More Web Proxy on the site http://driver.im/

research-article

Basketball Activity Recognition using Wearable Inertial Measurement Units

Authors:

Le Nguyen Ngu Nguyen,

Daniel Rodríguez-Martín,

Andreu Català,

Carlos Pérez-López,

Andrea CavallaroAuthors Info & Claims

Interacción '15: Proceedings of the XVI International Conference on Human Computer Interaction

Article No.: 60, Pages 1 - 6

https://doi.org/10.1145/2829875.2829930

Published: 07 September 2015 Publication History

Abstract

The analysis and evaluation of human movement is a growing research area within the field of sports monitoring. This analysis can help support the enhancement of an athlete's performance, the prediction of injuries or the optimization of training programs. Although camera-based techniques are often used to evaluate human movements, not all movements of interest can be analyzed or distinguished effectively with computer vision only. Wearable inertial systems are a promising technology to address this limitation. This paper presents a new wearable sensing system to record human movements for sports monitoring. A new paradigm is presented with the purpose of monitoring basketball players with multiple inertial measurement units. A data collection plan has been designed and implemented, and experimental results show the potential of the system in basketball activity recognition.

References

[1]

Aggarwal, J., Ryoo, M.: Human Activity Analysis: A Review ACM Comput. Surv., ACM, 2011.

Digital Library

[2]

Bulling, A., Blanke, U., Schiele, B. A.: Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. ACM Computing Surveys, 2014.

Digital Library

[3]

Lara, O., Labrador, M. A.: Survey on Human Activity Recognition using Wearable Sensors. Communications Surveys Tutorials, IEEE, 2013.

[4]

Kelly, D., Coughlan, G. F., Green, B. S., Caulfield, B.: Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Engineering, 2012.

[5]

Morris, D., Saponas, T. S., Guillory, A., Kelner, I.: RecoFit: Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2014.

Digital Library

[6]

Bächlin, M., Förster, K., Tröster, G.: SwimMaster: A Wearable Assistant for Swimmer. Proceedings of the 11th International Conference on Ubiquitous Computing, ACM, 2009.

Digital Library

[7]

Harle, R., Taherian, S., Pias, M., Coulouris, G., Hopper, A., Cameron, J., Lasenby, J., Kuntze, G., Bezodis, I., Irwin, G., Kerwin, D.: Towards real-time profiling of sprints using wearable pressure sensors. Computer Communications, 2012.

Digital Library

[8]

Ladha, C., Hammerla, N. Y., Olivier, P., Plötz, T.: ClimbAX: Skill Assessment for Climbing Enthusiasts. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 2013.

Digital Library

[9]

Velloso, E.; Bulling, A., Gellersen, H., Ugulino, W., Fuks, H.: Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of the 4th Augmented Human International Conference, ACM, 2013.

Digital Library

[10]

Ghasemzadeh, H., Jafari, R.: Body sensor networks for baseball swing training: Coordination analysis of human movements using motion transcripts. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010 8th IEEE International Conference on, 2010.

[11]

Massé, F., Bourke, K., Chardonnens, J., Paraschiv-Ionescu, A., Aminian, K.: Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring. Med. Eng. Phys. 36, 739--44, 2014.

[12]

Zhou, S., Shan, Q., Fei, F., Li, W.J., Wu, P.C.K., Meng, B., Chan, C.K.H., Liou, J.Y.J.: Gesture recognition for interactive controllers using MEMS motion sensors. 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems. pp. 935--940, 2009.

Digital Library

[13]

Najafi, B., Aminian, K., Loew, F., Blanc, Y., Robert, P.A.: Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans. Biomed. Eng. 49, 843--851, 2002.

[14]

Barralon, P., Vuillerme, N., Noury, N.: Walk detection with a kinematic sensor: frequency and wavelet comparison. 28th Annu. Int. Conf. IEEE EMBS. 1, 1711--1714, 2006.

[15]

Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011.

