An embedded 6-axis sensor based recognition for tennis stroke

Pei, Weiping; Wang, Jun; Xu, Xubin; Wu, Zhengwei; Du, Xiaoqiang

doi:10.1109/icce.2017.7889228

Cited by 13 publications

(19 citation statements)

References 6 publications

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Section: Introductionmentioning

confidence: 99%

“…Identifying actions automatically in sport activities is important and numerous studies have been conducted for that purpose [7][8][9][10]. Wearable devices such as Inertial Measurement Units (IMUs) [11,12] are becoming increasingly popular for sports related action analysis because of their reasonable price as well as portability [10]. While researchers have proposed different configurations in terms of number and placement of sensors [13], it is ideal to keep the number of sensors to a minimum due to cost, setup effort and player's comfort [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors

Haider

Salim

Postma

et al. 2020

MTI

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Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort. Data from Inertial Measurement Units (IMU) could be used for automatically tagging video recordings in terms of players’ actions. However, the data gathered during volleyball sessions are generally very imbalanced, since for an individual player most time intervals can be classified as “non-actions” rather than “actions”. This makes automatic annotation of video recordings of volleyball matches a challenging machine-learning problem. To address this problem, we evaluated balanced and imbalanced learning methods with our newly proposed ‘super-bagging’ method for volleyball action modelling. All methods are evaluated using six classifiers and four sensors (i.e., accelerometer, magnetometer, gyroscope and barometer). We demonstrate that imbalanced learning provides better unweighted average recall, (UAR = 83.99%) for the non-dominant hand using a naive Bayes classifier than balanced learning, while balanced learning provides better performance (UAR = 84.18%) for the dominant hand using a tree bagger classifier than imbalanced learning. Our super-bagging method provides the best UAR (84.19%). It is also noted that the super-bagging method provides better averaged UAR than balanced and imbalanced methods in 8 out of 10 cases, hence demonstrating the potential of the super-bagging method for IMU’s sensor data. One of the potential applications of these novel models is fatigue and stamina estimation e.g., by keeping track of how many actions a player is performing and when these are being performed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors

Haider

Salim

Postma

et al. 2020

MTI

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Automatically identifying actions in sport activities is important for multiple reasons, therefore there have been numerous studies to identify actions in sports [1,12,13,15]. Wearable devices such as Inertial Measurement Units (IMUs) [2,14] are becoming increasingly popular for sports related action analysis because of their reasonable price as well as portability [13]. While researchers have proposed different configurations in terms of number and placement of sensors [17], it is ideal to keep the number of sensors to a minimum due to cost, setup effort and player's comfort [3,6,16,17].…”

Section: Introductionmentioning

confidence: 99%

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

Haider

Salim

Naghashi

et al. 2019

Adjunct of the 2019 International Conference on Multimodal Interaction

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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". CCS CONCEPTS • Human-centered computing → Interactive systems and tools; • Interaction paradigms → Web-based interaction.

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“…Four different principles, that are used for smart embedded solutions integration, exist for tennis: (a) a device is placed in a tennis racket handle [15]; (b) a device is mounted on the racket strings (like a string vibration dampener); (c) a device is attached on the racket grip (at the bottom end); and (d) a device is worn on tennis player's wrist. The option with a sensor being integrated into the racket handle is also the most expensive because one must buy a special tennis racquet.…”

Section: Introductionmentioning

confidence: 99%

Smart Wearables for Tennis Game Performance Analysis

Kos¹,

Kramberger²

2020

Sports Science and Human Health - Different Approaches

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For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete's body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player's wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes' arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed.

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An embedded 6-axis sensor based recognition for tennis stroke

Cited by 13 publications

References 6 publications

A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors

A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors

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

Smart Wearables for Tennis Game Performance Analysis

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