{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:53:43Z","timestamp":1781110423517,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T00:00:00Z","timestamp":1707004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Processing with Intelligent Storage and Memory (PRISM)","award":["#2023-JU-3135"],"award-info":[{"award-number":["#2023-JU-3135"]}]},{"name":"Center for Processing with Intelligent Storage and Memory (PRISM)","award":["#2003277"],"award-info":[{"award-number":["#2003277"]}]},{"name":"Center for Processing with Intelligent Storage and Memory (PRISM)","award":["#1911095"],"award-info":[{"award-number":["#1911095"]}]},{"name":"NSF","award":["#2023-JU-3135"],"award-info":[{"award-number":["#2023-JU-3135"]}]},{"name":"NSF","award":["#2003277"],"award-info":[{"award-number":["#2003277"]}]},{"name":"NSF","award":["#1911095"],"award-info":[{"award-number":["#1911095"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs.<\/jats:p>","DOI":"10.3390\/s24031014","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T03:28:29Z","timestamp":1707103709000},"page":"1014","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1894-1565","authenticated-orcid":false,"given":"Fatemeh","family":"Asgarinejad","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA"},{"name":"Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofan","family":"Yu","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danlin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Morris","sequence":"additional","affiliation":[{"name":"Computer Science and Information Systems, California State University San Marcos, San Marcos, CA 92096, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tajana","family":"Rosing","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-9061","authenticated-orcid":false,"given":"Baris","family":"Aksanli","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.jclepro.2016.10.006","article-title":"A review of Internet of Things for smart home: Challenges and solutions","volume":"140","author":"Stojkoska","year":"2017","journal-title":"J. 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