1
|
Seong M, Kim G, Yeo D, Kang Y, Yang H, DelPreto J, Matusik W, Rus D, Kim S. MultiSenseBadminton: Wearable Sensor-Based Biomechanical Dataset for Evaluation of Badminton Performance. Sci Data 2024; 11:343. [PMID: 38580698 PMCID: PMC10997636 DOI: 10.1038/s41597-024-03144-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 03/14/2024] [Indexed: 04/07/2024] Open
Abstract
The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.
Collapse
Affiliation(s)
- Minwoo Seong
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Gwangbin Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Dohyeon Yeo
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Yumin Kang
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Heesan Yang
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea
| | - Joseph DelPreto
- Massachusetts Institute of Technology, CSAIL, Cambridge, MA, 02139, USA
| | - Wojciech Matusik
- Massachusetts Institute of Technology, CSAIL, Cambridge, MA, 02139, USA
| | - Daniela Rus
- Massachusetts Institute of Technology, CSAIL, Cambridge, MA, 02139, USA
| | - SeungJun Kim
- Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, 61005, South Korea.
| |
Collapse
|
2
|
Lei Y, Su Z, He X, Cheng C. Immersive virtual reality application for intelligent manufacturing: Applications and art design. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4353-4387. [PMID: 36896503 DOI: 10.3934/mbe.2023202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.
Collapse
Affiliation(s)
- Yu Lei
- College of Humanities and Arts, Hunan International Economics University, Changsha, 410205, China
| | - Zhi Su
- Department of Information, School of Design and Art Changsha University of Science and Technology, Changsha 410076, China
| | - Xiaotong He
- Weihai Institute for Bionics, Jilin University, 264402, Weihai, China
| | - Chao Cheng
- Weihai Institute for Bionics, Jilin University, 264402, Weihai, China
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, 130022, Changchun, China
| |
Collapse
|
3
|
Adriana Cárdenas-Robledo L, Hernández-Uribe Ó, Reta C, Antonio Cantoral-Ceballos J. Extended reality applications in industry 4.0. – A systematic literature review. TELEMATICS AND INFORMATICS 2022. [DOI: 10.1016/j.tele.2022.101863] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|