IoT Networks and Online Image Processing in IMU-Based Gait Analysis

Gait analysis is a comprehensive anatomical methodology, involves neural, musculoskeletal, and cardiorespiratory systems, utilized for diverse clinical objectives. The analysis techniques in gait analysis has evolved due to biomedical and electrical-electronic engineering progress, transitioning away from conventional visual techniques toward integrated solutions merging sensor and image processing methods. Integration of the Internet of Things technologies into this field has allowed the use of sensor and image processing techniques together in gait analysis which helped clinicals to follow the process more efficiently and to obtaining the results faster. This study presents an IoT-infrastructured system for gait analysis and explains the used IoT technologies, sensor fusion methods, sensor-camera calibration techniques, and an interface for clinical data monitoring. In addition, the actual application and experiment results for a patient are compared with the medical gold standard results of the same patient and the results produced by the sensor-camera integration at various rates are shown in this paper. Experiment results show that the developed gait analysis system provides correlated outputs with same patient’s golden standart results without the effect of camera synchronization. The camera synchronization improves the gait data in various joints for a interval of integration while it does not for other cases.