Discomfort Monitoring System using IoT applied to a Wheelchair

A wide variety of wheelchairs are already available in the market. However, discomfort monitoring is also not a standard feature for it. There are several types of discomfort that wheelchair users experience. This study mainly focuses on feelings of distress, such as wetness discomfort due to human wastes like urine. It also focuses on the uneven distribution of pressure on the surface, resulting in pressure sores and monitoring the user's stress through heart rate analysis and skin conductance. The discomfort monitoring system uses ECG, GSR, Wetness, and Pressure Sensors. With the ReLU activation function, the design used a neural network to predict the discomfort level felt by the user. IoT applications in the system include user detection, an LED indicator for the discomfort level, SMS alerts, and the execution of emergency calls. Based on the results, all the features extracted from the four sensors exhibited correlation to the discomfort felt by the user. The most correlated parameter to the discomfort level is from the ECG, next is pressure, followed by wetness, and lastly, GSR.