This paper introduces a data processing method to enhance the performance of a gesture classification model. When tested on the UTD-MHAD dataset, the HMM model initially rendered a poor performance due to seemingly resembling gestures. To tackle this problem, data has been altered via normalization and selection of significant joints that determine the gesture. Refining data prior to classifying generates a better performance in both HMM and LSTM models, highlighting the significance of data processing across different types of classification models.
|Title of host publication||ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 2021|
|Event||24th International Conference on Electrical Machines and Systems, ICEMS 2021 - Gyeongju, Korea, Republic of|
Duration: 2021 Oct 31 → 2021 Nov 3
|Name||ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems|
|Conference||24th International Conference on Electrical Machines and Systems, ICEMS 2021|
|Country/Territory||Korea, Republic of|
|Period||21/10/31 → 21/11/3|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2020R1A2B5B01002395).
This work was supported by the National Research Foundation of Korea through the Korean Government (MSIT) under Grant (NRF-2020R1A2B5B01002395).
© 2021 KIEE & EMECS.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Mechanical Engineering
- Safety, Risk, Reliability and Quality