Wearable sensor based acoustic gait analysis using phase transition-based optimization algorithm on IoT

Gait monitoring with IOT has emerged as an important area of research because of the need of assessment of daily activities of patients and elder people. Ailments such as Parkin's stroke and the need of monitoring physically challenged persons in a crowd have been the driving force in the research of gait analysis. The evaluation of athletic performance is yet another area of application. Current measurement techniques rely on gait parameters, and the accuracy due to different gait-related occurrences is very restricted. Many sophisticated sensor-based gait patterns were established to keep the patient from falling and alerting in an emergency. The main objective of this research endeavour paper is to utilize phase transition based optimization in IOT environment for developing characteristic phases which maybe stable, unstable or Meta stable. The method proposed by IOT is used to detect early stage failure to monitor by data produced by signals interacted with wearable sensors. Moreover, optimisation is performed for forecasting and detecting fall more effectively in comparison with conventional gait analysis. In this phase transition based optimization fitness function of the subject is defined by degrees of order and disorder. Similar to genetic algorithm, the elements of individual nodes are considered based on initial population and size. The current generation evolves the next through operators along with terminal condition. For high fitness value, the stability is worse and based on fitness, the 3 phases are defined. For the experimentation, real time data of 50 participants having 20 elder persons and 20 physically challenged persons with other from stroke cases is processed on MATLAB 14.1. Sensors are placed at leg, hip and toe: the collected data are processed in the processing unit before classification. Following cuckoo search method with many iterations. False alarm rate probability and detection probability are plotted using the ROC and having a threshold between these on the histogram in dynamic range. It is observed that the proposed method has less false ratio and greater accuracy in comparison with KNN 88% and HMDTW models. Moreover, the average precision of 96.42% is achieved by this method; the maximum detection rate is 96% for given gait cycle. It is inferred that phase transition and adaptive cuckoo search method can be effectively combined so give better classification accuracy, detection sets and time of duration. Interpolated IOT adds to the effectiveness of the proposed system to the extent of accuracy of 98.44% and false ratio of 2.02%.

Automated summary

This research utilizes IoT technology for gait monitoring and has achieved good results in terms of stability, accuracy, and prediction. By optimizing methods and interacting with sensor data, it can more effectively monitor and predict the gait performance of patients.