Tennis stroke detection using inertial data of a smartwatch

To assist individuals in sports activities is one of the emerging areas of wearable applications. Among various kinds of sports, detecting tennis strokes faces unique challenges. In this sport the speed of strokes is high, enforcing wearable sensors to have high sampling rates, high-speed bus (to transfer the data to the processor), and the most importantly adequate size of high-speed memory. The constraints encourage researchers to design a custom made hardware to cope with the challenges. The research question that we are trying to address is to show how accurate a commercial smartwatch can detect tennis strokes by using various techniques in machine learning. In this paper, we propose an approach to detect three tennis strokes by utilizing a smartwatch. In our method, the smartwatch is part of a wireless network in which inertial data file is transferred to a laptop where data prepossessing and classification is performed. The data file contains acceleration and angular velocity data of the 3D accelerometer and gyroscope. We also enhanced our method with data prepossessing techniques to elevate data quality. The evaluation of our devised method shows promising results compared to a similar method.

[1]  Abdullah Ahmed,et al.  A wearable wireless sensor for real time validation of bowling action in cricket , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[2]  Mathieu Lemay,et al.  Real-time monitoring of swimming performance , 2016, EMBC.

[3]  Hongnian Yu,et al.  A survey on wearable sensor modality centred human activity recognition in health care , 2019, Expert Syst. Appl..

[4]  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).

[5]  Paul J. M. Havinga,et al.  Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey , 2010, ARCS Workshops.

[6]  Nasser Kehtarnavaz,et al.  Home-based Senior Fitness Test measurement system using collaborative inertial and depth sensors , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Gerhard Tröster,et al.  A wearable sensing system for timing analysis in tennis , 2016, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[8]  Xiaohui Peng,et al.  Deep Learning for Sensor-based Activity Recognition: A Survey , 2017, Pattern Recognit. Lett..

[9]  Andrea Tonoli,et al.  Fall identification in rock climbing using wearable device , 2016 .

[10]  A. Ayyasamy,et al.  Tracking based Event Detection of Singles Broadcast Tennis Video , 2018, 2018 3rd International Conference on Communication and Electronics Systems (ICCES).

[11]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

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

[13]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[14]  Marko Kos,et al.  A Wearable Device and System for Movement and Biometric Data Acquisition for Sports Applications , 2017, IEEE Access.

[15]  Mohanasankar Sivaprakasam,et al.  Tennis serve correction using a performance improvement platform , 2018, 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH).

[16]  David Windridge,et al.  Automatic annotation of tennis games: An integration of audio, vision, and learning , 2014, Image Vis. Comput..

[17]  Nasser Kehtarnavaz,et al.  UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[18]  Dimitris Kanellopoulos,et al.  Data Preprocessing for Supervised Leaning , 2007 .

[19]  Mounir Zok.  Inertial sensors are changing the games , 2014, 2014 International Symposium on Inertial Sensors and Systems (ISISS).

[20]  James D. Amor,et al.  Preliminary study on activity monitoring using an android smart-watch , 2015, Healthcare technology letters.

[21]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[22]  Hadi Tabatabaee Malazi,et al.  PAMS: A new position-aware multi-sensor dataset for human activity recognition using smartphones , 2017, 2017 19th International Symposium on Computer Architecture and Digital Systems (CADS).

[23]  Jeen-Shing Wang,et al.  Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers , 2008, Pattern Recognit. Lett..

[24]  Nasser Kehtarnavaz,et al.  Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors , 2015, IEEE Transactions on Human-Machine Systems.

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

[26]  Diane J. Cook,et al.  Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data , 2015 .

[27]  Roozbeh Jafari,et al.  A Decision Level Fusion and Signal Analysis Technique for Activity Segmentation and Recognition on Smart Phones , 2018, UbiComp/ISWC Adjunct.