Tazid Ali

Papers

Total Citations

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About

Tazid Ali is a researcher in rehabilitation robotics, with a primary focus on object recognition and tactile sensing for prosthetic hands. His work addresses the critical challenge of enabling prosthetic devices to autonomously identify and grasp objects effectively, a key step toward restoring dexterous function for amputees. Ali’s most-cited paper, “Object Recognition based on Surface Detection - A Review” (2018, 7 citations), provides a comprehensive survey of vision-based and sensor-driven approaches for prosthetic grasping, highlighting the limitations and potential of current technologies. Building on this foundation, his 2022 study, “Shape oriented object recognition on grasp using features from enclosure based exploratory procedure” (4 citations), introduces a novel method that leverages exploratory touch—specifically enclosure-based procedures—to recognize object shape during grasping. This work advances the field by integrating tactile feedback with shape recognition, moving beyond purely visual systems. Ali’s contributions are particularly notable for their emphasis on practical, sensor-driven solutions that enhance the autonomy and adaptability of prosthetic hands, making his research valuable for engineers and clinicians developing next-generation assistive devices.