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

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

show abstract

“…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.…”

mentioning

confidence: 97%