Recent years have witnessed the flourish in research and development efforts towards clinical rehabilitation systems in smart home applications. One of the most prior is that such systems are needed to provide real-time motion information about patients. In this paper, a motion detecting approach is proposed for efficiently understanding the human movement on the foundation of the Internet of Things (IoT) based architecture. Specifically, the detection algorithm is based on the fuzzy neural network (FNN), which learns to detect the variation among different gait phases. The moving phases as well as the instability of the tester are identified for recognition. On the tasks of identifying the subject motion using wearable sensing devices, this model achieves a significant high accuracy. Experimental results prove that the system is feasible for application designs and could be implemented on technological platforms.
This entry was posted on December 10, 2019, 22:41 and is filed under Gait Rehabilitation - Foot Drop, REHABILITATION. You can follow any responses to this entry through RSS 2.0. You can leave a response, or trackback from your own site.