Sensor based decision making inference system for remote health monitoring

Fall occurring in older people is of major concern in medical environment as they are more prone to unexpected and unpredictable falls. Such falls often leads to injury and death in elderly. Hence, an automated fall detection mechanism using multiple sensing and event detection methods are required to analyze the activities of elderly. This paper presents a system considering 3-axis accelerometer sensor to detect the fall in home environment. Intelligent modeling technique such as ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier is employed in this paper to detect the fall with reduced computational complexity and more accuracy. Using ANFIS, the data obtained from 3-axis accelerometer is classified under one of the five states (standing, sitting, walking, falling and lying) and backpropogation method is used for weight updation. Weighted average method which provides crisp value is used for de-fuzzification process. Features such as mean, median and standard deviation are considered for training the neural network. When the activity is recognized as fall, the patient heart rate and ECG are examined to detect abnormality and alarm is raised.