Hand-gesture-based human-machine interface system using Compressive Sensing

Mantecón, Tomás; Mantecon, Ana; del-Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

doi:10.1109/isce.2015.7177828

Cited by 3 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…Computational sensing has been an active eld of research for over a decade now and is rich in foundational works [32] that paved the ways for applications like autonomous navigation [120], contactless perception and monitoring [80], human-machine interaction [84], and precision measurements [5]. Recent works explore acoustic sensing in the context of ubiquitous computing [21, 93, 106-110, 119, 137].…”

Section: Related Workmentioning

confidence: 99%

SPiDR

Bai

Garg

Roy

2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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This paper presents the design and implementation of SPiDR, an ultra-low-power spatial sensing system for miniature mobile robots. This acoustic sensor produces a cross-sectional map of the eld-of-view using only one speaker/microphone pair. While it is challenging to have enough spatial diversity of signal with a single omnidirectional source, we leverage sound's interaction with small structures to create a 3D-printed passive lter, called a stencil, that can project spatially coded signals on a region at a ne granularity. The system receives a linear combination of the reections from nearby objects and applies a novel power-aware depth-map reconstruction algorithm. The algorithm rst estimates the approximate locations of the objects in the scene and then iteratively applies fractional multi-resolution inversion. SPiDR consumes only 10<, of power to generate a depth-map in real-world scenario with over 80% structural similarity score with the scene. CCS CONCEPTS• Computer systems organization ! Embedded and cyberphysical systems; Sensors and actuators.

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Section: Related Workmentioning

confidence: 99%

SPiDR

Bai

Garg

Roy

2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

View full text Add to dashboard Cite

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“…A novel and robust vision-based human-machine interface system to naturally interact with computers/smart devices was proposed in [5]. The key contribution is the introduction of a Compressive Sensing technique to largely reduce the dimensionality of highly discriminative feature descriptors, which originally have an excessive and inoperative high dimension to be applied to a Support Vector Machine based classier.…”

Section: Main Contributions Of the Thesismentioning

confidence: 99%

“…5 shows the results of the comparison, using F-score values, with other stateof-the-art descriptors: the LBP-based solution[57] and SIFT-based solution[52]. From that table it can be drawn that the BAG-D3P is the solution which obtains best results, a 94.57% in the F-score mean value.…”

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confidence: 97%