scholarly journals Physical and physiological demands according to gender, playing positions, and match outcomes in youth basketball players.

2022 ◽  
Vol 18 (67) ◽  
pp. 15-28
Author(s):  
Randall Gutiérrez-Vargas ◽  
José Pino-Ortega ◽  
Alexis Ugalde-Ramírez ◽  
Braulio Sánchez-Ureña ◽  
Luis Blanco-Romero ◽  
...  
Keyword(s):  
Heart Rate ◽  
Inertial Measurement Unit ◽  
Band System ◽  
Wide Band ◽  
Ultra Wide Band ◽  
Measurement Unit ◽  
Maximal Heart Rate ◽  
Basketball Players ◽  
Inertial Measurement ◽  
Physiological Demands

This study aimed to compare physical and physiological demands in youth basketball players according to gender, playing positions, and match outcomes. 64 players (32 female and 32 male) from eight youth sub-elite basketball teams were monitored using an Ultra-Wide Band system and inertial measurement unit in three consecutive matches. The results showed some significant differences, although with magnitudes qualified as small. When the teams won, the guards covered a greater distance at 0-6 km/h than when they lost. When teams lost, the centers covered more distance at 12-18 km/h and 18-21 km/h. The winning female teams presented a lower maximal heart rate (HRmax) compared to the losing teams. The forwards of the winning teams performed greater efforts at 70-80% HRmax, while the forwards of losing teams performed more efforts at 90-95% HRmax. The greatest number of accelerations and decelerations were performed by the female guards and the male forwards. The number of jumps was higher in the male guards and forwards than in the female ones. HRmax was higher in the forwards of the female teams. Efforts at 80%-90% HRmax were higher in male centers. When the female teams won, they had a lower HRmax than when they lost. When efforts exceed 90% of HRmax the teams lost. In conclusion, despite the differences found, the effect of these contextual variables on physical and physiological demands is unclear. Nevertheless, knowing the game's requirements can help the design of training that enhances the performance of youth basketball players

scholarly journals Srdcová frekvencia v odozve na herné zaťaženie v basketbale žien

2014 ◽  
Vol 8 (2) ◽  
pp. 29-35
Author(s):  
Tomáš Vencúrik
Keyword(s):  
Heart Rate ◽  
Statistical Significance ◽  
Practical Significance ◽  
Future Research ◽  
Maximal Heart Rate ◽  
Training Process ◽  
Basketball Players ◽  
Time Motion ◽  
The Mean ◽  
Physiological Demands

The study compares intensity of game load among individual players’ positions and between first and second half. Ten female basketball players in senior category participated in this study. Four intensity zones were determined based on maximal heart rate (HRmax). Telemetric device Suunto Team Pack was used for monitoring the heart rate (HR) during the games. The mean HR during the games reached 88.1±3.9% of HRmax of total time. When we compared players’ positions in individual intensity zones we did not record statistical (p>0.05) nor practical significance and neither in % of HRmax (87.4±3.6 vs. 87.8±4.6 vs. 88.8±3.5; point guard vs. forward vs. center). Moreover, when we compared the 1st and the 2nd half in individual zones and in % of HRmax (87.7±4.1 vs. 88.5±3.7) we also did not record any statistical significance (p>0.05) and effect size coefficient shows small effect. Players spent 76.3% of total time with HR grater than 85% of HRmax. The results indicate high physiological demands on female basketball players during the games without taking into consideration the player’s position. This information can be useful for planning and managing training process as well as for comparison with training load. In similar future research we recommend to also evaluate the time-motion analysis besides the internal response and thus a more detailed look at the examined subject in question may be reached.

