Design of a Wireless 3D Hand Motion Tracking and Gesture Recognition Glove for Virtual Reality Applications

2017 ◽  
Author(s):  
Jen-Hsuan Hsiao ◽  
Yu-Heng Deng ◽  
Tsung-Ying Pao ◽  
Hsin-Rung Chou ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Virtual Reality ◽  
Gesture Recognition ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Computer Control ◽  
Recognition Algorithm ◽  
Training Time ◽  
Analog To Digital ◽  
Data Glove ◽  
Hand Motion

Hand motion tracking and gesture recognition are of crucial interest to the development of virtual reality systems and controllers. In this paper, a wireless data glove that can accurately sense hands’ dynamic movements and gestures of different modes was proposed. This data glove was custom-built, consisting of flex and inertial sensors, and a microcontroller with multi-channel ADC (analog to digital converter). For the classification algorithm, a hierarchical gesture system using Naïve Bayes Classifier was built. This low training time recognition algorithm allows categorization of all input signals, such as clicking, pointing, dragging, rotating and switching functions when performing computer control. This glove provided a more intuitive way to operate with human-computer interface. Some preliminary experimental results were presented in this paper. The data glove was also operated as a controller in a First-Person Shooter (FPS) game to perform the usability of the proposed glove.

scholarly journals Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements

Sensors ◽  
2015 ◽  
Vol 15 (12) ◽  
pp. 31644-31671 ◽  
Author(s):  
Ewout Arkenbout ◽  
Joost de Winter ◽  
Paul Breedveld
Keyword(s):  
Data Fusion ◽  
Motion Tracking ◽  
Data Glove ◽  
Hand Motion ◽  
Kinect Camera

Model-based articulated hand motion tracking for gesture recognition

1998 ◽  
Vol 16 (2) ◽  
pp. 121-134 ◽  
Author(s):  
Cheng-Chang Lien ◽  
Chung-Lin Huang
Keyword(s):  
Gesture Recognition ◽  
Motion Tracking ◽  
Hand Motion ◽  
Model Based

scholarly journals A Modular Data Glove System for Finger and Hand Motion Capture Based on Inertial Sensors

2018 ◽  
Vol 39 (4) ◽  
pp. 532-540 ◽  
Author(s):  
Bor-Shing Lin ◽  
I-Jung Lee ◽  
Pei-Ying Chiang ◽  
Shih-Yuan Huang ◽  
Chih-Wei Peng
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Data Glove ◽  
Hand Motion ◽  
Modular Data

scholarly journals Low-Latency Haptic Open Glove for Immersive Virtual Reality Interaction

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3682
Author(s):  
DongHyun Sim ◽  
Yoonchul Baek ◽  
MinJeong Cho ◽  
Sunghoon Park ◽  
A. S. M. Sharifuzzaman Sagar ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motion Tracking ◽  
Absolute Error ◽  
Sensor Data ◽  
Low Latency ◽  
Haptic Devices ◽  
Hand Motion ◽  
Finger Flexion ◽  
Flexion Extension ◽  
Haptic Technology

Recent advancements in telecommunications and the tactile Internet have paved the way for studying human senses through haptic technology. Haptic technology enables tactile sensations and control using virtual reality (VR) over a network. Researchers are developing various haptic devices to allow for real-time tactile sensation, which can be used in various industries, telesurgery, and other mission-critical operations. One of the main criteria of such devices is extremely low latency, as low as 1 ms. Although researchers are attempting to develop haptic devices with low latency, there remains a need to improve latency and robustness to hand sizes. In this paper, a low-latency haptic open glove (LLHOG) based on a rotary position sensor and min-max scaling (MMS) filter is proposed to realize immersive VR interaction. The proposed device detects finger flexion/extension and adduction/abduction motions using two position sensors located in the metacarpophalangeal (MCP) joint. The sensor data are processed using an MMS filter to enable low latency and ensure high accuracy. Moreover, the MMS filter is used to process object handling control data to enable hand motion-tracking. Its performance is evaluated in terms of accuracy, latency, and robustness to finger length variations. We achieved a very low processing delay of 145.37 s per finger and overall hand motion-tracking latency of 4ms. Moreover, we tested the proposed glove with 10 subjects and achieved an average mean absolute error (MAE) of 3.091∘ for flexion/extension, and 2.068∘ for adduction/abduction. The proposed method is therefore superior to the existing methods in terms of the above factors for immersive VR interaction.

scholarly journals Inertial Sensor Based Solution for Finger Motion Tracking

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Stepan Lemak ◽  
Viktor Chertopolokhov ◽  
Ivan Uvarov ◽  
Anna Kruchinina ◽  
Margarita Belousova ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Inertial Sensor ◽  
Complete Information ◽  
Recognition Algorithm ◽  
Inertial Motion ◽  
Hand Motion ◽  
Virtual Reconstruction ◽  
Finger Tracking ◽  
Finger Motion ◽  
Finger Model

Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as a comparison. The proposed algorithms utilize the three-link finger model and provide complete information about the position and orientation of the metacarpus. The numerical experiment shows that this approach is feasible and overcomes some of the major limitations of inertial motion tracking. The paper’s proposed solution was created in order to track a user’s pointing and grasping movements during the interaction with the virtual reconstruction of the cultural heritage of historical cities.

Sign in / Sign up

Export Citation Format

Share Document