Recognition of Material Surfaces with Smart Gloves Based on Machine Learning

Nowadays, wearable devices have been extensively studied in the fields of human-robot interaction and motion detection. As a wearable device, smart glove plays a significant role in intellisense. In this paper, a prototype of smart gloves is made by attaching an ultra-thin, sensitive and stretchable...

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Bibliographic Details
Published in2021 4th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) pp. 224 - 228
Main Authors Liu, Yajun, Zhang, Shenchao, Luo, Qi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2021
Subjects
characteristic extraction
Data acquisition
Frequency-domain analysis
Machine learning
Machine learning algorithms
material recognition
Motion detection
Prototypes
Smart gloves
Wearable computers
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Summary:Nowadays, wearable devices have been extensively studied in the fields of human-robot interaction and motion detection. As a wearable device, smart glove plays a significant role in intellisense. In this paper, a prototype of smart gloves is made by attaching an ultra-thin, sensitive and stretchable ZNS-01 sensor to the surface of ordinary gloves and a data acquisition system is built to collect the data of human hands to touch the surface of the table, wood, plastic, steel, and paper. Time domain characteristics and frequency domain characteristics are extracted as input to train different machine learning models. The prediction results of different machine learning algorithms are compared and the XGBoost shows strong generalization ability and the prediction effect is better. This smart glove can provide a reliable reference for material recognition.
DOI:10.1109/WCMEIM54377.2021.00054