Data Trimming Methods to Improve Gesture Classification
- Resource Type
- Conference
- Authors
- Roh, Hye Sung; Kim, DaeEun
- Source
- 2021 24th International Conference on Electrical Machines and Systems (ICEMS) Electrical Machines and Systems (ICEMS), 2021 24th International Conference on. :2449-2452 Oct, 2021
- Subject
- Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Transportation
Refining
Hidden Markov models
Data processing
Data models
Robustness
Hidden Markov Model (HMM)
Data Preprocessing
Gesture Recognition
Human-Computer Interaction
- Language
- ISSN
- 2642-5513
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.