The analysis of animal locomotion is critical for characterizing and ultimately understanding behaviour. While locomotion quantification of single animals is straightforward, simultaneous analysis of multiple animals ...
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ISBN:
(纸本)9781467369022
The analysis of animal locomotion is critical for characterizing and ultimately understanding behaviour. While locomotion quantification of single animals is straightforward, simultaneous analysis of multiple animals in a group is challenging. If performed manually, such analyses are labour-intensive and potentially unreliable, thereby necessitating the use of machine vision algorithms for automatic processing. Machine vision algorithms need to reliably label each animal and maintain all animal identities throughout the video-recorded experiment. This allows detailed characterization of behaviours such as taxis, locomotion and social interaction. In this study, we present an algorithm for analysing the locomotion behaviour of the fruit fly Drosophila melanogaster, a popular model organism in neurobiology. Our algorithm detects all flies inside a circular arena, determines their position and orientation and assigns fly identities between consecutive frame pairs. Position and orientation of the flies are accurately estimated with average errors of 0.108 +/- 0.006 mm (approximately 5 % of fly body length) and 2.2 +/- 0.2 degrees, respectively. Importantly, fly identity is correctly assigned in 99.5% of the cases. Our algorithm can be used to quantify the linear and angular velocities of walking flies in the presence or absence of various behaviourally important stimuli.
Pneumatic artificial muscles (PAMs) are light and soft, and are expected to be applied to gait assistance robots with multiple actuators on the human body. The PAMs can be used as not only actuators, but also sensors ...
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Pneumatic artificial muscles (PAMs) are light and soft, and are expected to be applied to gait assistance robots with multiple actuators on the human body. The PAMs can be used as not only actuators, but also sensors to detect the gait phase by using their deformable bodies whose internal pressure changes in response to the wearer's gait. However, conventional methods to detect the gait phase by PAMs have targeted single point detection in a gait phase and used for only ON/OFF control of the PAM actuators. In this study, we proposed an algorithm to estimate the postural state of the wearer, especially the state of both hip joints, from the internal pressure information of the PAMs with a small amount of calculation by using clustering method, and succeeded in controlling the PAMs' pressure continuously based on this algorithm. The effectiveness of the proposed control method was verified through gait-assistive experiments using a treadmill. We measured the electromyogram of the adductor longus muscle under 3 subjects and a one-sided significant difference test was performed. As a result, we confirmed significant differences at the 1% significance level for 2 subjects and at the 10% significance level for the remaining subject, allowing us to evaluate the effectiveness of the proposed PAM control strategy.
This study presents a robust and reliable method of human posture recognition for visual surveillance systems. In order to recognise the human body, a recognition method is developed based on the skeleton of moving ob...
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This study presents a robust and reliable method of human posture recognition for visual surveillance systems. In order to recognise the human body, a recognition method is developed based on the skeleton of moving object. To obtain the skeleton of object, the authors describe some thinning algorithms for binary images, including one pass thinning algorithm, Zhang's thinning algorithm, Rosenfeld's thinning algorithm and a new thinning algorithm. Three performance measurements are chosen to evaluate these thinning algorithms. Comparing the performance results the authors found that the proposed thinning algorithm had managed to produce several improvements, including high thinness, connectivity, robustness to noise and low time consuming. Moreover, the skeleton obtained by the proposed thinning algorithm is one-pixel width and more smooth. Next, three different postures such as standing, bending and crawling will be estimated by using support vector machines as a classifier, which the histograms of horizontal and vertical projections are selected to define the feature. Finally, experimental results demonstrate that the human body and posture estimation algorithm have a robust and real-time performance, and is useful for the discrimination of human postures.
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