A human orientation tracking system using Template Matching and active Infrared marker

Tsun, Mark Tee Kit; Jo, Hudyjaya Siswoyo; Lau, Sian Lun

doi:10.1109/icssa.2015.7322522

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…Among the important requirements for hand detection, and ultimately for recognition systems, are the sensors which track human movement. Nevertheless, one has to consider that the depth sensors' current movement tracking have a constrained zone of tracking and recognition, depending on natural lighting, lens occlusion (a special lens that removes visible light below 820 nm from being read), and hardware factors [14].…”

Section: Review Of Literaturementioning

confidence: 99%

A comparison of Arabic sign language dynamic gesture recognition models

Almasre

Al-Nuaim

2020

Heliyon

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Arabic Sign Language (ArSL) is similar to other sign languages in terms of the way it is gestured and interpreted and used as a medium of communication among the hearing-impaired and the communities in which they live in. Research investigating sensor utilization and natural user interfaces to facilitate ArSL recognition and interpretation, is lacking.Previous research has demonstrated that there is not a single classifier modeling approach that can be suitable for all hand gesture recognition tasks, therefore, this research investigated which combination of algorithms, set with different parameters used with a sensor device, produce higher ArSL recognition accuracy results in a gesture recognition system.This research proposed a dynamic prototype model (DPM) using Kinect as a sensor to recognize certain ArSL gestured dynamic words. The DPM used eleven predictive models of three algorithms (SVM, RF, KNN) based on different parameter settings. Research findings indicated that highest recognition accuracy rates for the dynamic words gestured were achieved by the SVM models, with linear kernel and cost parameter ¼ 0.035.

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Section: Review Of Literaturementioning

confidence: 99%

A comparison of Arabic sign language dynamic gesture recognition models

Almasre

Al-Nuaim

2020

Heliyon

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“…Besides the aquatic environment, human movement tracking itself has been extensively investigated in the literature. Gold standard approach includes the use of motion capture systems with standardized [17] or ad-hoc [18] optical markers placement, using either passive [19] or active [20] markers. However, optical motion capture systems and relative human motion tracking algorithms cannot be used in an aquatic context, for marker visibility issues, and due to optical distortion induced by water.…”

Section: Introductionmentioning

confidence: 99%

Wearable Biofeedback Suit to Promote and Monitor Aquatic Exercises: A Feasibility Study

Gandolla

Pedrocchi

Ferrante

et al. 2020

IEEE Trans. Instrum. Meas.

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Aquatic exercises have been demonstrated to benefit the facilitation of motor recovery and the enhancement of well-being in middle-aged adults and the elderly. Personalization, immersiveness, and biofeedback are key for amplifying and accelerating any rehabilitation process in neurological and orthopedic patients. However, a therapist can neither properly visualize nor monitor rehabilitation exercises executed under water, nor can he/she measure them. Therefore, the present study aims to provide adaptive biofeedback during aquatic exercises in order to enhance the training's effectiveness. A wearable biofeedback suit equipped with wearable underwater-resistant sensor nodes has been designed, produced, and tested. A dedicated algorithm for quantitatively extracting joint angles has been developed and validated against the optical tracking system. Multiple biofeedback modalities are proposed based on visual feedback: amplitude control with set target angles; velocityamplitude control with set target angles and angular velocity; and velocity tutor with set target angles, a frequency value, and a rest period. Joint angles estimated using the sensor network are compared to those estimated using an optical tracking system with the root-mean-squared angle error between the two systems ranging from 4.0° to 6.3° and a significant correlation coefficient that is always greater than 0.99. Pilot tests during aquatic exercises executed in a thermal environment demonstrate the feasibility and usability of the complete system in the final working environment. The relevant angles are correctly calculated and monitored online during the exercises, and the tested subjects understand the implemented biofeedback modalities easily and follow them well as the SUS evaluation indicates. 0018-9456 (c)

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“…This system consists of a camera with a modified high pass filter and a vest arrayed with Infrared (IR) Light Emitting Diodes (LEDs) (Figure 1) [12]. The idea is to capture the pattern of LEDs and use template matching to approximate the relative angle the subject (wearing the vest with active LEDs) is facing.…”

Section: Subject Locking and The Active Ir Marker Tracking System (Aimentioning

confidence: 99%

“…The autonomous indoor navigation problem is usually addressed using a combination of proximity sensors, depth cameras and machine vision, to keep track of both target users and The Kinect optimal tracking zone problem is initially treated with an assistive technology solution that integrates wearable electronics and vision-based marker tracking. This system consists of a camera with a modified high pass filter and a vest arrayed with Infrared (IR) Light Emitting Diodes (LEDs) (Figure 1) [12]. The idea is to capture the pattern of LEDs and use template matching to approximate the relative angle the subject (wearing the vest with active LEDs) is facing.…”

Section: Introductionmentioning

confidence: 99%

An Improved Indoor Robot Human-Following Navigation Model Using Depth Camera, Active IR Marker and Proximity Sensors Fusion

Tsun

2018

Robotics

Self Cite

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Creating a navigation system for autonomous companion robots has always been a difficult process, which must contend with a dynamically changing environment, which is populated by a myriad of obstructions and an unspecific number of people, other than the intended person, to follow. This study documents the implementation of an indoor autonomous robot navigation model, based on multi-sensor fusion, using Microsoft Robotics Developer Studio 4 (MRDS). The model relies on a depth camera, a limited array of proximity sensors and an active IR marker tracking system. This allows the robot to lock onto the correct target for human-following, while approximating the best starting direction to begin maneuvering around obstacles for minimum required motion. The system is implemented according to a navigation algorithm that transforms the data from all three types of sensors into tendency arrays and fuses them to determine whether to take a leftward or rightward route around an encountered obstacle. The decision process considers visible short, medium and long-range obstructions and the current position of the target person. The system is implemented using MRDS and its functional test performance is presented over a series of Virtual Simulation Environment scenarios, greenlighting further extensive benchmark simulations.

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A human orientation tracking system using Template Matching and active Infrared marker

Cited by 13 publications

References 13 publications

A comparison of Arabic sign language dynamic gesture recognition models

A comparison of Arabic sign language dynamic gesture recognition models

Wearable Biofeedback Suit to Promote and Monitor Aquatic Exercises: A Feasibility Study

An Improved Indoor Robot Human-Following Navigation Model Using Depth Camera, Active IR Marker and Proximity Sensors Fusion

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