Abstract

Gait analysis is a process of learning the motion of human and animal by using wearable sensor approach and vision approach. This analysis is mainly used in medical and sports field where the study of body parts is crucial. 3-space sensor is a sensor consists of accelerometer, gyroscope sensor and compass sensor, built in one device. In this study, 3-space sensor is used to collect data of walking and jogging motion, of a test subject running on a treadmill. Angular velocity of the test subject’s arm and the angle of subject’s leaping motion are the two main components under investigation. Data are analyzed and processed with Principal of Component Analysis (PCA) technique. This method aims to combine and reduce the number of variables of the raw data. The Quiver function is used in order to generate feature vectors for both motions. Furthermore, the output of the process was used to create a system that can recognize human motion on any given data. The system is highly able to differentiate both of the motions.

Highlights

  • 3-space sensor is used to collect data of walking and jogging motion, of a test subject running on a treadmill

  • Gait analysis is a study about movement of animal, or in other words, a study of animal locomotion, with the aim to analyze human motions, which is widely used in medical purposes

  • It can be concluded that a 3-space sensor is useful in analyzing human motion since the device consists of three different sensors which give advantage in measureing the motion in several aspects simultaneously[8]

Read more

Summary

Introduction

Gait analysis is a study about movement of animal, or in other words, a study of animal locomotion, with the aim to analyze human motions, which is widely used in medical purposes. This implementation is crucial to illustrate the different body dynamics which depend on the test subjects’ conditions, as stated in [1]. This analysis can be done by using two methods. The second method is using wearable sensor, by attaching a tri-axial sensor to the upper limb of the test subject to generate sets of motion data. A system is developed to recognize and differentiate these motions

Methods
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call