Abstract
This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.
Highlights
A large number of systems that rely on hand gestures as input technologies for wearable computers were proposed during the past two decades of research
The proposed glove system’s gesture recognition capabilities are examined in detail on data collected from 57 study participants performing gestures from the french sign language (LSF) alphabet, in order to validate how well such a system would work for larger sets of gestures
This article has contributed with a data glove design that enables the on-glove detection of fine-grained hand shapes based on Inertial Measurement Units (IMUs) sensors on all fingertips
Summary
A large number of systems that rely on hand gestures as input technologies for wearable computers were proposed during the past two decades of research The advantages of these gestures include that they are easy to learn and enable the adoption of common gestures used in everyday life or existing alphabets for sign languages, relying on a significant user base. As these gestures tend to use the full articulation of the hand, requiring the detection of the exact position and motion of all fingers, such systems are not straightforward to implement. A large amount of data gloves and glove-based systems have been proposed for interaction methods, though most of these have been focusing on sensors that measure bending [2,3,4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
