Many major knee surgeries or injuries have long rehabilitation periods, during which the patients may have abnormal gait cycles. The patient transitions to independent exercises performed at home, which are crucial to recovery. Doctors need to have a means to closely track patients exercises for proper recovery. This research utilizes statistical analysis tools, which is applied to analyze gait parameters of patients undergoing the rehabilitation process at different stages of recovery. Open source electronics, which were used for their affordability, identify and datalog variables like orientation (yaw, pitch, roll and time) and GRF to calculate important gait parameters. These parameters are used to study the different aspects of the patient’s performance and aids doctors track the progress of the patients at critical recovery periods. This research eliminates use of intuitive examination, and provides quantitative insight on the patients’ rehabilitation. Metrics such as cadence, stance time, weight distribution are determined from the data obtained from the device. A series of experiments were conducted using these sensors to relate the gait parameters to the recovery of forty people in the age group of 18–24. The collected results were used to create recovery indices for all specified metrics, which aid the doctors to objectively identify the extent of recovery. From the analysis, it was determined that patients with recent or ongoing rehabilitation are 10% (injured 6 months ago) to 40% (people injured recently) from optimal. Confirmation diagnosis were conducted to verify the results of the recovery indices and there was close agreement between the analysis and actual diagnosis.