Real-Time Human Energy Field Detection and Visualisation Using Advanced Image Processing Techniques

Lee, Yi Heng (2026) Real-Time Human Energy Field Detection and Visualisation Using Advanced Image Processing Techniques. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Visualization and analysis of the human energy field have gained increasing interest in image-processing-based wellness research. However, many existing approaches are limited by non-real-time operation, lack of user-friendly interaction, and heavy dependence on manual interpretation. Therefore, this project presents the development of a real-time human energy field visualization and analysis system using advanced image processing techniques with a graphical user interface for practical, structured, and quantitative assessment. The main objectives of this project are to develop a real-time processing framework for instant biofield analysis and to design an intuitive user interface for real-time visualization. To achieve these objectives, a software system was developed using Python with OpenCV, NumPy, scikit-image, Tkinter, and related image processing libraries. The system integrates controlled image acquisition, image enhancement, aura visualization rendering, chakra distribution analysis, brightness evaluation, symmetry assessment using Structural Similarity Index, and texture analysis using Gray-Level Co-occurrence Matrix features. In addition, an automatic report generation module was implemented to produce structured result summaries for before-and-after comparison. The developed system was tested using 10 participants under a controlled environment with standardized image acquisition conditions, including fixed camera position, full-spectrum lighting, white background, and consistent participant posture. Each participant was evaluated before and after a singing bowl therapy session. The results showed an average feature improvement score of 7.7 out of 8, indicating that most computed image features changed in the preferred direction. The feature-based analysis showed increases in vibrancy by 11.42%, symmetry SSIM by 9.39%, smoothness by 4.77%, harmony by 4.39%, and brightness by 2.49%. Meanwhile, the features where reduction was preferred also improved, with edge contrast decreasing by 1.62%, texture variation decreasing by 2.02%, and complexity decreasing by 2.48%. For chakra-based evaluation, the total number of balanced chakra instances increased from 39 before therapy to 53 after therapy, representing an improvement of 14 balanced chakra instances across the 10 participants. The overall average chakra alignment also increased from 91.31% before therapy to 92.87% after therapy, showing a general movement toward the balanced region. These quantitative results demonstrate that the developed framework was able to generate clear aura visualization outputs, chakra balance distributions, feature comparison results, and automatic reports in a stable and repeatable manner. In conclusion, the project successfully achieved its objectives by producing a functional real-time human energy field visualization and analysis system with GUI-based interaction, quantitative image analysis, chakra balance evaluation, and automatic report generation capability. The developed framework provides a structured foundation for further enhancement of image-processing-based human energy field studies and related wellness applications