Digital Library

Cited By

He ZSun YZhang Z(2024)Human Activity Recognition Based on Deep Learning Regardless of Sensor OrientationApplied Sciences10.3390/app1409363714:9(3637)Online publication date: 25-Apr-2024
https://doi.org/10.3390/app14093637
Chenyi ZWeijian ZBo LZhiyong ZFeng Z(2024)A Review on the Application of Artificial Intelligence in Basketball SportsInternational Journal of Computer Science in Sport10.2478/ijcss-2024-001323:2(62-90)Online publication date: 18-Oct-2024
https://doi.org/10.2478/ijcss-2024-0013
Panni LCosoli GArnesano MCitarelli FAntognoli LScalise L(2024)Metrological Characterization of a Wearable Device for the Assessment of Gait Parameters2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA60663.2024.10596895(1-6)Online publication date: 26-Jun-2024
https://doi.org/10.1109/MeMeA60663.2024.10596895
Show More Cited By

Index Terms

Basketball Activity Recognition using Wearable Inertial Measurement Units
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Using Wrist-Worn Activity Recognition for Basketball Game Analysis
iWOAR '18: Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction

Game play in the sport of basketball tends to combine highly dynamic phases in which the teams strategically move across the field, with specific actions made by individual players. Analysis of basketball games usually focuses on the locations of ...
A tutorial on human activity recognition using body-worn inertial sensors

The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity ...
Activity Recognition using Head Worn Inertial Sensors
iWOAR '18: Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction

Human activity recognition using inertial sensors is an increasingly used feature in smartphones or smartwatches, providing information on sports and physical activities of each individual. But while the position a smartphone is worn in varies between ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

Interacción '15: Proceedings of the XVI International Conference on Human Computer Interaction

September 2015

287 pages

ISBN:9781450334631

DOI:10.1145/2829875

Conference Chair:
Pere Ponsa,
Program Chair:
Daniel Guasch

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Universitat Politècnica de Catalunya: Universitat Politècnica de Catalunya

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

Interacción '15

Interacción '15: XVI International Conference on Human Computer Interaction

September 7 - 9, 2015

Vilanova i la Geltru, Spain

Acceptance Rates

Overall Acceptance Rate 109 of 163 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

55
Total Citations
View Citations
637
Total Downloads

Downloads (Last 12 months)49
Downloads (Last 6 weeks)4

Reflects downloads up to 10 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

He ZSun YZhang Z(2024)Human Activity Recognition Based on Deep Learning Regardless of Sensor OrientationApplied Sciences10.3390/app1409363714:9(3637)Online publication date: 25-Apr-2024
https://doi.org/10.3390/app14093637
Chenyi ZWeijian ZBo LZhiyong ZFeng Z(2024)A Review on the Application of Artificial Intelligence in Basketball SportsInternational Journal of Computer Science in Sport10.2478/ijcss-2024-001323:2(62-90)Online publication date: 18-Oct-2024
https://doi.org/10.2478/ijcss-2024-0013
Panni LCosoli GArnesano MCitarelli FAntognoli LScalise L(2024)Metrological Characterization of a Wearable Device for the Assessment of Gait Parameters2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA60663.2024.10596895(1-6)Online publication date: 26-Jun-2024
https://doi.org/10.1109/MeMeA60663.2024.10596895
Shahar NAs’Ari MSwee TGhazali N(2024)Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA)IEEE Access10.1109/ACCESS.2024.341382212(113300-113313)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3413822
Hoelzemann ARomero JBock MLaerhoven KLv Q(2023)Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial SensorsSensors10.3390/s2313587923:13(5879)Online publication date: 25-Jun-2023
https://doi.org/10.3390/s23135879
Shen QTeso SGiunchiglia FXu H(2023)To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity RecognitionElectronics10.3390/electronics1210227512:10(2275)Online publication date: 18-May-2023
https://doi.org/10.3390/electronics12102275
Kranzinger CBernhart SKremser WVenek VRieser HMayr SKranzinger S(2023)Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping ReviewApplied Sciences10.3390/app1315868413:15(8684)Online publication date: 27-Jul-2023
https://doi.org/10.3390/app13158684
Bao RLi JChen YYang KChen ZZhang Y(2023)PracticalHAR: A Practical Method of Human Activity Recognition Based on Embedded Sensors of Mobile PhoneProceedings of the 2023 13th International Conference on Communication and Network Security10.1145/3638782.3638835(342-349)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3638782.3638835
Mekruksavanich SJitpattanakul A(2023)Free-Weight Exercise Activity Recognition using Deep Residual Neural Network based on Sensor Data from In-Ear Wearable Devices2023 46th International Conference on Telecommunications and Signal Processing (TSP)10.1109/TSP59544.2023.10197815(52-55)Online publication date: 12-Jul-2023
https://doi.org/10.1109/TSP59544.2023.10197815
Azhar MUllah MImran AYamin MDaudpota SCheikh F(2023)Cricket Shot Classification using Transformer2023 International Symposium on Image and Signal Processing and Analysis (ISPA)10.1109/ISPA58351.2023.10279281(1-6)Online publication date: 18-Sep-2023
https://doi.org/10.1109/ISPA58351.2023.10279281
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents