Browsing by Author "Islam, Lamiya"

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    A comprehensive safety and support platform for domestic abuse victims
    (BRAC University, 2025-06) Mahin, Rifah Tasnim; Ainun, Atika Hossain; Islam, Lamiya; Chowdhury, Farida; Abedin, Jawaril Munshad
    Domestic violence remains a critical issue, especially in surveillance-heavy environments like Bangladesh where abusers often monitor their victims’ mobile activity. This research presents the iterative design and conceptual development of a discreet safety and support application for domestic abuse victims. Unlike traditional solutions, this mobile application is disguised as a benign utility app (e.g., a grocery list), ensuring discretion even under close monitoring. The app architecture follows a layered Four P’s Model: Preparation, Protection, Provision, and Prevention, aligning each feature with user safety goals. While designing, a user-centered approach was adopted, involving expert interviews, focus group discussions, feature assessment surveys, and victim testing across three design phases: hand-drawn paper prototypes, low-fidelity digital versions, and a fully navigable high-fidelity prototype. Each phase incorporated active feedback from survivors and professionals to ensure clarity, minimal cognitive load, and cultural relevance. Key functionalities include dummy interface switching, real/dummy login system, encrypted evidence logging, a Quick Exit button, and Bangla localization. Additionally, the app proposes two machine learning extensions: a voice-based distress and trigger word detection model using emotion recognition, and a conceptual risk prediction framework based on user-logged incidents. While not implemented due to time and development limitations, the models were architected using open-source datasets and preprocessing pipelines, ensuring future feasibility. By embedding iterative victim feedback and Human-Computer Interaction (HCI) principles throughout, this study demonstrates a survivor-informed, context-sensitive approach to mobile safety design. The final prototype serves as both a practical intervention model and a contribution to ongoing research in HCI, trauma-aware design, and machine learning for social good.