A Wearable Device to Detect in Real-Time Bimanual Gestures of Basketball Players During Training Sessions

[1]  Gerhard Tröster,et al.  Detection of eating and drinking arm gestures using inertial body-worn sensors , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[2]  Chee Siang Ang,et al.  weSport: Utilising wrist-band sensing to detect player activities in basketball games , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[3]  Good,et al.  Dynamics of mountain bicycles with rear suspensions: modelling and simulation , 1999 .

[4]  Jeffrey M. Hausdorff,et al.  Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls , 2012, PloS one.

[5]  Shengli Zhou,et al.  Gesture recognition for interactive controllers using MEMS motion sensors , 2009, 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems.

[6]  Robert Stanton,et al.  A Review of Player Monitoring Approaches in Basketball: Current Trends and Future Directions. , 2017, Journal of strength and conditioning research.

[7]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[8]  Thomas B. Schön,et al.  Using Inertial Sensors for Position and Orientation Estimation , 2017, Found. Trends Signal Process..

[9]  Sonja Stork,et al.  Movement Coordination in Applied Human-Human and Human-Robot Interaction , 2007, USAB.

[10]  Liming Chen,et al.  Dynamic sensor data segmentation for real-time knowledge-driven activity recognition , 2014, Pervasive Mob. Comput..

[11]  Holly A. Yanco,et al.  Classifying human-robot interaction: an updated taxonomy , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[12]  Daniel Arthur James,et al.  Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve , 2009 .

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

[14]  Billur Barshan,et al.  Human Activity Recognition Using Inertial/Magnetic Sensor Units , 2010, HBU.

[15]  David V. Thiel,et al.  An integrated swimming monitoring system for the biomechanical analysis of swimming strokes , 2011 .

[16]  Noel E. O'Connor,et al.  Classification of Sporting Activities Using Smartphone Accelerometers , 2013, Sensors.

[17]  C. Becker,et al.  Evaluation of a fall detector based on accelerometers: A pilot study , 2005, Medical and Biological Engineering and Computing.

[18]  L. Ransdell,et al.  Use of Integrated Technology in Team Sports: A Review of Opportunities, Challenges, and Future Directions for Athletes , 2014, Journal of strength and conditioning research.

[19]  Md. Zia Uddin,et al.  A Depth Camera-based Human Activity Recognition via Deep Learning Recurrent Neural Network for Health and Social Care Services , 2016, CENTERIS/ProjMAN/HCist.

[20]  K. Aminian,et al.  Physical activity monitoring based on accelerometry: validation and comparison with video observation , 1999, Medical & Biological Engineering & Computing.

[21]  Huosheng Hu,et al.  Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation , 2007, Int. J. Robotics Res..

[22]  Xavi Schelling,et al.  Accelerometer Load Profiles for Basketball-Specific Drills in Elite Players. , 2016, Journal of sports science & medicine.

[23]  Daniel Wundersitz,et al.  Construct Validity of Accelerometry-Derived Force to Quantify Basketball Movement Patterns , 2017, International Journal of Sports Medicine.

[24]  Robert Puers,et al.  Wireless Communication with Miniaturized Sensor Devices in Swimming , 2014 .

[25]  Guanglie Zhang,et al.  A gait recognition system for rehabilitation based on wearable micro inertial measurement unit , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[26]  Dominik Schuldhaus,et al.  Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset , 2013, PloS one.

[27]  Joseph A. Paradiso,et al.  A Distributed Wearable, Wireless Sensor System for Evaluating Professional Baseball Pitchers and Batters , 2009, 2009 International Symposium on Wearable Computers.

[28]  Andreu Català,et al.  Basketball Activity Recognition using Wearable Inertial Measurement Units , 2015, Interacción.

[29]  Kamiar Aminian,et al.  Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly , 2003, IEEE Transactions on Biomedical Engineering.

[30]  Peter H. Veltink,et al.  Ambulatory Position and Orientation Tracking Fusing Magnetic and Inertial Sensing , 2007, IEEE Transactions on Biomedical Engineering.

[31]  Gheorghe Sebestyen,et al.  Human activity recognition and monitoring for elderly people , 2016, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP).

[32]  Brian Caulfield,et al.  Automatic detection of collisions in elite level rugby union using a wearable sensing device , 2012, Sports Engineering.

[33]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[34]  Charles Markham,et al.  Weakly Supervised Training of a Sign Language Recognition System Using Multiple Instance Learning Density Matrices , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[35]  Paul Lukowicz,et al.  Gesture spotting with body-worn inertial sensors to detect user activities , 2008, Pattern Recognit..

[36]  Hugo Fuks,et al.  Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements , 2012, SBIA.

[37]  Einar Snekkenes,et al.  Gait Recognition Using Wearable Motion Recording Sensors , 2009, EURASIP J. Adv. Signal Process..

[38]  Hassan Ghasemzadeh,et al.  Sport training using body sensor networks: a statistical approach to measure wrist rotation for golf swing , 2009, BODYNETS.