Magnetic Hand Tracking for Human-Computer Interface

Ma, Yu; Mao, Zhi-Hong; Jia, Wenyan; Li, Chengliu; Yang, Jiawei; Sun, Mingui

doi:10.1109/tmag.2010.2076401

Cited by 55 publications

(26 citation statements)

References 10 publications

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“…MCR has a large field of applications in place of static cameras for motion capture, virtual reality immersion, and sports performance analysis. A subset of this field is the use of MCR for human-computer interfacing [33]. Finally, the MCR method could be applied toward the shape recovery of un-restrained wild animals to study their physiology and kinesiology, without capturing the animals or deploying humans to unfriendly environments such as oceans.…”

Section: Motivationmentioning

confidence: 99%

Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects

Nuger

Benhabib

2018

J Intell Robot Syst

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This thesis proposes a multi-camera active-vision reconfiguration system which selects camera poses online to improve the shape recovery of a priori unknown, markerless, deforming objects in dynamic environments. The objectives of shape recovery are defined as surface sampling accuracy, and shape completeness. The completeness objective is generalized for both solid and surface-based objects as the maximization of surface visibility. Thus, improving the recovered shape of target objects is shown to be analogous to maximizing the surface area visibility. This improvement is achieved through the on-line reconfiguration of multiple cameras. The system developed herein is composed of a shape recovery method, a robust tracking algorithm, and a multi-camera reconfiguration method.The shape recovery method is based on a modular fusion technique that produces a complete 3D mesh-model of the target object. The method fuses triangulation data with a visual hull to maximize recovery accuracy. The modularity of the method lies in the ability to modify data filtering techniques to improve modelling accuracy, and interchange stereo correspondence features. The adaptive particle filtering algorithm produces a deformation estimation of the recovered model from the tracking data. The algorithm automatically adapts to the quantity of tracking data available and changes in the object's dynamics. The modularity of the algorithm allows modifications in terms of the number of particles and motion models for task-specific implementations. The reconfiguration method consists of a robust stereovisibility objective function, workspace discretization, and a path planner. The complete system can iii improve the shape recovery of a priori unknown deforming objects in obstacle-laden environments when compared to static camera methods.To validate the proposed system, extensive simulations and experiments were conducted. The simulations tested the system in comparison to static multi-camera systems, and ideal camera placement where the object model was a priori known and the camera's dynamics were unconstrained. Simulation results showed the proposed methodology outperformed static cameras and approached the performance of ideal camera placement in a dynamic, obstacle-laden environment. The experimental results showed similar improvement in shape recovery when comparison to a static camera system in an obstacle-laden environment.iv

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Section: Motivationmentioning

confidence: 99%

Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects

Nuger

Benhabib

2018

J Intell Robot Syst

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“…Gesture recognition technology involves interpreting a user's gestures on the basis of the flexion and extension of fingers, elbows, and knees, and their interaction with objects. Gesture recognition has several applications, including human-computer interface (HCI), (1)(2)(3)(4)(5)(6)(7) medicine, (8)(9)(10)(11) and virtual reality. (12) Various sensors such as magnetometers, accelerometers, and gyroscopes are used for gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Virtual Keyboard Recognition with e-Textile Sensors

Ahn¹,

Han²,

Ryu³

et al. 2020

Sensors and Materials

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In this study, we propose a gesture recognition method using e-textile sensors and involving the pressing of numeric keys from "0" to "9". An e-textile sensor comprises a double-layer structure with complementary resistance characteristics, and it is attached to the garment to obtain a resistance signal. For gesture recognition, we tested dynamic time warping (DTW), machine learning, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). Before applying each machine learning technique, we performed normalization and resized the data to ensure that they are of the same length. A total of 120 iterations were performed for each gesture for a single subject. The results indicate that the lowest gesture classification accuracy for DTW was 74.2%, followed by 78.8 and 91.6% for LSTM and BiLSTM, respectively.

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“…It is considered to be fundamental to many other vision problems and applications such as surveillance, 1,2 human-computer interfaces, 3,4 robotics, 5,6 and so on. Visual tracking is one of the most important components in computer vision and it has gained significant attention from the research community over the past decade.…”

Section: Introductionmentioning

confidence: 99%

Visual tracking method based on cuckoo search algorithm

et al. 2015

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Cuckoo search (CS) is a new meta-heuristic optimization algorithm that is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. It has been found to be efficient in solving global optimization problems. An application of CS is presented to solve the visual tracking problem. The relationship between optimization and visual tracking is comparatively studied and the parameters' sensitivity and adjustment of CS in the tracking system are experimentally studied. To demonstrate the tracking ability of a CS-based tracker, a comparative study of tracking accuracy and speed of the CS-based tracker with six "state-of-art" trackers, namely, particle filter, meanshift, PSO, ensemble tracker, fragments tracker, and compressive tracker are presented. Comparative results show that the CS-based tracker outperforms the other trackers.

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Magnetic Hand Tracking for Human-Computer Interface

Cited by 55 publications

References 10 publications

Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects

Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects

Virtual Keyboard Recognition with e-Textile Sensors

Visual tracking method based on cuckoo search algorithm

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