A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms - Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd (Gait & posture, 2017)
•IMU-based systems can acquire gait kinematics despite the lack of standardization.•AI algorithms can classify and group accurately subjects due to kinematic parameters.•Most applied, neural network algorithms present a better performance on gait data grouping. The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.