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.

scholarly journals A Tangible Solution for Hand Motion Tracking in Clinical Applications

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 208 ◽  
Author(s):  
Christina Salchow-Hömmen ◽  
Leonie Callies ◽  
Daniel Laidig ◽  
Markus Valtin ◽  
Thomas Schauer ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Motion Tracking ◽  
Inertial Sensor ◽  
Tracking System ◽  
Motor Impairment ◽  
Clinical Applications ◽  
Mean Square ◽  
Hand Motion ◽  
Fusion Methods ◽  
Mean Square Errors

Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.

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.

Assessing Learning Effort with Hand Motion Tracking Methods

2020 ◽  
Author(s):  
Hansol Rheem ◽  
David V. Becker ◽  
Scotty D. Craig
Keyword(s):  
Motion Tracking ◽  
Hand Motion

ARM-based Behavior Tracking and Identification System for Grouphoused Pigs

2019 ◽  
Vol 12 (6) ◽  
pp. 554-565
Author(s):  
Xingqiao Liu ◽  
Jun Xuan ◽  
Fida Hussain ◽  
Chen Chong ◽  
Pengyu Li
Keyword(s):  
Monitoring System ◽  
Complete Information ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
Identification System ◽  
Monitoring Method ◽  
Embedded Platform ◽  
Food And Water Intake ◽  
Behavior Tracking

Background: A smart monitoring system is essential to improve the quality of pig farming. A real-time monitoring system provides growth, health and food information of pigs while the manual monitoring method is inefficient and produces stress on pigs, and the direct contact between human and pig body increases diseases. Methods: In this paper, an ARM-based embedded platform and image recognition algorithms are proposed to monitor the abnormality of pigs. The proposed approach provides complete information on in-house pigs throughout the day such as eating, drinking, and excretion behaviors. The system records in detail each pig's time to eat and drink, and the amount of food and water intake. Results: The experimental results show that the accuracy of the proposed method is about 85%, and the effect of the technique has a significant advantage over traditional behavior detection methods. Conclusion: Therefore, the ARM-based behavior recognition algorithm has certain reference significance for the fine group aquaculture industry. The proposed approach can be used for a central monitoring system.

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