Authors : Yamuna U; Suchith R; Sumanth M; Pon Muthulakshimi; Tarun S

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/46b3ukdu

Scribd : https://tinyurl.com/3bhj47cf

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY934

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Alzheimer's disease presents a global challenge, affecting the ability of individuals to communicate and trust their caregivers. Effective caregiver interactions are vital for emotional well-being in Alzheimer's patients. However, the progressive nature of the disease often leads to communication barriers and a lack of trust, exacerbated by the absence of objective trust assessment mechanisms. Additionally, patients are unable to recall past conversations. To address these challenges, EmoConnect proposes an innovative ML- based solution. EmoConnect solution involves the development of an ML based application that employs brain wave detection and analysis to objectively assess caregiver trustworthiness during interactions with Alzheimer's patients. It calculates a trustworthiness score, enhancing the quality of caregiving and promoting emotional well- being for both caregivers and patients. Furthermore, it stores the text of conversations alongside trust scores, enabling patients to access and review past interactions.

Keywords : Alzheimer's Disease, Caregiver Interactions, Trust, EmoConnect, ML (Machine Learning), Brain Wave Detection, Technology, Innovative Solution, Conversation Assessment.

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