İnsan Yürüyüşünün Yapay Zekayla Sınıflandırılması: Sistemik Bir Gözden Geçirme.

Gait is a gain that has a cyclic process studied as a phase of posture and oscillation. Gait is a subjective approach that is quite difficult to classify and describe with naked eye. A lot data about walking is obtained with development of imaging and sensor technology. The multiplicity and complexity of data can lead to errors in its interpretation. During Data Processing, Artificial Intelligence (AI) are preferred. AI has been preferred in studies due to its determination of compatibility between data and its ability to easily incorporate new data into system. The large dataset makes it easier to train AI and interpret newly issued data. The algorithm to be used may vary depending on variety of dataset. This can affect learning and interpretation success of AI. Before developing AI application, first process to be applied is selection of algorithm that will provide maximum performance according to desired result from dataset. In this study, literature studies that detect and classify walking using AI algorithms were examined. It is aimed to determine success rate of AI in gait classification, to determine optimal gait parameter for AI, to determine ideal environment for gait and appropriate algorithm. During the selection of studies in literature, 68 studies were reached between 2002-2020, taking into account innovations in algorithms and computer technology. 17 studies before 2006 were not included because they were studies of determining algorithms in a computer environment. Studies with people who did not have any barriers were included, without taking into account the number of samples. All studies using data acquisition methods are included, except for single-insert wearable-device technology. In gait recognition and classification, data from camera and baropedometer were found to be highly successful using multiple-algorithms. It has been shown that using multiple-algorithms will achieve most success in defining the gait. [ABSTRACT FROM AUTHOR]