Document Type

Article

Publication Date

10-2020

Keywords

Remote learning, Laboratory data acquisition, Biomechanics, Video Tracking, COVID-19

Abstract

Background: Delivery of a hands-on laboratory experience is a real challenge in the present pandemic environment. Many instructors tend to acquire and record experimental data, and then instruct their students to analyze such data to produce results and lab reports in an online course mode. Such a process considerably diminishes students’ motivation, engagement, and eagerness to explore further knowledge, as students appreciate more experimental data that they gather themselves. Such drawbacks are even more profound in a practical field such as biomechanics, where students need to feel the sense of kinematic and kinetic data of their own body motions and muscle forces.
Purpose: The aim of this paper is to communicate with engineering educators facing challenges in the current pandemic time, a new teaching method that allows students to remotely gain a hands-on knowledge by applying the principles of mechanics on their body motions using a computer vision kinematics laboratory module that can be easily applied at home.
Methods: In this research, students first capture their selected body motion with a webcam at home. They are provided a video tracking algorithm that calculate spatial locations of the body segment motion. Students then perform calculations to estimate further kinematic and kinetic data, plot them, and comment on their own estimated muscle forces. The students’ perceived workload and effort in completing the lab requirements can also be evaluated. The proposed computer vision approach is utilized to calculate the participants’ kinematic data in an interactive motion analysis problem.
Results: As a work in progress study, initial results showed that 97% of the participating students successfully applied the computer vision-based kinematics module, supported by the generation and submission of full laboratory reports including critical data analysis of their body motion experience.
Conclusions: The proposed computer vision experimental approach may enhance the learning experience of biomechanics students at home in such an isolating pandemic environment. The demonstrated methods can be applied to many teaching fields including biomechanics and robotics.

Pages

9

Format

application/pdf

Publisher

South Dakota State University

Rights

© American Society for Engineering Education, 2020. Posted with permission.

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