|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM346945259 |
| 003 |
DE-627 |
| 005 |
20250303215842.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TVCG.2022.3209352
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1156.xml
|
| 035 |
|
|
|a (DE-627)NLM346945259
|
| 035 |
|
|
|a (NLM)36179004
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Wang, Jiachen
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Tac-Trainer
|b A Visual Analytics System for IoT-based Racket Sports Training
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 05.04.2023
|
| 500 |
|
|
|a Date Revised 05.04.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Conventional racket sports training highly relies on coaches' knowledge and experience, leading to biases in the guidance. To solve this problem, smart wearable devices based on Internet of Things technology (IoT) have been extensively investigated to support data-driven training. Considerable studies introduced methods to extract valuable information from the sensor data collected by IoT devices. However, the information cannot provide actionable insights for coaches due to the large data volume and high data dimensions. We proposed an IoT + VA framework, Tac-Trainer, to integrate the sensor data, the information, and coaches' knowledge to facilitate racket sports training. Tac-Trainer consists of four components: device configuration, data interpretation, training optimization, and result visualization. These components collect trainees' kinematic data through IoT devices, transform the data into attributes and indicators, generate training suggestions, and provide an interactive visualization interface for exploration, respectively. We further discuss new research opportunities and challenges inspired by our work from two perspectives, VA for IoT and IoT for VA
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Ma, Ji
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Hu, Kangping
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhou, Zheng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Hui
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Xie, Xiao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wu, Yingcai
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 29(2023), 1 vom: 13. Jan., Seite 951-961
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnas
|
| 773 |
1 |
8 |
|g volume:29
|g year:2023
|g number:1
|g day:13
|g month:01
|g pages:951-961
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TVCG.2022.3209352
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 29
|j 2023
|e 1
|b 13
|c 01
|h 951-961
|