Evaluation of Models for Gesture Recognition from Biometric Signals of a Person with Reduced Mobility

Keywords: Gesture recognition, Human computer interaction, Signal processing, Machine learning, Pattern recognition


This paper compares the results of three computational models (pattern recognition, hidden Markov models, and bag of features) for recognizing the hand gestures of a user with reduced mobility using biometric signal processing. The evaluation of the models included eight gestures co-designed with a person with reduced mobility. The models were evaluated using a cross-validation scheme, calculating sensitivity and precision metrics, and a data set of ten repetitions of each gesture. It can be concluded that the bag-of-features model achieved the best performance considering the two metrics under evaluation; the traditional pattern recognition model, using vector support machines, produced the most stable results; and the hidden Markov models had the lowest performance.

Author Biographies

Holman S. Cabezas, Universidad Militar Nueva Granada, Colombia

Ingeniero en Multimedia, Grupo de Investigación en Multimedia -GIM, Facultad de Ingeniería, Universidad Militar Nueva Granada, Bogotá-Colombia, u1201569@unimilitar.edu.co

Wilson J. Sarmiento*, Universidad Militar Nueva Granada, Colombia

PhD. en Ciencias de la Electrónica-Áreas Computación, Grupo de Investigación en Multimedia -GIM, Facultad de Ingeniería, Universidad Militar Nueva Granada, Bogotá- Colombia, wilson.sarmiento@unimilitar.edu.co


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How to Cite
Cabezas, H. S., & Sarmiento, W. J. (2019). Evaluation of Models for Gesture Recognition from Biometric Signals of a Person with Reduced Mobility. TecnoLógicas, 22, 33-47. https://doi.org/10.22430/22565337.1513


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