Control of artificial human finger using wearable device and adaptive network-based fuzzy inference system

Abstract

This paper demonstrates a new approach for the use of multiple strain sensors on a wearable flexible finger band to measure the posture and movement of a human finger accurately. The system is further developed to repeat the human finger motion on a robotic finger. Here, we used adaptive network-based fuzzy interface system (ANFIS) to relate the strain sensor readings to human finger posture and motion. The input and output measurements used to train ANFIS are obtained from the strain sensors of the wearable platform and a 3 degree of freedom (DOF) exoskeleton testbed, respectively. The ANFIS model is then used to predict human finger posture and motion directly from the strain sensors installed on the finger band. We made additional experiments and generated testing data using the exoskeleton testbed to verify the ANFIS model. Finally, we demonstrate that the robotic finger closely follows the human finger motion by reading the wearable finger band output and calculating the posture and motion parameters in real time.