TennisEye

Zhao, Hongyang; Wang, Shuangquan; Zhou, Gang; Jung, Woosub

doi:10.1145/3302506.3310404

Cited by 15 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides wrist, data sensed from the racket was used as well. Zhao et al [31] place a sensor above the handle, and Pei et al [23] embedded it into the handle. [21] place a sensor on the upper back region of the handle.…”

Section: Related Workmentioning

confidence: 99%

“…As sensors, both self-built prototypes [18,20,22,23] and commercial sensing systems [4,6,7,21,28,29] have been used. Most frequently, the IMUs used process acceleration within ±16д, and the gyroscopes used a range of ±2000 deg s [18,23,29,31]. Anand et al [4] and Srivastava et al [28] use a smartwatch sensor with an acceleration range of ±8д, Brzostowski and Szwach [6] one with ±4д.…”

Section: Related Workmentioning

confidence: 99%

“…Büthe et al [7] record the gyroscope data with a range of ±500 deg s instead of ±2000 deg s . The utilized sampling rates range from 25Hz [28] over 100Hz [4,6,18,21,23,31], 120Hz [22], 200Hz [7], to 500Hz [29].…”

Section: Related Workmentioning

confidence: 99%

“…From a technical perspective, tennis strokes are mainly sensed with computer vision or wearable motion sensors [31]. The former can be expensive, complex, and their access can be limited [13].…”

mentioning

confidence: 99%

“…In our evaluation we utilize video data of a semi-professional tennis player to allow for error-free alignment and marking of the phases of the considered strokes (e.g. backswing, forward swing, the impact point of the ball, follow through, and the start and end of the execution [17,31]) in the sensed acceleration and angular velocity data. Based on this synchronization, we use inertial measurement unit (IMU) sensor data of multiple players to quantify detection and segmentation performance, in which players vary the execution speed and the target location of their tennis strokes.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Tennis Stroke Classification

Ebner

Findling

2019

Proceedings of the 17th International Conference on Advances in Mobile Computing &Amp; Multimedia

View full text Add to dashboard Cite

Automatic tennis stroke recognition can help tennis players improve their training experience. Previous work has used sensors positions on both wrist and tennis racket, of which different physiological aspects bring different sensing capabilities. However, no comparison of the performance of both positions has been done yet. In this paper we comparatively assess wrist and racket sensor positions for tennis stroke detection and classification. We investigate detection and classification rates with 8 well-known stroke types and visualize their differences in 3D acceleration and angular velocity. Our stroke detection utilizes a peak detection with thresholding and windowing on the derivative of sensed acceleration, while for our stroke recognition we evaluate different feature sets and classification models. Despite the different physiological aspects of wrist and racket as sensor position, for a controlled environment results indicate similar performance in both stroke detection (98.5%-99.5%) and user-dependent and independent classification (89%-99%). CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Visualization; • Computing methodologies → Machine learning; • Applied computing → Life and medical sciences.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Tennis Stroke Classification

Ebner

Findling

2019

Proceedings of the 17th International Conference on Advances in Mobile Computing &Amp; Multimedia

View full text Add to dashboard Cite

show abstract

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things

show abstract

Arrayed piezoresistive and inertial measurement unit sensor-integrated assistant training tennis racket for multipoint hand pressure monitoring and representative action recognition

Yang

Hou

et al. 2023

Sci. China Technol. Sci.

View full text Add to dashboard Cite

TennisEye

Cited by 15 publications

References 26 publications

Tennis Stroke Classification

Tennis Stroke Classification

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Arrayed piezoresistive and inertial measurement unit sensor-integrated assistant training tennis racket for multipoint hand pressure monitoring and representative action recognition

Contact Info

Product

Resources

About