Effectiveness of mindfulness: The evaluation using machine learning algorithm

Authors

  • Krishnamurthy Ramasubramanian Dept. of Computer Science & Engineering (CSE), Koneru Lakshmaiah Education Foundation, Hyderabad, India
  • Venkateswarlu Lendale Dept. of Computer Science & Engineering (CSE),Geethanjali College of Engineering and Technology, Hyderabad, India
  • Gillala Rekha Dept. of Computer Science & Engineering (CSE), Koneru Lakshmaiah Education Foundation, Hyderabad, India
  • Kalambur Swaminathan Lavanya Special Educator CEO, Bodhya Vocational Training unit, Hyderabad, India

DOI:

https://doi.org/10.31629/jit.v3i1.3337

Keywords:

cognitive modelling, network-oriented, temporal-causal network, mindfulness, extreme emotions

Abstract

Mindfulness is a practice that is thousands of years old. This practice is also included in the modern mindfulness-based stress reduction training, and its effects on our cognitive system are supported by extensive literature that comprehend mostly fMRI studies and task-oriented experiments with control groups. In this paper, the problem of testing the effects of mindfulness therapy, with specific regard to the yoga practice, is addressed with a Network-Oriented Modelling approach. The first component of the proposed network simulates the elicitation of an extreme stressful emotion due to a strong stress-inducing event. This was done following the same line of previous papers that proposed simulations of similar processes. A second component represents the role of memory, attention, and self-awareness in coping with the stressful event. Finally, the yoga practice, divided into physical movements and breathing, is modelled, to show its influence on memory, attention, and self-awareness, leading in this way to a reduction of the stress level.

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Published

2022-04-30

How to Cite

Ramasubramanian, K., Lendale, V., Rekha, G., & Lavanya, K. S. (2022). Effectiveness of mindfulness: The evaluation using machine learning algorithm. Journal of Innovation and Technology, 3(1), 1–5. https://doi.org/10.31629/jit.v3i1.3337

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Articles