A machine learning approach for gesture recognition with a lensless smart sensor system
Susto, Gian Antoino
Hand motion tracking traditionally requires highly complex and expensive systems in terms of energy and computational demands. A low-power, low-cost system could lead to a revolution in this field as it would not require complex hardware while representing an infrastructure-less ultra-miniature (~ 100μm - ) solution. The present paper exploits the Multiple Point Tracking algorithm developed at the Tyndall National Institute as the basic algorithm to perform a series of gesture recognition tasks. The hardware relies upon the combination of a stereoscopic vision of two novel Lensless Smart Sensors (LSS) combined with IR filters and five hand-held LEDs to track. Tracking common gestures generates a six-gestures dataset, which is then employed to train three Machine Learning models: k-Nearest Neighbors, Support Vector Machine and Random Forest. An offline analysis highlights how different LEDs' positions on the hand affect the classification accuracy. The comparison shows how the Random Forest outperforms the other two models with a classification accuracy of 90-91 %.
Computer vision , Digital filters , Gesture recognition , Image motion analysis , Intelligent sensors , Learning (artificial intelligence) , Light emitting diodes , Object tracking , Stereo image processing , Support vector machines , Random Forest , Machine learning approach , Lensless smart sensor system , Hand motion tracking , Computational demands , Low-cost system , Complex hardware , Multiple Point Tracking algorithm , Tyndall National Institute , Gesture recognition tasks , Stereoscopic vision , IR filters , Tracking common gestures , Six-gestures dataset , Machine Learning models , Support Vector Machine , Low-power system , Infrastructure-less ultra-miniature solution , Hand-held LED , k-Nearest Neighbors , Sensors , Hardware , Radio frequency , Calibration , Lensless Smart Sensor , Machine Learning
Normani, N., Urru, A., Abraham, L., Walsh, M., Tedesco, S., Cenedese, A., Susto, G. A. and O'Flynn, B. (2018) 'A machine learning approach for gesture recognition with a lensless smart sensor system', 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Las Vegas, NV, USA, 4-7 March, pp. 136-139. doi: 10.1109/BSN.2018.8329677
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