Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2212.02918 (cs)
[Submitted on 6 Dec 2022]

Title:Thermal Dissipation Resulting from Everyday Interactions as a Sensing Modality -- The MIDAS Touch

Authors:Farooq Dar, Hilary Emenike, Zhigang Yin, Mohan Liyanage, Rajesh Sharma, Agustin Zuniga, Mohammad A. Hoque, Marko Radeta, Petteri Nurmi, Huber Flores
View PDF
Abstract:We contribute MIDAS as a novel sensing solution for characterizing everyday objects using thermal dissipation. MIDAS takes advantage of the fact that anytime a person touches an object it results in heat transfer. By capturing and modeling the dissipation of the transferred heat, e.g., through the decrease in the captured thermal radiation, MIDAS can characterize the object and determine its material. We validate MIDAS through extensive empirical benchmarks and demonstrate that MIDAS offers an innovative sensing modality that can recognize a wide range of materials with up to 83% accuracy and generalize to variations in the people interacting with objects. We also demonstrate that MIDAS can detect thermal dissipation through objects, up to 2 mm thickness, and support analysis of multiple objects that are interacted with
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)
Cite as: arXiv:2212.02918 [cs.DC]
  (or arXiv:2212.02918v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2212.02918
Journal reference: Pervasive and Mobile Computing, Volume 84, 2022
Related DOI: https://doi.org/10.1016/j.pmcj.2022.101625

Submission history

From: Mohan Liyanage [view email]
[v1] Tue, 6 Dec 2022 12:18:24 UTC (6,430 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.DC
< prev   |   next >
new | recent | 2022-12
Change to browse by:
cs
cs.HC
eess
eess.SP

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)