Abstract
This paper aims to evaluate two machine learning techniques using low-frequency photoplethysmography (PPG) associated with motion sensors, accelerometers and gyroscopes, from wearable devices such as smartwatches in gesture recognition from the wrist and fingers motion. The applied method consider the application of second-order gradient calculation on PPG to identify motion artifacts and then create segments that will be used gesture classification, the classification process used a support vector machine and random forests trained using statistical features extracted from PPG and motion sensor signals. Preliminary evaluations show that frequencies of 25 Hz are suitable for the recognition process, achieving an F1-score of 0.819 for seven gesture sets.
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