Lamiyah Khattar

Papers

Total Citations

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About

Lamiyah Khattar is a rising researcher at the intersection of deep learning and ubiquitous computing, with a primary focus on Human Activity Recognition (HAR). Her most-cited work, "Analysis of Human Activity Recognition using Deep Learning" (2021), has garnered 15 citations and provides a critical comparative evaluation of various deep learning architectures for interpreting sensor data. This foundational study directly addresses the challenges posed by the explosive growth of data from IoT and robotics, offering a clear benchmark for accuracy across different models. By systematically analyzing how neural networks can best classify human movements from raw sensor streams, Khattar’s research lays essential groundwork for smarter, context-aware systems. Her contributions are particularly relevant for advancing applications in healthcare monitoring, smart environments, and autonomous robotics, where reliable activity recognition is key. As a scholar focused on bridging algorithmic performance with real-world deployment, Khattar’s work continues to inform the next generation of intelligent, responsive technologies.