Development of low-cost mobile laser scanning for 3D construction indoor mapping by using inertial measurement unit, ultra-wide band and 2D laser scanner

2019 ◽  
Vol 26 (7) ◽  
pp. 1367-1386
Author(s):  
Chao Chen ◽  
Llewellyn Tang ◽  
Craig Matthew Hancock ◽  
Penghe Zhang
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
Laser Scanning ◽  
Low Cost ◽  
Laser Scanner ◽  
Wide Band ◽  
Measurement Unit ◽  
Content Type ◽  
Inertial Measurement ◽  
Mls Method

Purpose The purpose of this paper is to introduce the development of an innovative mobile laser scanning (MLS) method for 3D indoor mapping. The generally accepted and used procedure for this type of mapping is usually performed using static terrestrial laser scanning (TLS) which is high-cost and time-consuming. Compared with conventional TLS, the developed method proposes a new idea with advantages of low-cost, high mobility and time saving on the implementation of a 3D indoor mapping. Design/methodology/approach This method integrates a low-cost 2D laser scanner with two indoor positioning techniques – ultra-wide band (UWB) and an inertial measurement unit (IMU), to implement a 3D MLS for reality captures from an experimental indoor environment through developed programming algorithms. In addition, a reference experiment by using conventional TLS was also conducted under the same conditions for scan result comparison to validate the feasibility of the developed method. Findings The findings include: preset UWB system integrated with a low-cost IMU can provide a reliable positioning method for indoor environment; scan results from a portable 2D laser scanner integrated with a motion trajectory from the IMU/UWB positioning approach is able to generate a 3D point cloud based in an indoor environment; and the limitations on hardware, accuracy, automation and the positioning approach are also summarized in this study. Research limitations/implications As the main advantage of the developed method is low-cost, it may limit the automation of the method due to the consideration of the cost control. Robotic carriers and higher-performance 2D laser scanners can be applied to realize panoramic and higher-quality scan results for improvements of the method. Practical implications Moreover, during the practical application, the UWB system can be disturbed by variances of the indoor environment, which can affect the positioning accuracy in practice. More advanced algorithms are also needed to optimize the automatic data processing for reducing errors caused by manual operations. Originality/value The development of this MLS method provides a novel idea that integrates data from heterogeneous systems or sensors to realize a practical aim of indoor mapping, and meanwhile promote the current laser scanning technology to a lower-cost, more flexible, more portable and less time-consuming trend.

Validation of a commercially available inertial measurement unit for recording jump load in youth basketball players

2020 ◽  
Vol 38 (8) ◽  
pp. 928-936 ◽  
Author(s):  
Lauren C. Benson ◽  
Tyler J. Tait ◽  
Kimberley Befus ◽  
John Choi ◽  
Colin Hillson ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Basketball Players ◽  
Inertial Measurement

scholarly journals Comparison of Seismocardiography Based Heart Rate Measurement Method

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Bayu Erfianto ◽  
Achmad Rizal ◽  
Vera Suryani
Keyword(s):  
Heart Rate ◽  
Inertial Measurement Unit ◽  
Digital Signal ◽  
Measurement Unit ◽  
Fiducial Point ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Packet Filter ◽  
Mode Decomposition ◽  
Time Period

The article describes a new alternative method of detecting the Aorta Open fiducial point based on digital signal processing formulated from the average seismocardiogram cycle obtained from the 6-degree-of-freedom Micro Electro-Mechanical Systems Inertial Measurement Unit, enabling estimation of heartbeat during heart muscle contraction without reference to electrocardiogram time period. Using the seismocardiography data obtained from the Inertial Measurement Unit, the authors then process the data using two methods: 1) Empirical Mode Decomposition and 2) Jerk signal, which is extracted as a first derivative of the Inertial Measurement Unit signal. As an example, we compare the two proposed methods to the existing method. Our Method 2 allows us to detect Aorta Open-Aorta Open value between 400ms and 450ms using Berkeley Packet Filter 5-15 Hz with dynamic peak threshold from the Hilbert envelope. Thus, the evaluation of the new method’s effectiveness is confirmed by the estimation of the Aorta Open-Aorta Open fiducial point as closer to the reference. Therefore, the result of our research, especially using jerk signal, can be considered a more accurate alternative for estimating heart rate or heartbeat based on seismocardiogram.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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