Abstracts Track 2022

Area 1 - Computer Systems in Sports

Nr: 2

Integrating Mobile Technology into Physical Education Application: A Framework of Authentic Big Data Retrieved from IOT and Clouds


Adel Belkadi, Mohamed Moussa, Douaa Mimoun and Guezgouz Elhadj

Abstract: With the dynamic development of wireless transmission technology and the popularization of mobile devices, mobile technology is becoming more and more closely related to modern people's life, and it plays an indispensable role in daily life or learning applications. Recently, government concerned endeavoured to promote mobile learning in education. With the aid of mobile technologies, it can increase the motivation of learning and enhance the ability of critical thinking. This paper proposed the framework of FABRIC (Framework of Authentic Big data Retrieved from Internet of things and Clouds). By means of the Data Visualization, it provides more information to instructors and construct more instructional activities, including assessment, remedial teaching and problem base learning. This framework tried to integrate these technologies (Points), includes wireless data transferring, internet of things, cloud computing, big data and data visualization. Moreover, taking the advantage of interactions (Threads) within data transferring and flowing among these technologies weaves these threads into a complete structure of knowledge (FABRIC).The contemporary students were categorized into digital natives. They have different learning preference and style. The teachers are supposed digital emigrants or digital refugee. How to make the best use of computer technologies applying to instructional design and activities might be an important issue in the future.

Area 2 - Signal Processing in Human Movement

Nr: 5

Validity and Reliability of a Smartphone Application to Analyze the Counter Movement Jump Height


Larissa Santos P. Pinheiro, George S. Sabino, Diego H. Antunes, Jerfferson M. de Brito, Daniel C. Oliveira, Guilherme S. Araújo and Renan A. Resende

Abstract: Introduction: The counter movement jump (CMJ) is often used to assess explosive strength, power, fatigue and physical fitness in sports. The CMJ height can assessed by force plates and by smartphone apps. Although force plates provides high precision data analysis, their high cost make their use in clinical practice limited. On the other hand, smartphone applications are less expensive and consequently more accessible for clinicians. Objective: Evaluate the concurrent validity and intra- and inter-rater reliability of the PysioCode Posture application (PCP app) to assess the CMJ height. Methods: The participants were instructed to perform the CMJ three times for familiarization and three valid CMJ, with intervals of 15 seconds, in a force plate (Bertec a 1000Hz, Bertec Corp, Columbus, OH). The CMJ were also recorded with the PCP app using a smartphone Motorola Motog6 Play® (4:3 13MP, HD 720p - 30fps) vertically positioned at the height of the participant’s knee and at a distance of 1.5 meters. The PCP app is avaiable for Android™ and, in addition to camera integration with the inertial sensor, presents features such as the amplification of the analyzed point by screen touch. For the force plate, the time interval between the ground reaction force disappearance and reappearance was considered for the analysis of the CMJ height. For the PCP app, two trained examiners selected the moment in the video when the feet lost contact with the ground and the moment they touched it again and the time interval between these two events was used for analysis of the CMJ height. We used the following formula to calculate CMJ height: h= t2×1,23, where h is the height in meters and t is the time in seconds. For the intra-rater reliability, the examiners repeated the analysis in the PCP app after seven days without contact with the previous data or the other examiner’s data. Data were tested for normal distribution using the Shapiro-Wilk test and did not present normal distribution. Therefore, Spearman's nonparametric test was used to verify the correlation between data from PCP app and force plate measurements. For reliability measures, the Intraclass Correlation Coefficient (ICC) was used. The significance was set at α=0.05. SPSS 19 software (SPSS Inc., Chicago, IL) was used for all analyses. Results: Thirty-seven healthy individuals (18 women and 19 men) participated in this study. The sample characteristics were: mean age of 23.5 years (standard deviation [SD]: 4.0), mean body mass of 65.2 kg (SD: 10.5), mean height of 170.0 cm (SD: 10.0), mean body mass index of 22.2 kg/m2 (SD: 3.0). The mean (SD) CMJ height for the examiner 1 was 26.48 cm (8.63); for examiner 2 was 26.39 cm (8.46); and in the force plate was 27.83 cm (8.29). The PCP concurrent validity with force plate was excellent, with a Spearman's correlation index of 0.98. The height of the CMJ using the PCP app presented excellent intra-rater reliability (Intraclass Correlation Coefficient [ICC]3,3: 0.99; Confidence Interval [CI95%]: 0.98–0.99; Standard Error of Measurement [SEM]: 0.73 cm) and also excellent inter-rater reliability (ICC3,3: 0.96; CI95%: 0.94–0.98; SEM: 1.54 cm). Conclusion: The use of PCP app to measure the CMJ height was valid and demonstrated excellent intra- and inter-rater reliability levels. This accessible and low-cost method of evaluation is essential for professionals in clinical settings.

Area 3 - Sport Performance and Support Technology

Nr: 3

Should the Anaerobic Potential be Considered When Calculating Running Economy?


