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Abstract
(1) Background: Insects, which serve as model systems for many disciplines with their unique advantages, have not been extensively studied in gait research because of the lack of appropriate tools and insect models to properly study the insect gaits. (2) Methods: In this study, we present a gait analysis of grasshoppers with a closed-loop custom-designed motorized insect treadmill with an optical recording system for quantitative gait analysis. We used the eastern lubber grasshopper, a flightless and large-bodied species, as our insect model. Gait kinematics were recorded and analyzed by making three grasshoppers walk on the treadmill with various speeds from 0.1 to 1.5 m/s. (3) Results: Stance duty factor was measured as 70–95% and decreased as walking speed increased. As the walking speed increased, the number of contact legs decreased, and diagonal arrangement of contact was observed at walking speed of 1.1 cm/s. (4) Conclusions: This pilot study of gait analysis of grasshoppers using the custom-designed motorized insect treadmill with the optical recording system demonstrates the feasibility of quantitative, repeatable, and real-time insect gait analysis.
Highlights
IntroductionAs multiple muscles are involved in generating each kinematic output and a continuum of proper kinematic outputs constitutes the optimal gait [1,2], well-orchestrated neural activities are crucial for modulating the gait pattern in real time [3,4]
A stance duty factor is defined as the percentage of the duration in which a given leg is on the ground over the whole gait cycle
We found that the stance duty factor was negatively correlated with the walking speed (Figure 7)
Summary
As multiple muscles are involved in generating each kinematic output and a continuum of proper kinematic outputs constitutes the optimal gait [1,2], well-orchestrated neural activities are crucial for modulating the gait pattern in real time [3,4]. In this regard, gaits have been used as an important tool for understanding the operation of the complex neural circuitry [5,6]. Gait pattern dependencies on genetic and environmental factors can contribute to the study of biological evolution [15,16,17]
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