Prediction of Anxiety Levels Using Smartwatch Data in Uncontrolled Environments
Abstract
Anxiety is a prevalent disorder associated with changes in movement and sleep patterns. This study investigates wearable devices for continuous monitoring in real-life settings using deep learning. An LSTM network was evaluated to regress anxiety levels from accelerometer data collected over 24 hours from 41 volunteers. Anxiety scores were obtained using the STAI, with data augmentation via downsampling (20, 50, and 100 repetitions). The best performance was achieved with 50 repetitions (MAE: 6.04 training; 5.30 validation). Pearson correlations were 0.71 and 0.50, respectively. The results indicate potential for continuous and non-invasive mental health monitoring.References
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Bursa, S. Ö., Durmaz İncel, Ö., and Işıklar Alptekin, G. (2023). Building lightweight deep learning models with tensorflow lite for human activity recognition on mobile devices. Annals of Telecommunications, 78(11):687–702.
Duval, E. R., Javanbakht, A., and Liberzon, I. (2015). Neural circuits in anxiety and stress disorders: a focused review. Therapeutics and clinical risk management, pages 115–126.
Elkjær, E., Mikkelsen, M. B., Michalak, J., Mennin, D. S., and O’Toole, M. S. (2022a). Motor alterations in depression and anxiety disorders: A systematic review and meta-analysis. Journal of affective disorders, 317:373–387.
Elkjær, E., Mikkelsen, M. B., Michalak, J., Mennin, D. S., and O’Toole, M. S. (2022b). Motor alterations in depression and anxiety disorders: A systematic review and meta-analysis. Journal of affective disorders, 317:373–387.
Fang, L., Zeng, W., Zheng, S., Du, S., Yang, H., Luo, X., Zeng, S., Huang, Z., Chen, W., and Zhang, B. (2025). a. Depression and anxiety, 2025(1):3082856.
Franklin, A. R., Mathersul, D. C., Raine, A., and Ruscio, A. M. (2021a). Restlessness in generalized anxiety disorder: using actigraphy to measure physiological reactions to threat. Behavior therapy, 52(3):734–744.
Franklin, A. R., Mathersul, D. C., Raine, A., and Ruscio, A. M. (2021b). Restlessness in generalized anxiety disorder: using actigraphy to measure physiological reactions to threat. Behavior therapy, 52(3):734–744.
Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, pages 37–45.
Hamitouche, D., Zamorano, T., Barkat, Y., Parekh, D., Palaniyappan, L., Jalali, S., and Benrimoh, D. (2025). Sleep and activity patterns as transdiagnostic behavioral biomarkers in psychiatry: Longitudinal observational study from the deep-dd study. JMIR Form Res, 9.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Liang, Q., Xu, Z., Chen, S., and Qiu, Y. (2026). Differential neural dynamics in psychomotor retardation and agitation of depression. Human Brain Mapping, 47(2):e70453.
Luca, M., Luca, A., Barlati, S., Buson, L., Menesello, V., Magistrali, A., Silva, R., Perusi, G., Nibbio, G., Bignotti, S., Tura, G., Ferri, R., Gennarelli, M., Vita, A., Souery, D., Kasper, S., Zohar, J., Montgomery, S., Ferentinos, P., and Spina, E. (2026). Agitation and anxiety features define a more severe phenotype in unipolar depression. Psychiatry Research, 357:116928.
Pastre, M. and Lopez-Castroman, J. (2022). Actigraphy monitoring in anxiety disorders: A mini-review of the literature. Frontiers in psychiatry, 13:984878.
Pompili, M., Ducci, G., Galluzzo, A., Rosso, G., Palumbo, C., and De Berardis, D. (2021). The management of psychomotor agitation associated with schizophrenia or bipolar disorder: a brief review. International journal of environmental research and public health, 18(8):4368.
Spielberger, C. and Gorsuch, R. (1983). Manual for the State-trait Anxiety Inventory (form Y) (”self-evaluation Questionnaire”). Consulting Psychologists Press.
Straus, L. D., An, X., Ji, Y., McLean, S. A., Neylan, T. C., Group, A. S., Cakmak, A. S., Richards, A., Clifford, G. D., Liu, M., et al. (2023). Utility of wrist-wearable data for assessing pain, sleep, and anxiety outcomes after traumatic stress exposure. JAMA psychiatry, 80(3):220–229.
Vincent, C. J., Aguilar-Alvarez, R., Vanderhoof, S. O., Mott, D. D., and Jasnow, A. M. (2025). An amygdala-cortical circuit for encoding generalized fear memories. Molecular Psychiatry, pages 1–14.
Wang, J., Chen, Y., Hao, S., Peng, X., and Hu, L. (2019). Deep learning for sensor-based human activity recognition: A survey. Sensors, 17:416.
Published
2026-06-01
How to Cite
FERNANDES, Raphael M. M.; REBELLO, Bernardo M.; RODRIGUES, João Vitor P.; ALVES, Ana Luiza P.; CONCEIÇÃO, Gabriel V. S.; FERNANDES, Arthur M.; MUCHALUAT-SAADE, Débora C.; RAMOS, Taiane C..
Prediction of Anxiety Levels Using Smartwatch Data in Uncontrolled Environments. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1014-1025.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.21603.
