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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Intelligent Activity Suggesting System Based on User Emotional State Using Multimodal Data |
| Authors Name: | B Jyothi , Vasugani Prasanth , Pikki Koteswari , Alumuri Adamu |
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IJRTI_211586
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| Published Paper Id: | IJRTI2604186 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | Abstract—Abstract—Human emotional states have a direct influence on decision-making, produc- tivity, and mental well-being, though most existing computing systems remain entirely unresponsive to their users’ affective conditions. Our research presents EmotionAI, an intelligent activity suggestion system that detects the real-time emotional state of a user through multimodal data and recommends contextually appropriate activities to support emotional well-being. The proposed system combines three complementary input streams—facial image analysis, voice signal processing, and demographic classification—to produce a robust emotional profile that overcomes the accuracy limitations inherent in unimodal approaches. Deep learning models including Convolutional Neural Networks (CNN), transfer- learning variants (VGG16, ResNet50), and Long Short-Term Memory (LSTM) networks are used for facial and acoustic (voice) feature extraction respectively, while a multimodal combination layer com- bine their outputs into a final predicted emotional state. An activity recommendation engine, operat- ing through a combination of hardcoded mapping and decision-tree inference, converts the detected emotion and demographic context into personalised activity suggestions. Experimental evaluation on the FER2013 benchmark dataset render that the multimodal configuration achieves 96.2out-compute all single-modality baselines. The system is deployed through a web-based interface supporting real-time camera input, voice capture, and media upload, making it accessible without specialised hardware. Emo- tionAI is suited to mental health monitoring, educational environments, workplace wellness, and smart home applications, and establishes a reproducible architecture for future expansion into severity assess- ment and longitudinal emotional tracking. Index Terms—emotion recognition, multimodal fusion, facial expression analysis, voice emotion, activity recommendation, deep learning, human–computer interac- tion, mental well-being, CNN, LSTM |
| Keywords: | emotion recognition, multimodal fusion, facial expression analysis, voice emotion, ac- tivity recommendation, deep learning, human-computer interaction, mental well-being, CNN, LSTM. |
| Cite Article: | "Intelligent Activity Suggesting System Based on User Emotional State Using Multimodal Data", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b355-b363, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604186.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604186
Registration ID:211586
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b355-b363 Country: Narasaraopet, Palnadu, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604186 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604186 |
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