Demographic Influences on Human Motion Patterns: A Feature-Based Study

Abstract

This study investigates the intricate relationships between demographic factors and human motion patterns, employing a feature-based analysis to find variations across different population segments. By examining a diverse range of demographic variables, such as age, gender, socioeconomic status, and cultural background, we aim to identify how these factors shape and influence human movement characteristics.For this work, data is collected through wearable sensors and features are extracted form data. To find the outcomes of this study, statisticalanalysis was conducted to predict the effect of demographic factors on gender basis. The aim of this study is to provide a comprehensive understanding of the demographic influences on human motion, which can have implications for various fields, including urban planning, healthcare, and security

Introduction

Human motion is a complex reflection of biological, psychological, and social factors, closely tied to individual identity and environment. Demographics like age, gender, socioeconomic status, and culture significantly influence movement patterns, which are important for applications in healthcare, rehabilitation, urban planning, security, and robotics. Advances in wearable sensors and motion tracking allow detailed analysis of gait and physical activities, aiding in clinical diagnosis and personalized treatment.

Research highlights differences in gait related to age and cultural background, with wearable technologies playing a key role in monitoring mobility and fall risk, especially in older adults. Quantitative gait assessment facilitates effective medical interventions, while rehabilitation robotics and AI enhance motion analysis and recovery.

The study involved 51 healthy adults performing varied movements while sensors recorded data from multiple body parts. Extracted features such as gait speed and joint angles were normalized for analysis. Statistical results showed gender differences, with males generally exhibiting higher median motion values, though some females had notably high values. Age comparisons revealed that younger individuals had greater variability in movement, while older adults showed more constrained motion patterns. However, ANOVA tests indicated no statistically significant differences across age groups for key motion features.

Conclusion

This work is used to assess the effect of demographic factors on motion features and the statistical analysis contribute to establish the relation between motion feature on two constraints age and gender.The results indicate the clear distinction in motion features for male and female participants. Similarly,when compared on age bases for young, middle and old age participants, the results showclear difference in motion features. In future, work can be extended to establish the similar relations for different races and further more parameters and tests can be included to make the system more predictable.

References

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Copyright

Copyright © 2025 Sandeep Gupta, Vibha Aggarwal , Manjeet Singh Patterh, Lovepreet Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.