Human-Robot Interaction (HRI) increasingly integrates direct robot control, requiring precision and stability. In this study, we investigate the impact of natural human tremors - micromovements on real-time HRI and their interaction with robotic system vibrations. Tremors, involuntary rhythmic muscle movements, can affect fine motor tasks, particularly in high-precision applications. We analyze resting and action tremors, excluding pathological tremors. To examine these effects, we employ two synchronized accelerometers (MPU6050) to capture tremor-induced micromovements: one on the human hand or controller and another on the robot’s end effector. Experiments involve the Fanuc LR Mate 200ic and UR5 robots, controlled via the Mediapipe Hands module and HTC Vive VR controller. We analyze accelerometer data using Fast Fourier Transform (FFT). Our findings contribute to mitigating unwanted oscillations in HRI, paving the way for adaptive filters and machine learning approaches to enhance real-time robot control. By minimizing tremor-induced micromovements, we aim to achieve seamless, embodied interaction, optimizing robotic precision while preserving human fine motor skills and dexterity in collaborative settings.