Ana Sofia M. Monteiro, Diogo D. Carvalho, Ricardo Cardoso, Manoel Rios, João P. Vilas-Boas and Ricardo J. Fernandes

Abstract: Running economy is usually described as the steady state oxygen uptake required at a given submaximal velocity or to cover a given distance, and is traditionally considered as the inverse of the running energy cost (C; [1]). However, the energy provided by both aerobic and anaerobic systems should be considered in its assessment, since the two support exercise even at longer distances or slower paces. In addition, running economy assessment using a wider range of velocities can be useful for training monitoring and prescription by providing a deeper knowledge about runners physiological and training status. A comparison between running economy obtained with submaximal vs low to maximal velocities (REsubmax vs RElow-max, respectively) was conducted using 10 middle- and long-distance male runners (26.5 ± 5.4 years of age, 12.8 ± 3.9 h/week of training and > seven years of middle- and long-distance training practice). Participants performed a 7 x 800 m running incremental protocol (with 1 km∙h-1 velocity rise per step and 30 s rest intervals) plus a maximal 400 m running on an outdoor 400 m track field. Oxygen uptake (VO2) was continuously measured using a K4b2 portable gas analyser (Cosmed, Italy) and blood lactate concentrations ([La-]) were analysed using Lactate Pro2 (Arkay, Japan) at rest, during the intervals and at the end of exercise (until maximal values were reached). The energy expenditure (E) for each step of the incremental protocol was determined by adding the net VO2 and net [La-] (expressed in O2 equivalents using the proportionality constant of 3 mL∙kg-1∙mM-1) values. REsubmax was obtained using the steps below and at the anaerobic threshold (assessed using the interception of a pair of linear and exponential regressions). C was calculated as the slope of the regression line obtained from the relationship between E (assuming an energetic equivalent of 20.9 kJ∙L-1) and corresponding velocities. Runners presented VO2max and maximal [La-] values similar to those reported in the literature (66.0 ± 7.9 mL∙kg-1∙min-1 and 17.8 ± 4.8 mmol∙L-1; [2,3]). REsubmax and RElow-max amplitude were [3.47-8.89] and [6.25-12.32] J∙kg-1∙m-1 (respectively), with their mean values being lower in the former comparing with the later (5.64 ± 1.43 vs. 9.01 ± 2.03 J∙kg-1∙m-1; p < 0.001; ES = 1.8 [0.8-2.8 95% CI]). This evidence the importance of including a wider range of running velocities when assessing running economy, applying the traditional concept to different training velocities independently if the evaluated subjects are short-, middle- or long-distance runners. Through the analysis of the E distribution along the incremental protocol, it is possible to monitor runners running economy consistency from low to maximal velocities and to identify the velocities where runners need more or less intervention, improving training prescription. Founding: Portuguese Foundation for Science and Technology (FCT) and European Union (EU) under grant number 2020.07714.BD. [1] Margaria et al, 1963. J Appl Physiol, 18: 367-370 [2] Sousa et al., 2015. Med Sci Sports Exerc, 47: 1705-1713 [3] Ohkuwa et al., 1984. Eur J Appl Physiol, 53: 213-218

Nr: 4

Feature Extraction to Estimate the Standing Long Jump (SLJ) Length from Smartphone Inertial Sensors


Beatrice De Lazzari, Guido Mascia, Giuseppe Vannozzi and Valentina Camomilla

Abstract: 1 INTRODUCTION Standing long jump (SLJ) is a well-recognized test performed to estimate the lower limb power in athletes. The assessment of this task is typically performed through force plates (Harry et al, 2021), whose signal is analyzed to extract phases of the jump and related features. The work tests the possiblity to use a smartphone as a measurement instrument of SLJ length. To this aim, signals from smartphone inertial sensors are acquired during SLJs using a dedicated app and selected features are extracted from the signal vertical (V) and antero-posterior (AP) components. 2 METHODS A group of 109 participants (73 M, 36 F; age: 21±5 y, mass: 71±10 kg, height: 1.79±0.12 m) performed 3 SLJ each, starting in orthostatic posture for 3 s, with hands on the hips maintaining firmly with the right one a smartphone equipped with an app (Phyphox) to record motion data (fs=500 sample/s, accelerometer range: 78.45 m/s^2; gyroscope range: 17.45 rad/s), jumping forward with the hands on the hips and then standing in orthostatic posture for 3 s after landing. From V and AP acceleration components of each SLJ trial, a total of 45 candidate features were extracted. Significant features were determined using a Lasso regression with MSE minimization, over 313 valid SLJs (median [iqr]=1.87 [0.42] m, measured with tape meter). 3 RESULTS The lasso regression returns 17 variables. The most relevant 5 are reported in Table 1. The first 3 spatio-temporal variables are directly related to measured jump distance, while the last two are inversely related. Table 1: Most relevant features (features with a coefficient absolute value higher than 2). Variable Unit Coeff Meaning lB m 6.57 ballistic jump distance CV s 5.42 time from min-to-max acceleration JV s 4.69 time from velocity negative peak up to take-off MAP s -2.50 time duration of positive power OV s -2.01 time from positive peak power to take-off 4 CONCLUSIONS The smartphone-based SLJ assessment has a median [iqr] error of -0.7 [17.6] % (max=50%). The proposed model seems promising for an effective real-time estimation of SLJ length when assessing sport performance using smartphone inertial sensors. REFERENCES Harry, J. R., et al. (2021). Phase-specific force and time predictors of standing long jump distance. J Appl Biomech, 37(5), 400-407.