Human computer interaction: an operational definition
ACM SIGCHI Bulletin
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Detection of eating and drinking arm gestures using inertial body-worn sensors
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Accelerometer-based gesture control for a design environment
Personal and Ubiquitous Computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Neural-network feature selector
IEEE Transactions on Neural Networks
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
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Gestures, due to their natural modality, can be normally used in human-computer interaction (HCI) domains such as robotics, design environments and handheld devices. In this paper, a single wrist-mounted triaxial accelerometer is used to collect the acceleration data generated by hand movements forming 36 different gestures. This study intends to find the gestures which are capable of controlling an appliance with a maximum accuracy. A neuro-fuzzy classifier is devised for gestures detection to improve the classification rate in relative terms. The neuro-fuzzy system also selects the best features which yield the highest rate of classification. It reduces the dimensionality of feature set in two phases; the first phase is before carrying out the classification and the second phase is after selecting the most suitable gestures. The feature selection process finally reduces the number of features from 120 to 19. Our neuro-fuzzy system detects 25 gestures that can be classified with an accuracy of 100%, which is the highest rate among other classifiers. So, since the gesture-based control is accurately performed, it can be a proper method for HCI applications.