Authors:
Andualem T. Maereg
1
;
Yang Lou
2
;
Emanuele L. Secco
1
and
Raymond King
2
Affiliations:
1
Robotics Lab, Liverpool Hope University, Liverpool, U.K.
;
2
Facebook Reality Labs, Redmond, WA, U.S.A.
Keyword(s):
Hand Gesture, NIR, Human-machine Interaction (HCI), Bio-sensing, Virtual-reality, Wearable Sensing.
Abstract:
Wrist-worn gesture sensing systems can be used as a seamless interface for AR/VR interactions and control of various devices. In this paper, we present a low-cost gesture sensing system that utilizes near Infrared Emitters (600 - 1100 nm) and Photo-Receivers encompassing the wrist to infer hand gestures. The proposed system consists of a wristband comprising Infrared emitters and receivers, data acquisition hardware, data post-processing software, and gesture classification algorithms. During the data acquisition process, 24 near Infrared Emitters are sequentially switched on around the wrist, and twelve Photo-diodes measure the light reflected, refracted, and scattered by the tissues inside the wrist. The acquired data corresponding to different gestures are labeled and input into a machine learning algorithm for gesture classification. To demonstrated the accuracy and speed of the proposed system, real-time gesture sensing user studies were conducted. As a result of this comparison
, we obtained an average accuracy of 98.06% with standard deviation of 1.82%. In addition, we evaluated that the system can perform six-eight gestures per second in real time using a desktop computer operating with Core i7-7800X CPU at 3.5GHz and 32 GB RAM.
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