A Low-Cost, Autonomous Gait Detection and Estimation System for Analyzing Gait Impairments in Mice

Authors

• Pranav U. Damale, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA.
• Edwin K. Chong, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA.
• Sean L. Hammond, Division of Clinical Pharmacology and Toxicology, University of Colorado, Anschutz, CO 80045-2608, USA.
• Ronald B. Tjalkens, Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523-1601, USA.

Document Type

Article

Abstract

With the advancement in imaging technology, many commercial systems have been developed for performing motion analysis in mice. However, available commercial systems are expensive and use proprietary software. In this paper, we describe a low-cost, camera-based design of an autonomous gait acquisition and analysis system for inspecting gait deficits in C57BL/6 mice. Our system includes video acquisition, autonomous gait-event detection, gait-parameter extraction, and result visualization. We provide a simple, user-friendly, step-by-step detailed methodology to apply well-known image processing techniques for detecting mice footfalls and calculating various gait parameters for analyzing gait abnormalities in healthy and neurotraumatic mice. The system was used in a live animal study for assessing recovery in a mouse model of Parkinson's disease. Using the videos acquired in the study, we validate the performance of our system with receiver operating characteristic (ROC) and Hit : Miss : False (H : M : F) detection analyses. Our system correctly detected the mice footfalls with an average H : M : F score of 92.1 : 2.3 : 5.6. The values for the area under an ROC curve for all the ROC plots are above 0.95, which indicates an almost perfect detection model. The ROC and H : M : F analyses show that our system produces accurate gait detection. The results observed from the gait assessment study are in agreement with the known literature. This demonstrates the practical viability of our system as a gait analysis tool.

Medical Subject Headings

Animals; Gait; Gait Analysis (methods); Humans; Image Processing, Computer-Assisted; Mice; Mice, Inbred C57BL; Software

Publication Date

1-1-2021

Publication Title

Journal of healthcare engineering

E-ISSN

2040-2309

Volume

2021

First Page

9937904

PubMed ID

34804462

Digital Object Identifier (DOI)

10.1155/2021/9937904

Recommended Citation

Damale, Pranav U.; Chong, Edwin K.; Hammond, Sean L.; and Tjalkens, Ronald B., "A Low-Cost, Autonomous Gait Detection and Estimation System for Analyzing Gait Impairments in Mice" (2021). Translational Neuroscience. 2494.
https://scholar.barrowneuro.org/neurobiology/2494