MomsVoice: Postpartum Depression Assistance System

This paper presents an AI-powered postpartum depression assistance system designed to support mothers during the postnatal period through continuous emotional monitoring and intelligent care. The system uses a voice-based conversational assistant to interact naturally with users, analyze emotional conditions, and detect signs of postpartum depression through advanced AI models. It integrates a Flutter mobile application with a Django backend, enabling secure communication, automated alerts to doctors and family members, smart reminder notifications, and access to educational mental health resources. A 14-category emotion classification framework helps identify distress levels ranging from mild stress to critical suicidal thoughts. The proposed solution offers an accessible, scalable, and proactive approach to improving maternal mental health and ensuring timely intervention.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-25
DOI
https://doi.org/10.5281/zenodo.19757113
Primary Topic
Maternal Mental Health During Pregnancy and Postpartum
Type
article
Field-Weighted Citation Impact
0.00
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article

MomsVoice: Postpartum Depression Assistance System

JOMOL THOMAS
Zenodo (CERN European Organization for Nuclear Research)
Maternal Mental Health During Pregnancy and Postpartum
article

MomsVoice: Postpartum Depression Assistance System

JOMOL THOMAS
article en

Abstract

This paper presents an AI-powered postpartum depression assistance system designed to support mothers during the postnatal period through continuous emotional monitoring and intelligent care. The system uses a voice-based conversational assistant to interact naturally with users, analyze emotional conditions, and detect signs of postpartum depression through advanced AI models. It integrates a Flutter mobile application with a Django backend, enabling secure communication, automated alerts to doctors and family members, smart reminder notifications, and access to educational mental health resources. A 14-category emotion classification framework helps identify distress levels ranging from mild stress to critical suicidal thoughts. The proposed solution offers an accessible, scalable, and proactive approach to improving maternal mental health and ensuring timely intervention.

Zenodo (CERN European Organization for Nuclear Research)
École Nationale d'Ingénieurs de Gabès (TN)
Openalex Percentile: Top 15%
Maternal Mental Health During Pregnancy and Postpartum
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