A Wearable Device and System for Movement and Biometric Data Acquisition for Sports Applications

This paper presents a miniature wearable device and a system for detecting and recording the movement and biometric information of a user during sport activities. The wearable device is designed to be worn on a wrist and can monitor skin temperature and pulse rate. Furthermore, it can monitor arm movement and detect gestures using inertial measurement unit. The device can be used for various professional and amateur sport applications and for health monitoring. Because of its small size and minimum weight, it is especially appropriate for swing-based sports like tennis or golf, where any additional weight on the arms would most likely disturb the player and have some influence on the player’s performance. Basic signal processing is performed directly on the wearable device but for more complex signal analysis, the data can be uploaded via the Internet to a cloud service, where it can be processed by a dedicated application. The device is powered by a lightweight miniature LiPo battery and has about 6 h of autonomy at maximum performance.

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

[2]  Yu-Liang Hsu,et al.  A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease , 2016, IEEE Access.

[3]  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).

[4]  Noel E. O'Connor,et al.  Game, shot and match: Event-based indexing of tennis , 2011, 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI).

[5]  Jeong-Mook Lim,et al.  Actual remote control: a universal remote control using hand motions on a virtual menu , 2009, IEEE Transactions on Consumer Electronics.

[6]  David C Viano,et al.  On the accuracy of the Head Impact Telemetry (HIT) System used in football helmets. , 2013, Journal of biomechanics.

[7]  Zhihan Lv,et al.  Evaluation of Kinect2 based balance measurement , 2016, Neurocomputing.

[8]  F X Li,et al.  String vibration dampers do not reduce racket frame vibration transfer to the forearm , 2004, Journal of sports sciences.

[9]  N. Owens,et al.  Hawk-eye tennis system , 2003 .

[10]  Xingyu Wang,et al.  Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..

[11]  Noel E. O'Connor,et al.  Multi-sensor classification of tennis strokes , 2011, 2011 IEEE SENSORS Proceedings.

[12]  Joseph Wei,et al.  How Wearables Intersect with the Cloud and the Internet of Things : Considerations for the developers of wearables. , 2014, IEEE Consumer Electronics Magazine.

[13]  Yu-Liang Hsu,et al.  Golf swing motion detection using an inertial-sensor-based portable instrument , 2016, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[14]  Mika P. Tarvainen,et al.  Kubios HRV - Heart rate variability analysis software , 2014, Comput. Methods Programs Biomed..

[15]  B. Pluim,et al.  Intensity of tennis match play , 2006, British Journal of Sports Medicine.

[16]  Emily Waltz.  A wearable turns baseball pitching into a science [News] , 2015 .

[17]  A. Ababou,et al.  A new instrument for punch analysis in boxing , 2014 .

[18]  Sunil Kumar,et al.  Efficient characterization of tennis shots and game analysis using wearable sensors data , 2015, 2015 IEEE SENSORS.

[19]  Karen Lightman.  Silicon gets sporty , 2016, IEEE Spectrum.

[20]  Zhihan Lv,et al.  Multimodal Hand and Foot Gesture Interaction for Handheld Devices , 2014, TOMM.

[21]  Xingyu Wang,et al.  Sparse Bayesian Classification of EEG for Brain–Computer Interface , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Zhihan Lv,et al.  Bigdata Oriented Multimedia Mobile Health Applications , 2016, Journal of Medical Systems.

[23]  M. Elgendi.  On the Analysis of Fingertip Photoplethysmogram Signals , 2012, Current cardiology reviews.

[24]  Liang-Bi Chen,et al.  WristEye: Wrist-Wearable Devices and a System for Supporting Elderly Computer Learners , 2016, IEEE Access.

[25]  Bjoern M. Eskofier,et al.  An IMU-based mobile system for golf putt analysis , 2015 .

[26]  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).

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

[28]  Marko Kos,et al.  Pulse rate variability and blood oxidation content identification using miniature wearable wrist device , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).

[29]  John R Brotherhood,et al.  Match Characteristics of Professional Singles Tennis , 2005 .

[30]  Xingyu Wang,et al.  Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Dominik Schuldhaus,et al.  Inertial Sensor-Based Approach for Shot / Pass Classification During a Soccer Match , 2015 .

[32]  Anthony D. Whitehead,et al.  Gesture recognition using Markov Systems and wearable wireless inertial sensors , 2015, IEEE Transactions on Consumer Electronics.

[33]  Xingyu Wang,et al.  Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification , 2017, Int. J. Neural Syst..