong>Conclusion: </strong>Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":" ","pages":"68-78"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330493/pdf/nihms-1672920.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39281318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility and Validity of Assessing Low-Income, African American Older Adults’ Physical Activity and Sedentary Behavior Through Ecological Momentary Assessment","authors":"Jaclyn P. Maher, Kourtney Sappenfield, Heidi Scheer, Christine Zecca, D. Hevel, L. Kennedy-Malone","doi":"10.1123/jmpb.2021-0024","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0024","url":null,"abstract":"Ecological momentary assessment (EMA) is a methodological tool that can provide novel insights into the prediction and modeling of physical behavior; however, EMA has not been used to study physical activity (PA) or sedentary behavior (SB) among racial minority older adults. This study aimed to determine the feasibility and validity of an EMA protocol to assess racial minority older adults’ PA and SB. For 8 days, older adults (n = 91; 89% African American; 70% earning <$20,000/year) received six randomly prompted, smartphone-based EMA questionnaires per day and wore an activPAL monitor to measure PA and SB. The PA and SB were also self-reported through EMA. Participants were compliant with the EMA protocol on 92.4% of occasions. Participants were more likely to miss an EMA prompt in the afternoon compared to morning and on weekend days compared to weekdays. Participants were less likely to miss an EMA prompt when engaged in more device-based SB in the 30 min around the prompt. When participants self-reported PA, they engaged in less device-based PA in the 15 min after compared to the 15 min before the EMA prompt, suggesting possible reactance or disruption of PA. EMA-reported PA and SB were positively associated with device-based PA and SB in the 30 min around the EMA prompt, supporting criterion validity. Overall, the assessment of low-income, African American older adults’ PA and SB through EMA is feasible and valid, though physical behaviors may influence compliance and prompting may create reactivity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79442107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is the Polar M430 a Valid Tool for Estimating Maximal Oxygen Consumption in Adult Females?","authors":"K. Miller, T. Kempf, Brian C. Rider, S. Conger","doi":"10.1123/jmpb.2020-0050","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0050","url":null,"abstract":"Background: Previous research studies have found that heart rate monitors that predict maximal oxygen consumption () are valid for males but overestimate in females. Inaccurate self-reported physical activity (PA) levels may affect the validity of the prediction algorithm used to predict . Purpose: To investigate the validity of the Polar M430 in predicting among females with varying PA levels. Methods: Polar M430 was used to predict () for 43 healthy female study participants (26.9 ± 1.3 years), under three conditions: the participant’s self-selected PA category (sPA), one PA category below the sPA (sPA − 1), and one category above the sPA (sPA + 1). Indirect calorimetry was utilized to measure () via a modified Astrand treadmill protocol. Repeated-measures analyses of covariance using age and percentage of body fat as covariates were used to detect differences between groups. Bland–Altman plots were used to assess the precision of the measurement. Results: was significantly correlated with (r = .695, p < .001). The mean values for and were 44.58 ± 9.29 and 43.98 ± 8.76, respectively. No significant differences were found between , , sPA – 1, and sPA + 1 (p = .492). However, the Bland–Altman plots indicated a low level of precision with the estimate. Conclusions: The Polar M430 was a valid method to predict across different sPA levels in females. Moreover, an under/overestimation in sPA had little effect on the predicted .","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84279507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concurrent Validity of ActiGraph GT3X+ and Axivity AX3 Accelerometers for Estimating Physical Activity and Sedentary Behavior","authors":"Leila Hedayatrad, T. Stewart, S. Duncan","doi":"10.1123/jmpb.2019-0075","DOIUrl":"https://doi.org/10.1123/jmpb.2019-0075","url":null,"abstract":"Introduction: Accelerometers are commonly used to assess time-use behaviors related to physical activity, sedentary behavior, and sleep; however, as new accelerometer technologies emerge, it is important to ensure consistency with previous devices. This study aimed to evaluate the concurrent validity of the commonly used accelerometer, ActiGraph GT3X+, and the relatively new Axivity AX3 (fastened to the lower back) for detecting physical activity intensity and body postures when using direct observation as the criterion measure. Methods: A total of 41 children (aged 6–16 years) and 33 adults (aged 28–59 years) wore both monitors concurrently while performing 10 prescribed activities under laboratory conditions. The GT3X+ data were categorized into different physical activity intensity and posture categories using intensity-based cut points and ActiGraph proprietary inclinometer algorithms, respectively. The AX3 data were first converted to ActiGraph counts before being categorized into different physical activity intensity categories, while activity recognition models were used to detect the target postures. Sensitivity, specificity, and the balanced accuracy for intensity and posture category classification were calculated for each accelerometer. Differences in balanced accuracy between the devices and between children and adults were also calculated. Results: Both accelerometers obtained 74–96% balanced accuracy, with the AX3 performing slightly better (∼4% higher, p < .01) for detecting postures and physical activity intensity. Error in both devices was greatest when contrasting sitting/standing, sedentary/light intensity, and moderate/light intensity. Conclusion: In comparison with the GT3X+ accelerometer, AX3 was able to detect various postures and activity intensities with slightly higher balanced accuracy in children and adults.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77557799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration","authors":"Anna M. J. Iveson, M. Granat, B. Ellis, P. Dall","doi":"10.1123/jmpb.2020-0005","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0005","url":null,"abstract":"Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78108962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional Day-to-Day Associations of Reported Sleep Duration With Accelerometer Measured Physical Activity and Sedentary Time Among Dutch Adolescents: An Observational Study.","authors":"Nathalie Berninger, Gregory Knell, Kelley Pettee Gabriel, Guy Plasqui, Rik Crutzen, Gill Ten Hoor","doi":"10.1123/jmpb.2020-0010","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0010","url":null,"abstract":"<p><strong>Objectives: </strong>To examine the bidirectional association of sleep duration with proportions of time spent in physical behaviors among Dutch adolescents.