Learning from Demonstration (LfD) facilitates the transfer of human skills to robots through the analysis of motion-based demonstrations, but most approaches rely only on kinematics, limiting their use in surgical applications where interaction forces are critical. This work proposes a multimodal LfD framework that integrates position, velocity, orientation, force and torque data into a Hidden Markov Model (HMM). Thirty-three demonstrations of a puncture task on a deformable surface were collected from 11 participants and used to train the model. The UR3e robot reproduced the learned trajectory with sub-millimeter accuracy (RMSE = 0.1127 mm) and replicated key force patterns observed in human demonstrations. Results demonstrate the feasibility of incorporating force information into LfD, enhancing trajectory learning and contributing to the development of intelligent robotic systems for intraoperative assistance.