Title: Finger Grip Force Estimation from Video using Two Stream Approach,
Abstract: Estimation of a hand grip force is essential for the understanding of force
pattern during the execution of assembly or disassembly operations. Human
demonstration of a correct way of doing an operation is a powerful source of
information which can be used for guided robot teaching. Typically to assess
this problem instrumented approach is used, which requires hand or object
mounted devices and poses an inconvenience for an operator or limits the scope
of addressable objects. The work demonstrates that contact force may be
estimated using a noninvasive contactless method with the help of vision system
alone. We propose a two-stream approach for video processing, which utilizes
both spatial information of each frame and dynamic information of frame change.
In this work, image processing and machine learning techniques are used along
with dense optical flow for frame change tracking and Kalman filter is used for
stream fusion. Our studies show that the proposed method can successfully
estimate contact grip force with RMSE < 10% of sensor range (RMSE $\approx 0.2$
N), the performances of each stream and overall method performance are
reported. The proposed method has a wide range of applications, including robot
teaching through demonstration, haptic force feedback, and validation of human-
performed operations.