DiscoBand: Multiview Depth-Sensing Smartwatch Strap for Hand, Body and Environment Tracking

Devrio, Nathan; Harrison, Chris

doi:10.1145/3526113.3545634

Cited by 15 publications

(2 citation statements)

References 56 publications

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“…Much research has focused on capturing specific body parts. For instance, hand pose is of great importance in VR/AR applications, and has been tracked using e.g., wrist-worn cameras [10,29,62], electrical impedance tomography [67], electromyography [30], depth sensors [16], magnetic trackers [13], and specialized gloves [17]. Faces are important too, most often captured using cameras [4,57], but other methods such as ultrasound [23] and electromyography [18] have also been explored.…”

Section: Body Capture With Non-imu Worn Sensorsmentioning

confidence: 99%

IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds

Mollyn

Arakawa

Goel

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Figure 1: Using whatever mobile devices a user has with them, IMUPoser estimates full-body pose. In the best case, a user can have a smartphone, smartwatch and earbuds (pose from 3 devices). Of course, the number of devices will vary over time, e.g., earbud use is intermittent and not everyone wears a smartwatch. This means IMUPoser must track what devices are present, where they are located, and use whatever IMU data is available.

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Section: Body Capture With Non-imu Worn Sensorsmentioning

confidence: 99%

IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds

Mollyn

Arakawa

Goel

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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“…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. Furthermore, the fusion of electromyography and depth sensors allows for the accurate tracking of arm and hand poses, thereby supporting supplementary applications such as held object recognition and environment mapping [ 39 ]. Nevertheless, accurately estimating hand poses in the presence of significant self-occlusion remains a highly complex challenge.…”

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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