Sensor signal processing to extract features from finger temperature in a case-based stress classification scheme

One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, this pattern of the finger temperature sensor signal is so individual and in practice, it is difficult and tedious even for experienced clinicians to interpret and analyze the signal to classify individual stress levels. So there is an inherent need to develop methods or techniques providing computational solution to utilize this sensor signal in a computer-based system. This paper presents a feature extraction approach based on finger temperature sensor signal. The extracted features are then used to formulate cases in a case-based reasoning system to classify individual sensitivity to stress. The evaluation result shows an encouraging performance to apply the approach in feature extraction from slowly changing sensor signals such as finger temperature signal.