{"id":240,"date":"2026-01-09T16:00:46","date_gmt":"2026-01-09T21:00:46","guid":{"rendered":"https:\/\/carleton.ca\/richarddansereau\/?p=240"},"modified":"2026-01-10T21:19:37","modified_gmt":"2026-01-11T02:19:37","slug":"heba-nematallah-defends-her-phd-thesis","status":"publish","type":"post","link":"https:\/\/carleton.ca\/richarddansereau\/2026\/heba-nematallah-defends-her-phd-thesis\/","title":{"rendered":"Heba Nematallah defends her PhD thesis"},"content":{"rendered":"\n\n\n
\n

\"End-to-End<\/h3>\n<\/td>\n

\n

Heba successfully defended her PhD thesis:<\/h3>\n
\n

From Conv-FOS-MMW to AHDT: Novel Frameworks for Feature Extraction and Hierarchical Classification in Human Activity Recognition.<\/b><\/h4>\n<\/blockquote>\n

This thesis\u00a0introduces a new adaptive, hierarchical classification framework that significantly improves the accuracy, scalability, and interpretability of IMU\u2011based human activity recognition. By combining adaptive segmentation, multi\u2011mother wavelet feature extraction, and a global decision\u2011tree architecture, the approach handles complex real\u2011world movements with greater efficiency and lower computational cost. The work demonstrates strong performance across multiple datasets, paving the way for more reliable HAR systems in healthcare, aging support, and activity monitoring.<\/p>\n

Congratulations !<\/p>\n

And a special thanks goes to Prof. Sreeraman Rajan who supervised this research for many years.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Heba successfully defended her PhD thesis: From Conv-FOS-MMW to AHDT: Novel Frameworks for Feature Extraction and Hierarchical Classification in Human Activity Recognition. This thesis\u00a0introduces a new adaptive, hierarchical classification framework that significantly improves the accuracy, scalability, and interpretability of IMU\u2011based human activity recognition. By combining adaptive segmentation, multi\u2011mother wavelet feature extraction, and a global decision\u2011tree […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[22,1],"tags":[],"yoast_head":"\nHeba Nematallah defends her PhD thesis - Prof. Richard M. 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