</p><p><strong>Methods: </strong>Adolescents (<i>n</i> = 294, 11-15 years) completed sleep diaries and wore an accelerometer (ActiGraph) over 1 week. With linear mixed-effects models, the authors estimated the association of sleep categories (short, optimal, and long) with the following day's proportion in physical behaviors. With generalized linear mixed models with binomial distribution, the authors estimated the association of physical behavior proportions on sleep categories. Physical behavior proportions were operationalized using percentages of wearing time and by applying a compositional approach. All analyses were stratified by gender accounting for differing developmental stages.</p><p><strong>Results: </strong>For males (number of observed days: 345, <i>n</i> = 83), short as compared with optimal sleep was associated with the following day's proportion spent in sedentary (-2.57%, <i>p</i> = .03, 95% confidence interval [CI] [-4.95, -0.19]) and light-intensity activities (1.96%, <i>p</i> = .02, 95% CI [0.27, 3.65]), which was not significant in the compositional approach models. Among females (number of observed days: 427, <i>n</i> = 104), long sleep was associated with the proportions spent in moderate- to vigorous-intensity physical activity (1.69%, <i>p</i> < .001, 95% CI [0.75, 2.64]) and in sedentary behavior (-3.02%, <i>p</i> < .01, 95% CI [-5.09, -0.96]), which was replicated by the compositional approach models. None of the associations between daytime activity and sleep were significant (number of obs.: 844, <i>n</i> = 204).</p><p><strong>Conclusions: </strong>Results indicate partial associations between sleep and the following day's physical behaviors, and no associations between physical behaviors and the following night's sleep.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"3 4","pages":"304-314"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of Wearable Trackers for Measuring Moderate- to Vigorous-Intensity Physical Activity: A Systematic Review and Meta-Analysis","authors":"Jessica S. Gorzelitz, Chloe Farber, R. Gangnon, L. Cadmus-Bertram","doi":"10.1123/jmpb.2019-0072","DOIUrl":"https://doi.org/10.1123/jmpb.2019-0072","url":null,"abstract":"Background: The evidence base regarding validity of wearable fitness trackers for assessment and/or modification of physical activity behavior is evolving. Accurate assessment of moderate- to vigorous-intensity physical activity (MVPA) is important for measuring adherence to physical activity guidelines in the United States and abroad. Therefore, this systematic review synthesizes the state of the validation literature regarding wearable trackers and MVPA. Methods: A systematic search of the PubMed, Scopus, SPORTDiscus, and Cochrane Library databases was conducted through October 2019 (PROSPERO registration number: CRD42018103808). Studies were eligible if they reported on the validity of MVPA and used devices from Fitbit, Apple, or Garmin released in 2012 or later or available on the market at the time of review. A meta-analysis was conducted on the correlation measures comparing wearables with the ActiGraph. Results: Twenty-two studies met the inclusion criteria; all used a Fitbit device; one included a Garmin model and no Apple-device studies were found. Moderate to high correlations (.7–.9) were found between MVPA from the wearable tracker versus criterion measure (ActiGraph n = 14). Considerable heterogeneity was seen with respect to the specific definition of MVPA for the criterion device, the statistical techniques used to assess validity, and the correlations between wearable trackers and ActiGraph across studies. Conclusions: There is a need for standardization of validation methods and reporting outcomes in individual studies to allow for comparability across the evidence base. Despite the different methods utilized within studies, nearly all concluded that wearable trackers are valid for measuring MVPA.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"150 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83302400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Automatic Modeling of Volleyball Players’ Behavior for Analysis, Feedback, and Hybrid Training","authors":"F. Salim, F. Haider, D. Postma, R. V. Delden, D. Reidsma, S. Luz, B. Beijnum","doi":"10.1123/jmpb.2020-0012","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0012","url":null,"abstract":"Automatic tagging of video recordings of sports matches and training sessions can be helpful to coaches and players and provide access to structured data at a scale that would be unfeasible if one were to rely on manual tagging. Recognition of different actions forms an essential part of sports video tagging. In this paper, the authors employ machine learning techniques to automatically recognize specific types of volleyball actions (i.e., underhand serve, overhead pass, serve, forearm pass, one hand pass, smash, and block which are manually annotated) during matches and training sessions (uncontrolled, in the wild data) based on motion data captured by inertial measurement unit sensors strapped on the wrists of eight female volleyball players. Analysis of the results suggests that all sensors in the inertial measurement unit (i.e., magnetometer, accelerometer, barometer, and gyroscope) contribute unique information in the classification of volleyball actions types. The authors demonstrate that while the accelerometer feature set provides better results than other sensors, overall (i.e., gyroscope, magnetometer, and barometer) feature fusion of the accelerometer, magnetometer, and gyroscope provides the bests results (unweighted average recall = 67.87%, unweighted average precision = 68.68%, and κ = .727), well above the chance level of 14.28%. Interestingly, it is also demonstrated that the dominant hand (unweighted average recall = 61.45%, unweighted average precision = 65.41%, and κ = .652) provides better results than the nondominant (unweighted average recall = 45.56%, unweighted average precision = 55.45, and κ = .553) hand. Apart from machine learning models, this paper also discusses a modular architecture for a system to automatically supplement video recording by detecting events of interests in volleyball matches and training sessions and to provide tailored and interactive multimodal feedback by utilizing an HTML5/JavaScript application. A proof of concept prototype developed based on this architecture is also described.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72690957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}