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This paper presents the methodology and outcomes of a comprehensive dataset collection using ESP32-Nodemcu devices and the ESP32-CSI Toolkit. The dataset, designed to explore the capabilities of Channel State Information (CSI) in distinguishing human activities, was collected in a controlled indoor environment under three scenarios: single-user, two-user, and three-user setups.
This report presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets in the Indian context by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style. The IMUs are utilized to capture the dynamic movement patterns associated with handwriting, enabling more accurate recognition of alphabets. The Indian context introduces various challenges due to the heterogeneity in writing styles across different regions and languages.