Gesture Recognition for Interactive Presentation Control: A Deep Learning and Edge Computing Approach on Raspberry Pi
Year: 2024
Type: article
Abstract: Gesture recognition is one of the important tech- nologies for intuitive HCI applications, for ex- ample, in interactive presentations. This re- view focuses on recent work on gesture recog- nition systems, deep learning-based methods for presentation control by hand gestures, on edge devices like the Raspberry Pi. This work de- scribes both static and dynamic frameworks for gesture recognition and human-machine interac- tion techniques based on deep learning.Recent studies show the usability of real-time hand gesture recognition using Python-based systems via Raspberry Pi, stressing portability and ef- ficiency in solutions. The review discusses var- ious deep learning frameworks such as Tensor- Flow, which can be used in image preprocess- ing and model training to enhance the accuracy of gesture detection. Techniques cover wear- able devices to computer vision and demon- strate the flexibility of gesture recognition in a variety of hardware platforms. Advances in edge computing enable complex gesture recog- nition on low- power devices, with reduced la- tency, and enables improved accessibility.The re- view addresses challenges such as gesture type distinction, responsiveness, and environmental variability in developing reliable gesture-based systems. In this regard, it emphasizes the Rasp- berry Pi as an edge computing solution for in- teractive presentation control in HCI. Keywords Gesture Recognition Human-Computer Inter- action (HCI) Edge Computing Deep Learning Real-Time Gesture Detection
DOI: 10.55041/ijsrem39636
Authors: Prashanth N, K T Anchan Poovanna, Gaurav Raj, Preetham S, Qurrath Ul Ayen
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405395116