WITHDRAWN: Gait diagnosis using fuzzy logic with wearable tech for prolonged disorders of diabetic cardiomyopathy

Abstract

Abstract Gait patterns and coordination are altered in diabetic cardiomyopathy patients with Parkinson's disease. There is little information with possible explanations. Fall identification and simulation in all age groups, including the use of Fuzzy logic and sensor data for evaluating gait error. The current proposal emphasizes about fall prediction and estimation of diabetic cardiomyopathy disorders for aged, adults and infants. The inputs for the fluctuating inference system are the patient's height, gyroscope, age and weight. RMS error, Estimation and identification are the output variables where inputs are from the MIMO-used sensor data. Input and output variables and rules are supplemented with the membership functions. The extracted features are equivalent to ordinary random mean square error values. The application of a fuzzy Mamdani technique uses a triangular-trapezoidal logic to obtain a random mean square error, classification and recognition requirements for the elderly, adults and children. IoT monitors the collected outputs and store in cloud. The accuracy of a study through triangular trapezoidal approaches of 20 patients with Parkinson's diabetes cardiopathy in the fuzzy logic is 95%. A wide range of fields such as digital control, data detection, decision interpretation, expert systems and computer vision have been employed successfully.