Intrinsic hand muscles and tendons are crucial for joint stabilization, fine motor control, and coordinating flexion—functions essential for performing dexterous tasks such as typing, grasping, and tool manipulation. However, monitoring strength and real-time activities remains challenging. Surface electromyography (EMG) struggles to isolate signals from interior tissue due to low signal-to-noise ratios. Devices like the Rotterdam Intrinsic Hand Myometer measure strength but are cumbersome for continuous monitoring. Electrical Impedance sensing offers a promising alternative. This technique passes a low-frequency electrical current through electrode pairs (injectors and receivers) on the skin and measures the resulting voltage changes to model tissue impedance. Through this approach, we can track and classify the activity of hand muscles and tendons in real-time, targeting the challenge of capturing signals within the hand. Our approach integrates a custom conductive fabric electrode array into a wearable form, such as a glove or a flexible bandage, to detect impedance variation with muscle contractions. These signals are processed through a regression-based machine-learning algorithm that predicts hand poses. A dynamic simulation further visualizes the motion and corresponding muscle activity, providing feedback on intrinsic muscle coordination. By offering real-time monitoring of deeper musculoskeletal dynamics, our system opens new avenues for analyzing muscle function and optimizing performance. Beyond research, this system can inform a range of applications—from enhancing human-computer interaction and prosthetic control to supporting personalized rehabilitation protocols. Looking ahead, we plan to optimize electrode designs for improved comfort and precision and to incorporate advanced machine-learning techniques for enhanced pose prediction. Through refinements, we aim to make EIS-based hand muscle monitoring a versatile tool for researchers, clinicians, and innovators across diverse fields.