Work in Progress: Remote Laboratory Delivery with an At-Home Biomechanical Kinematic Data Acquisition Method

Ahmed Sayed

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: Biomedical Engineering Division Poster Session
Page Count: 8
DOI: 10.18260/1-2--41648
Permanent URL: https://peer.asee.org/41648
Download Count: 199

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Ahmed Sayed Milwaukee School of Engineering

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Ahmed M. Sayed is a faculty member in the Department of Electrical Engineering and Computer Science, Biomedical Engineering program at MSOE University, where he has been since 2019. He is also an associate professor of Biomedical Engineering at Helwan University, Helwan, Egypt. From 2017 to 2019 he was a postdoctoral research associate at Bascom Palmer Eye Institute, University of Miami. He received his Ph.D. in Mechanical Engineering from West Virginia University in 2013. From 2013 to 2017 he worked at different health care facilities as a Medical Technology Consultant and as a Biomedical engineering lecturer at various Universities. Ahmed Sayed received his B.Sc. and M.Sc. degrees in systems and biomedical engineering from Cairo University, Egypt in 2003 and 2008, respectively.
He is the author/co-author of 40 publications in international peer-reviewed journals and conferences. He is listed as a co-inventor on 9 granted US patents in the field of Bioinstrumentation. He serves as an expert reviewer for several top-tier journals including IEEE Transactions on Biomedical Engineering and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency. He is a member of ASEE, ARVO, and a senior IEEE member. His fields of research interest are Image processing, Bioinstrumentation, Ultrasonics, Biomechanics, Finite-element modeling, and development of computer algorithms.

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Abstract

Under the remote learning mode due to COVID-19, educational laboratory modules lacking the active data acquisition step tend to lose students’ engagement and diminish their eagerness to explore further knowledge. Such shortcomings are more profound in practical fields of study, such as Biomechanics. The goal of this paper is to present a remote laboratory delivery and evaluation method where students can apply principles of kinematic and kinetic biomechanical analysis on their own body motions with a computer vision algorithm to interactively solve a motion analysis problem.

In this preliminary study, students were given the freedom to choose a specific body motion to be captured and analyzed, such as elbow, knee, wrist, and neck joint movements. Motion specifications included determination of the motion type, and also the starting and ending angular or linear positions. Readily available labels were utilized as passive joint markers. Students were then instructed to video record their joint motions using their laptop cameras. A custom video tracking algorithm specifically designed to track spatial locations was then employed to capture relative positions of the recorded motions. Laboratory instructions asked the students to perform kinematic calculations on the algorithm’s generated positional data to determine joint velocities and accelerations, and then perform kinetic analyses to estimate the associated muscle forces. Laboratory requirements were concluded with a reflection prompt to evaluate the activity’s workload and effort perceived by the students. These activities were delivered twice in two different academic terms. Samples of the produced kinematic data using our methods were verified in comparison with a standard physical motion capture system, where similar joint motion descriptive results were observed.

Results show that the completion rate of laboratory requirements was 97% in the first term of delivery, and 100% in the second term, as supported by the full technical reports submissions that included critical data analysis and reflections of the laboratory experience. Student reflections were very positive and expressed how the lab activity was interesting as it kept a high level of engagement and provided a way to make connections between practice and theory. In conclusion, the proposed approach may improve the students’ laboratory experience in learning biomechanics through a motion analysis scenario, and allow them to remotely be fully engaged, active, and passionate learners.

Citation
Format

Sayed, A. (2022, August), Work in Progress: Remote Laboratory Delivery with an At-Home Biomechanical Kinematic Data Acquisition Method Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41648

TY - CPAPER
AB - Under the remote learning mode due to COVID-19, educational laboratory modules lacking the active data acquisition step tend to lose students’ engagement and diminish their eagerness to explore further knowledge. Such shortcomings are more profound in practical fields of study, such as Biomechanics. The goal of this paper is to present a remote laboratory delivery and evaluation method where students can apply principles of kinematic and kinetic biomechanical analysis on their own body motions with a computer vision algorithm to interactively solve a motion analysis problem.

In this preliminary study, students were given the freedom to choose a specific body motion to be captured and analyzed, such as elbow, knee, wrist, and neck joint movements. Motion specifications included determination of the motion type, and also the starting and ending angular or linear positions. Readily available labels were utilized as passive joint markers. Students were then instructed to video record their joint motions using their laptop cameras. A custom video tracking algorithm specifically designed to track spatial locations was then employed to capture relative positions of the recorded motions. Laboratory instructions asked the students to perform kinematic calculations on the algorithm’s generated positional data to determine joint velocities and accelerations, and then perform kinetic analyses to estimate the associated muscle forces. Laboratory requirements were concluded with a reflection prompt to evaluate the activity’s workload and effort perceived by the students. These activities were delivered twice in two different academic terms. Samples of the produced kinematic data using our methods were verified in comparison with a standard physical motion capture system, where similar joint motion descriptive results were observed.

Results show that the completion rate of laboratory requirements was 97% in the first term of delivery, and 100% in the second term, as supported by the full technical reports submissions that included critical data analysis and reflections of the laboratory experience. Student reflections were very positive and expressed how the lab activity was interesting as it kept a high level of engagement and provided a way to make connections between practice and theory. In conclusion, the proposed approach may improve the students’ laboratory experience in learning biomechanics through a motion analysis scenario, and allow them to remotely be fully engaged, active, and passionate learners.

AU - Ahmed Sayed
CY - Minneapolis, MN
DA - 2022/08/23
PB - ASEE Conferences
TI - Work in Progress: Remote Laboratory Delivery with an At-Home Biomechanical Kinematic Data Acquisition Method
UR - https://peer.asee.org/41648
DO - 10.18260/1-2--41648
ER -