Seoul National University Professor Jooeun Ahn's collaboration research team develops grasp enhancing gloves using a single sensor

With a collaboration study of Department of Physical Education, Seoul National University (Professor Jooeun Ahn), Department of Mechanical Engineering, Seoul National University (Professor Kyu-Jin Cho), School of Computing, KAIST (Professor Sungho Jo), and Seoul National University Bundang Hospital (Professor Hyunsik Gong), Exo-Glove Power (EGPO), a compact/portable soft robotic glove for augmenting the grasping force while the user intends a firm power grasp, has been developed.
When carrying heavy objects or handling tools such as hammers, drills and saws in everyday life, hand grasp is very important for stable work performance. In particular, in a disaster site such as a fire or a work site where various power tools are used, grasping a tool or object tightly is frequently required. Meanwhile, if the grasping action is continued for a long time, fatigue builds up in the arm muscles, which makes it difficult to perform stable work. However, current myoelectric interfaces based on surface EMG often fail to achieve these requirements by demanding multiple sensors and exhibiting unreliable performance under limb posture changes.
The collaboration research team found that the myoelectric signals from the MTJs of the FDS show significantly increased amplitudes exclusively when a power grasp is performed, regardless of the arm posture. The research team systematically verified that, in identifying power grasp intentions, the proposed single-sensor myoelectric interface even outperforms a five-sensor myoelectric interface around the proximal forearm. By exploiting the unique biological feature of the MTJs, the research team devised two myoelectric control methods for a robotic glove, Dual-threshold control and Morse-code control.
The outcome of this study is an example of future technology development through the convergence of expertise in various fields such as new discoveries related to the human body, kinematic experiments, machine learning, and soft robot development. In particular, the research results showing that accurate power grasp intention identification can be enhanced with only a single sensor are expected to make an important contribution to the development of practical wearable robots for enhancing physical capabilities in the future.