Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

Informatics

Chaithanya Kumar Mummadi¹  Philipp M. Scholl¹  Frederic Philips Peter Leo¹  Keshav Deep Verma¹  Shivaji Kasireddy¹  Jochen Kempfle²  Kristof Van Laerhoven² 

[1] Faculty of Engineering, University of Freiburg, 79085 Freiburg im Breisgau, Germany;Ubiquitous Computing, University of Siegen, 57068 Siegen, Germany;

关键词: gesture recognition;    data gloves;    inertial sensing;    hand articulation tracking;   

DOI  :  10.3390/informatics5020028

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown