Research on mobile machine learning platforms for human gesture recognition in human-machine interaction systems - Barkovska, Olesia; Ruban, Igor; Tymoshenko, Daria; Holovchenko, Oleksandr; Yankovskyi , Oleksandr (Technology audit and production reserves, 2025)
The subject of this research is mobile machine learning platforms for human gesture recognition within human-machine interaction systems, specifically for managing smart home components. One of the key challenges in gesture recognition is ensuring high accuracy, efficiency, and robustness of algorithms under real-world operating conditions. The problem lies in selecting optimal machine learning platforms capable of balancing local and cloud computing, processing speed, and adaptability to changing environmental conditions. The study presents a comparative analysis of the ML platforms Create ML (Apple) and Google Cloud AI Platform, which are used for gesture detection and recognition in smart home control systems. The obtained results demonstrate that Create ML achieves an accuracy of 95.81 %, while Google Cloud AI Platform reaches 89.43%, justifying their selection for further research. Additionally, experimental testing of sensor placement topology revealed that diagonal camera positioning increases accuracy by 0.62 % compared to parallel placement. The increased efficiency of Create ML is due to its ability to process data locally, reducing latency and dependence on an internet connection. In contrast, Google Cloud AI Platform relies on cloud resources, enabling the processing of large volumes of data but making it dependent on data transmission speed. The proposed gesture control algorithms can be used to enhance the accessibility of technology for people with disabilities, particularly in rehabilitation centers. Additionally, the research findings can be applied to contactless interfaces in medical facilities and public spaces, reducing the need for physical interaction with surfaces and improving hygiene levels. The use of mobile ML platforms in such scenarios allows for the optimization of computational resources and ensures the effective integration of gesture control into modern human-machine systems.