Evaluation of Dominant and Non-Dominant Hand Movements For Volleyball Action Modelling

In this paper, we assess the use of Inertial Measurement Units (IMU) in recognising different volleyball-specific actions. Analysis of the results suggests that all sensors in the IMU (i.e. magnetometer, accelerometer, barometer and gyroscope) contribute unique information in the classification of volleyball-specific actions. We demonstrate that while the accelerometer feature set provides the best Unweighted Average Recall (UAR) overall, “decision fusion” of the accelerometer with the magnetometer improves UAR slightly from 85.86% to 86.9%. Interestingly, it is also demonstrated that the non-dominant hand provides better UAR than the dominant hand. These results are even more marked with “decision fusion”.

[1]  David S. Rosenblum,et al.  From action to activity: Sensor-based activity recognition , 2016, Neurocomputing.

[2]  Rosa H. M. Chan,et al.  Volleyball Skill Assessment Using a Single Wearable Micro Inertial Measurement Unit at Wrist , 2018, IEEE Access.

[3]  Bodo Rosenhahn,et al.  Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs , 2017, Comput. Graph. Forum.

[4]  Daniel Roggen,et al.  Beach volleyball serve type recognition , 2016, SEMWEB.

[5]  Jun Wang,et al.  An embedded 6-axis sensor based recognition for tennis stroke , 2017, 2017 IEEE International Conference on Consumer Electronics (ICCE).

[6]  Tovi Grossman,et al.  Video lens: rapid playback and exploration of large video collections and associated metadata , 2014, UIST.

[7]  Rahmat Adnan,et al.  Comparison between Marker-less Kinect-based and Conventional 2D Motion Analysis System on Vertical Jump Kinematic Properties Measured from Sagittal View , 2016 .

[8]  Dimitrios I. Fotiadis,et al.  Wearability Assessment of a Wearable System for Parkinson's Disease Remote Monitoring Based on a Body Area Network of Sensors , 2014, Sensors.

[9]  Julius Hannink,et al.  Activity recognition in beach volleyball using a Deep Convolutional Neural Network , 2017, Data Mining and Knowledge Discovery.

[10]  Damjan Vlaj,et al.  Tennis stroke detection and classification using miniature wearable IMU device , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).

[11]  Silvio Savarese,et al.  Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Milan Petkovic,et al.  Image Segmentation and Feature Extraction for Recognizing Strokes in Tennis Game Videos , 2001 .

[13]  Francisco Javier González-Castaño,et al.  SAETA: A Smart Coaching Assistant for Professional Volleyball Training , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Paddy Jarit,et al.  Dominant-hand to Nondominant-hand Grip-strength Ratios of College Baseball Players , 1991 .