상세정보
학술대회
Multi-feature based Object Classification using Flexible Gloves inspired by Human Grasping
- 발행일
- 202110
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.1728-1730
- 협약과제
- 21HB1400, 고압전성 복합소재 및 초저전력 적층형 압전 센서/액추에이터 복합모듈 기술 개발, 김혜진
- 초록
- We present high accuracy object classification using flexible gloves and machine learning algorithms. The flexible gloves are designed with two flex sensors mounted on finger joints and two FSR sensors inside fingertips. When grasping an object, electrical signals are acquired from physically deformed sensors. In this paper, the key features of objects are extracted from the mean and standard deviation values of the sensing signal waveforms. We prepared four sets of blocks for classification and each of them had a different size and weight. As a result, we demonstrated the accuracy of the object classification can be achieved 100 % using the multi-featured sensing dataset acquired by the flexible glove. The multi-featured classification method which combines the flexible gloves and machine learning technology shows a great potential application such as visual impairment aid and human-machine interface.
- KSP 제안 키워드
- Classification method, Different sizes, Electrical signal, High accuracy, Human-machine interaction(HMI), Key features, Machine Interface, Machine Learning Algorithms, Object classification, Potential applications, Sensing signal