Meniscal surgery is considered the most general orthopedic process that deals with the treatment of meniscus tears for human health care. It leads to a communal contusion to the cartilage that stabilizes and cushions the knee joints of human beings. Such tears can be classified into different categories based on age group, region, and occupation. Further, a large number of sportsmen and heavy weightlifters even in developed countries are affected by meniscus injuries. These patients are subjected to arthroscopic surgery, and during surgical treatment, the perseverance of meniscus is a very crucial task. Current research provides a significant ratio of meniscal tear patients around the globe, the critical expanse is considered as having strikingly risen with a mean annual of 0.066% due to surgery failure. To decumbent this ratio, an innovative training mechanism is proposed through video retrieval system in this research. This research work is focussed on developing a corpus and video retrieval system for meniscus surgery. Using the proposed system, surgeons can access guidance by watching the videos of surgeries performed by an expert and their seniors. The proposed system is comprised of four approaches to the spatiotemporal methodology to improve health care services. It entails key point, statistical modeling, PCA-scale invariant feature transform (SIFT), and PCA-Gaussian mixture model (GMM) with a combination of sparse-optical flow. The real meniscal surgery dataset is used for testing purposes and evaluation. The results conclude that using PCA-SIFT approach improves the results with an average precision of 0.78.
HandVR: a hand-gesture-based interface to a video retrieval system