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Dynamic Analysis of human gait system through Machine Learning and Data Analysis Tool

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imabhi241/Gait_Analysis-Project

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Dynamic Analysis of Human Gait system through Machine Learning and Data Analysis Tool

Note: If Nootbook file does not open head over to the given link for Gait Visualization-https://bit.ly/3oeVnnd

This project is based on analysis of human gait for prediction of different Neurological disabilities related with gait dysfunction. A complete observation was made on 21 participants with different age group and gender for study of their gait patterns.

Authors

Documentation

Dataset 1 Dataset 2

Common Terms

  1. Gait is defined as the pattern of Human limb movement during different locomotions like running, walking, Jumping etc. An abnormal gait is the main factor for different diseases. The central nervous system regulates gait in a highly ordered fashion through a combination of voluntary and automatic processes. The basic locomotor pattern is an automatic process that results from rhythmic reciprocal bursts of flexor and extensor activity.
  2. Freezing of gait[FOG] is an abnormal gait pattern that can accompany Parkinson’s disease (PD) as well as other neurological disorders in which there are sudden, short and temporary episodes of an inability to move the feet forward despite the intention to walk. This results in the characteristic appearance of the feet making quick stepping movements in place.

Conclusion

After merging all factors we concluded that arm swings and leg swing are major indication of neurological Disease and can be used for its prediction. If early predictions are made than it can be cured with proper treatment.

We also predicted the accuracy of different Machine Learning Algorithms which can be used for given data set.

Acknowledgements

FAQ

How was the data recorded?

Two pair of inertial sensor and one EMG sensor was placed in arm and leg for recording data.

Which Machine Learning Algorithm is best for future studies?

SVM, Decision Tree or Random Forest can be used efficiently

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