Augmented feedback of relevant training parameters is of great interest for athletes and coaches. This is especially the case if parameters cannot be visually determined and if small value deviations change the training effects. In plyometric training, the ground contact time was identified as crucial measures to monitor and optimize training. This project investigates the ground contact measurement of the drop jump with Inertial Measurement Unit measurements and a Hidden Markov Model (HMM) analysis. Different approaches to increase the jump phase detection performance like signal-shift boosting and more complex HMM modeling were investigated. Our algorithms achieved an absolute ground contact time error of 12.3 ms (signal-shift boosting) and 4.3 ms (complex HMM model) on a dataset of 8 subjects (80 jumps). The proposed measurement setup and analysis algorithm can be used to create a wearable augmented feedback system for plyometric training practice.