Print Email Facebook Twitter Low-Power Gesture Recognition Using Convolutional Neural Networks and Ambient Lighting Title Low-Power Gesture Recognition Using Convolutional Neural Networks and Ambient Lighting Author de Beer, Arne (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Wang, Q. (mentor) Yang, M. (mentor) Zhu, R. (mentor) Venkatesha Prasad, Ranga Rao (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-07-03 Abstract This paper presents a study focused on developing an efficient signal processing pipeline and identifying suitable machine learning models for real-time gesture recognition using a testbed consisting of an Arduino Nano 33 BLE and three OPT101 photodiodes. Our research aims to address the challenges of limited computational power whilst maintaining a high inference accuracy. Experiments were conducted to optimise the signal processing and explore various machine learning model architectures, specifically revolving around convolutional neural networks. The data used for these experiments was gathered by creating a dataset of gestures from left- and right-handed participants. We took ethical considerations regarding participant recruitment and data security into account and we made sure to balance the dataset with both left- and right-handed participants as much as possible.We obtained accurate gesture recognition results, surpassing the goal of a 75% success rate. Our machine learning models, trained on pre-processed 2D data, achieved near real-time inference times while running on the resource-constrained Arduino Nano 33 BLE.The findings of this study contribute to the field of gesture recognition by providing insights into efficient signal processing techniques and identifying suitable machine learning models for resource-constrained devices. The developed system can be applied in various applications, ranging from games to healthcare. Furthermore, a dataset is contributed which can be used for further research. Subject deep learningneural networkconvolutional neural networkembedded systemsmachine learninggesture recognitionlow-powermultivariate time seriesambient lightTensorflowTensorFlow Lite for MicrocontrollersphotodiodeArduinoArduino Nano 33 BLEmicrocontroller To reference this document use: http://resolver.tudelft.nl/uuid:4688225e-7c50-49bc-b5eb-7e3d2ac18f9d Part of collection Student theses Document type bachelor thesis Rights © 2023 Arne de Beer Files PDF CSE3000_Final_Paper_53360 ... e_Beer.pdf 5.01 MB Close viewer /islandora/object/uuid:4688225e-7c50-49bc-b5eb-7e3d2ac18f9d/datastream/OBJ/view