VISUALIZING STRESS AND MINDFULNESS: INTEGRATING INTELLIGENT SYSTEMS FOR EXPERIENTIAL WELL-BEING PRACTICES

Silky Arora; Shradha Vaishnav; Aparna Sharma; Touseef Ahmed Lone; Shiney  Chib; Suhas Bhise

doi:10.29121/shodhkosh.v6.i5s.2025.6961

Authors

Silky Arora Department of Computer Science and Engineering, CT University, Ludhiana, Punjab, India
Dr. Shradha Vaishnav Assistant Professor, School of Wellness, AAFT University of Media and Arts, Raipur, Chhattisgarh-492001, India
Aparna Sharma Professor, School of Liberal Arts, Noida International University, Noida, Uttar Pradesh, India
Touseef Ahmed Lone Department of Computer Science and Engineering, CT University, Ludhiana, Punjab, India
Dr. Shiney Chib Dean Academics & Research Head, Datta Meghe Institute of Management Studies, Nagpur, Maharashtra, India
Suhas Bhise Assistant Professor, Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra-411037, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6961

Keywords:

Stress Detection, Machine Learning, Biosensor Data, Real-Time Feedback, Physiological Signals, Deep Learning, Wearable Technology

Abstract [English]

Stress and mindfulness Visualized using intelligent systems are an emerging way of performing experiential practices of well-being that use both data gathered analytically and embodied awareness. This paper suggests an implementation of an integrative framework in which artificial intelligence, multimodal sensing, and interactive visualization are used to encode latent psychophysiological indicators of stress and provide an intuitive experiential response. The presented problem is that traditional stress measures are not as accessible or comprehensible and, in most cases, lack the ability to sustain mindfulness. It aims to develop a smart system that allows people to sense, consider, and manage stress on the spot with the help of graphics and fully interactive controls. The methodology uses wearable sensor data containing the heart rate variability, electrodermal activity, and respiration with machine learning models to infer the presence of stress and patterns. These deliverables are projected to adaptive displays and immersive interfaces that facilitate mindfulness activity, e.g., which includes breathing control, attention, and contemplation. The experimental appraisal proves that the subjects on the proposed system are more aware of the stress, have better self-regulations, and are more engaged than non-visual or stationary feedback technologies. The results show that there are measurable decreases in the levels of perceived stress, an increase of coherence between physiological stimuli, and an enhancement of adherence to mindfulness practices. The innovation of moving smart analytics to experiential design will build the proposed approach to contribute to human-centered well-being technologies. The study underscores how visual intelligence may be used to convert abstract stress data into valuable experience making possible the personalization and sustainability of mindfulness interventions in the healthcare, education and adult life context.

References

Adhau, T. P., and Gadicha, V. (2024). Design and Development of Prime Herder Optimization Based BiLSTM Congestion Predictor Model in Live Video Streaming. Intelligent Decision Technologies, 18(1), 237–255. https://doi.org/10.3233/IDT-230158 DOI: https://doi.org/10.3233/IDT-230158

Adhau, T. P., and Lokulwar, P. (2025). Incentivized BiLSTM with Triplet Attention for Predicting Congestion in Network Traffic. PMJ, 35(1s). https://doi.org/10.52783/pmj.v35.i1s.2099 DOI: https://doi.org/10.52783/pmj.v35.i1s.2099

Anders, C., and Arnrich, B. (2022). Wearable Electroencephalography and Multi-Modal Mental State Classification: A Systematic Literature Review. Computers in Biology and Medicine, 150. DOI: https://doi.org/10.1016/j.compbiomed.2022.106088

Arsalan, A., and Majid, M. (2021). Human Stress Classification During Public Speaking Using Physiological Signals. Computers in Biology and Medicine, 133. DOI: https://doi.org/10.1016/j.compbiomed.2021.104377

Chaudhari, A. U., and Shrivastava, H. (2024). Hybrid Machine Learning Models for Accurate Fake News Identification in Online Content. in 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI) (1–6). IEEE. DOI: https://doi.org/10.1109/IDICAIEI61867.2024.10842717

Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., et al. (2023). Emotion Recognition in EEG Signals Using Deep Learning Methods: A Review. Computers in Biology and Medicine, 165. DOI: https://doi.org/10.1016/j.compbiomed.2023.107450

Jin, C., Osotsi, A., and Oravecz, Z. (2023). Predicting Adolescent Female Stress with Wearable Device Data Using Machine and Deep Learning. In 2023 IEEE 19th International Conference on Body Sensor Networks (BSN) (1–4). IEEE. DOI: https://doi.org/10.1109/BSN58485.2023.10331414

Maji, S., Chaturmohta, A., Deevela, D., Sinha, S., Tarsolia, S., and Barsaiya, A. (2024). Mental Health Consequences of Academic Stress, Amotivation, and Coaching Experience: A Study of India’s Top Engineering Undergraduates. Psychology in the Schools, 61(9), 3540–3566. DOI: https://doi.org/10.1002/pits.23230

Motogna, V., Lupu-Florian, G., and Lupu, E. (2021). Strategy for Affective Computing based on HRV and EDA. In 2021 International Conference on e-Health and Bioengineering (EHB) (1–4). IEEE. DOI: https://doi.org/10.1109/EHB52898.2021.9657654

Pecori, R., Panella, G., Vurro, F., Bettelli, M., Fazzolari, M., and Ducange, P. (2024). An explainable smart agriculture system based on in‑vivo biosensors. in 2024 IEEE International Conference on Fuzzy Systems (FUZZ‑IEEE) (1–8). IEEE. DOI: https://doi.org/10.1109/FUZZ-IEEE60900.2024.10612040

Sagar, D. S., and Ninoria, S. (2022). Smart Healthcare Monitoring System for Early Diagnosis of Patient’s Disease Using Artificial Intelligence. in 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering (ICATIECE) (1–5). IEEE. DOI: https://doi.org/10.1109/ICATIECE56365.2022.10047205

Shahid, M. M., Agada, G. E., Kayyali, M., Ihianle, I. K., and Machado, P. (2023). Towards Enhanced Well-Being: Monitoring Stress and Health with Smart Sensor Systems. in 2023 International Conference Automatics and Informatics (ICAI) (432–437). IEEE. DOI: https://doi.org/10.1109/ICAI58806.2023.10339032

Shikha, S., Sethia, D., and Indu, S. (2024). Optimization of Wearable Biosensor Data for Stress Classification using Machine Learning and Explainable AI. IEEE Access, 12, 169310–169327. DOI: https://doi.org/10.1109/ACCESS.2024.3463742

Ullah, M., Akbar, S., Raza, A., and Zou, Q. (2024). DeepAVP‑TPPred: Identification of Antiviral Peptides Using Transformed Image-Based Localized Descriptors and Binary Tree Growth Algorithm. Bioinformatics, 40(5), 305. DOI: https://doi.org/10.1093/bioinformatics/btae305