{"id":1855,"date":"2026-06-05T21:01:17","date_gmt":"2026-06-05T21:01:17","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1855"},"modified":"2026-06-05T21:01:17","modified_gmt":"2026-06-05T21:01:17","slug":"vol24-no1-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art1\/","title":{"rendered":"Energy Efficiency in Human Activity Recognition on Smartphones with Adaptive Sampling and Deep Learning Models with Early Exits"},"content":{"rendered":"<p>Marcel Franco de Oliveira <a href=\"https:\/\/orcid.org\/0009-0006-1656-0135\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Alessandro Copetti<a href=\"https:\/\/orcid.org\/0000-0002-0559-9038\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Roberto Pacheco <a href=\"https:\/\/orcid.org\/0000-0002-6763-7255\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Lauro Alexandre G. Sampaio <a href=\"https:\/\/orcid.org\/0009-0007-1557-5922\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>&amp; Luciano Bertini <a href=\"https:\/\/orcid.org\/0000-0001-7360-6685\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Human Activity Recognition (HAR) plays a crucial role in applications such as health monitoring, physical training, and therapeutic support. Smartphones equipped with embedded sensors provide a convenient platform for these applications. However, running deep learning models continuously can drain device energy autonomy. Despite recent advances, few studies have explored how executing deep learning-based HAR models across different computational environments affects energy consumption, especially when combined with strategies that leverage application-level contextual information. This work develops and evaluates optimized deep learning models, including variants with early exits, in both local and remote execution scenarios. It also incorporates an adaptive sampling strategy that dynamically adjusts sensor reading frequency according to movement intensity (contextual information). Results show that the proposed approach reduces energy consumption and inference time while preserving model accuracy.<\/p>\n<p><strong>Keywords:<\/strong>  Deep learning; early exit; human activity recognition; adaptive sampling; energy efficiency; mobile devices.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol24-no1-art1\">10.21528\/lnlm-vol24-no1-art1<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2026\/12\/vol24-no1-art1.pdf\">vol24-no1-art1.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2026\/12\/vol24-no1-art1.bib\">vol24-no1-art1.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Marcel Franco de Oliveira , Alessandro Copetti, Roberto Pacheco , Lauro Alexandre G. Sampaio &amp; Luciano Bertini Abstract: Human Activity Recognition (HAR) plays a crucial role in applications such as health monitoring, physical training, and therapeutic support. Smartphones equipped with <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art1\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1851,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1855","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Energy Efficiency in Human Activity Recognition on Smartphones with Adaptive Sampling and Deep Learning Models with Early Exits - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Energy Efficiency in Human Activity Recognition on Smartphones with Adaptive Sampling and Deep Learning Models with Early Exits - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Marcel Franco de Oliveira , Alessandro Copetti, Roberto Pacheco , Lauro Alexandre G. 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