arXiv:2512.07997v2 Announce Type: replace-cross Abstract: Gestures are an integral part of our daily interactions with the environment. Hand gesture recognition (HGR) is the process of interpreting human intent through various input modalities, such as visual data (images and videos) and bio-signals. Bio-signals are widely used in HGR due to their ability to be captured non-invasively via sensors placed on the arm. Among these, surface electromyography (sEMG), which measures the electrical activity of muscles, is the most extensively studied modality. However, less-explored alternatives such a
This research is emerging now due to the accelerating advancements in AI and sensor technology, making sophisticated bio-signal interpretation more accessible and accurate for human-computer interaction.
This study advances the foundational technology for more intuitive and robust human-machine interfaces, which is critical for the development and deployment of agentic systems and robotics.
The improved understanding and comparison of different bio-signal modalities for gesture recognition provide clearer pathways for developing more reliable and user-friendly control mechanisms, especially for AI and robotic applications.
- · AI hardware developers
- · Robotics companies
- · Wearable tech companies
- · HCI researchers
- · Traditional input device manufacturers
- · Companies reliant on less intuitive control methods
Enhanced gesture recognition leads to more natural and precise control over complex systems.
Improved HGR could accelerate the adoption of robotic and AI agents in various industries, from manufacturing to healthcare.
Widespread adoption of intuitive bio-signal-based interfaces might redefine disability assistance and general human-computer interaction paradigms, pushing human-AI collaboration to new levels.
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