A sensor fusion approach to MARG module orientation estimation for a real-time hand tracking application

Ratchatanantakit, Neeranut; O-larnnithipong, Nonnarit; Sonchan, Pontakorn; Adjouadi, Malek; Barreto, Armando

doi:10.1016/j.inffus.2022.09.017

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…To overcome the above-mentioned limitations, sensor fusion approaches have been proposed to integrate multiple sensors and enhance the effectiveness of hand tracking methodologies. For instance, the fusion of Magnetic/Angular-Rate/Gravity (MARG) sensors and IMUs offers the potential to compensate for individual sensor limitations and achieve precise hand tracking across a broad range of motions [ 38 ]. However, challenges arise when the sensor module experiences significant accelerations or rotations, which can adversely affect the performance of the tracking algorithm and result in suboptimal accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

et al. 2023

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The rapidly evolving field of Virtual Reality (VR)-based Human–Computer Interaction (HCI) presents a significant demand for robust and accurate hand tracking solutions. Current technologies, predominantly based on single-sensing modalities, fall short in providing comprehensive information capture due to susceptibility to occlusions and environmental factors. In this paper, we introduce a novel sensor fusion approach combined with a Long Short-Term Memory (LSTM)-based algorithm for enhanced hand tracking in VR-based HCI. Our system employs six Leap Motion controllers, two RealSense depth cameras, and two Myo armbands to yield a multi-modal data capture. This rich data set is then processed using LSTM, ensuring the accurate real-time tracking of complex hand movements. The proposed system provides a powerful tool for intuitive and immersive interactions in VR environments.

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Section: Related Workmentioning

confidence: 99%

A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

et al. 2023

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“…In principle, MARG module orientation estimation is possible based on gyroscope data, particularly when periodically corrected by information from the accelerometer and the magnetometer. However, a critical challenge that emerges is the risk of applying a correction when the assumptions made for the utilization of secondary sensor (accelerometer or magnetometer) data are not met [12]. If such inappropriate corrections are fully applied, significant error may be introduced in the current and future orientation estimates.…”

Section: Introduction 1need For a Mems Marg Benchmarking Datasetmentioning

confidence: 99%

“…The environmental context and movements carried out by the MARG module should be similar to those a MARG module may experience in its application in body movement (e.g., hand and fingers movement) tracking for the purpose of human-computer interaction (as that is our area of work [12,34,35]).…”

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confidence: 99%

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion

Sonchan

Ratchatanantakit

O-larnnithipong

et al. 2023

Sensors

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In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic–angular rate–gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference (“ground truth”) MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human–computer interaction, kinesiology, motor rehabilitation, etc.

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A Self-contained Approach to MEMS MARG Orientation Estimation for Hand Gesture Tracking in Magnetically Distorted Environments

Sonchan

Ratchatanantakit

O-larnnithipong

et al. 2023

Lecture Notes in Computer Science

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A sensor fusion approach to MARG module orientation estimation for a real-time hand tracking application

Cited by 6 publications

References 14 publications

A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

A Novel Sensor Fusion Approach for Precise Hand Tracking in Virtual Reality-Based Human—Computer Interaction

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion

A Self-contained Approach to MEMS MARG Orientation Estimation for Hand Gesture Tracking in Magnetically Distorted Environments

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