Study of animal locomotion

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The study of animal locomotion is a branch of biology that investigates and quantifies how animals move.

Contents

Kinematics

Kinematics is the study of how objects move, whether they are mechanical or living. In animal locomotion, kinematics is used to describe the motion of the body and limbs of an animal. The goal is ultimately to understand how the movement of individual limbs relates to the overall movement of an animal within its environment. Below highlights the key kinematic parameters used to quantify body and limb movement for different modes of animal locomotion.

Quantifying locomotion

Walking

Legged locomotion is the dominant form of terrestrial locomotion, the movement on land. The motion of limbs is quantified by the kinematics of the limb itself (intralimb kinematics) and the coordination between limbs (interlimb kinematics). [1] [2]

Figure 1. Classifying stance and swing transitions of the front right (red) and left (blue) legs of a fly. The onset of stance (black dot) occurs at the peaks of the leg position signal, whereas, the onset of swing (light blue dot) occurs at the troughs. Classification of Swing and Stance.png
Figure 1. Classifying stance and swing transitions of the front right (red) and left (blue) legs of a fly. The onset of stance (black dot) occurs at the peaks of the leg position signal, whereas, the onset of swing (light blue dot) occurs at the troughs.

To quantify the intralimb kinematics and interlimb coordination during walking, the stance and swing phases of the step cycle must be isolated. [2] [3] [4] [5] Stance is associated with the portion of the step where the leg contacts the ground, whereas, swing is where the leg lifts off the ground and moves forward along the body. High-speed videography is used to record the motion of the legs. Pose-estimation methods are then used to track key point(s) on each leg, typically at the joints of the leg. [6] [7] [8] [9] After extracting the positions of each leg throughout a recording, there are several ways of determining the stance and swing phases of the step cycle. One approach involves using peak and trough detection of the leg tip positions in ego-centric coordinates and after the animal has been aligned to a common heading (Fig. 1). Alternatively, swing and stance can be classified as leg tip velocities above and below a chosen threshold, respectively. In this case, leg tip velocities are calculated in allocentric, or world-oriented, coordinates. Once swing and stance phases are determined, the following kinematic and coordination parameters can be calculated.

Intralimb kinematic parameters: [3] [1] [2] [4] [5]

  • Anterior Extreme Position (AEP): the forwardmost position of the leg (i.e. usually the start of stance phase).
  • Posterior Extreme Position (PEP): the rearmost position of the leg (i.e. usually the start of swing phase).
  • Step duration: elapsed time between two onsets of stance.
  • Step frequency: inverse of stride duration (i.e. number of strides per second)
  • Stance duration: time elapsed between stance onset and swing onset.
  • Swing duration: time elapsed between swing onset and the subsequent stance onset .
  • Step amplitude: the distance a leg travels during swing in a ego-centric reference frame.
  • Step length: the distance from the stance onset to stance onset in a world reference frame.
  • Stride range of motion: the leg's integrated path between stance onset and swing offset.
  • Joint angles: Walking can also be quantified through the analysis of joint angles. [10] [11] [12] During legged locomotion, an animal flexes and extends its joints in an oscillatory manner, creating a joint angle pattern that repeats across steps. The following are some useful joint angle analyses for characterizing walking:
  • Joint angle trace: a trace of the angles that a joint exhibits during walking.
  • Joint angle distribution: the distribution of angles of a joint.
  • Joint angle extremes: the maximum (extension) and minimum (flexion) angle of a joint during walking.
  • Joint angle variability across steps: the variability between joint angle traces of several steps.

Interlimb kinematic parameters

  • Phase offsets: the lag of a leg relative to the stride period of a reference leg.
  • Number of legs in stance: The number of legs in stance at a single point in time.
  • Tripod coordination strength (TCS): specific to hexapod interlimb coordination, this parameter determines how much the interlimb coordination resembles the canonical tripod gait. TCS is calculated as the ratio of the total time legs belonging to a tripod (i.e. left front, middle right, and hind left legs, or vice versa) are in swing together, by the time elapsed between the first leg of the tripod that enters swing and the last leg of the same tripod that exits swing.
  • Relationship between several joint angles: the relative angles of two joints, either from the same leg or between legs. For example, the angle of a human's left femur-tibia (knee) joint when the right femur-tibia joint is at its most flexed or extended angle.

Measures of walking stability

Static stability: minimum distance from the center of mass (COM) to any edge of the support polygon created by the legs in stance for each moment in time. [13] A walking animal is statically stable if there are enough legs to form the support polygon (i.e. 3 or more) and the COM is within the support polygon. Moreover, static stability is at its maximum when it lies at the center of the support polygon. Steps to calculate static stability are as follows:

  1. Find which legs are in stance and the location of the center of mass. Note, if there are less than 3 legs in stance then the animal is not statically stable.
  2. Form the support polygon by creating edges between these legs in a clock-wise manner.
  3. Determine if the center of mass lies inside or outside of the support polygon. The ray casting algorithm is a common approach of finding if a point is located within a polygon. If the center of mass is outside of the polygon then the animal is statically unstable.
  4. If the center of mass is inside the support polygon, calculate static stability by computing the minimum distance of the center of mass to any edge of the polygon.

Dynamic stability: dictates the degree to which deviations from periodic movement during walking will result in instability. [14]

Analyzing kinematics across steps

Quantifying walking often involves assessing the kinematics of individual steps. For more information on methods for acquiring this data, see Methods of Study. The first task is to parse walking data into individual steps. Methods for parsing individual steps from walking data rely heavily on the data collection process. At a high-level, walking data should be periodic with each cycle reflecting the movements of one step, and steps can therefore be parsed at the peaks of the signal. It is often useful to compare or pool step data. One difficulty in this pursuit is the variable length of steps both within and between legs. There are many ways to align steps, the following are a few useful methods.

  • Stretch step: steps of variable durations may be stretched to the same duration.
  • Step phase: the phase of each step can be computed which quantifies how far through the step each data point is. This normalizes the data by step length, allowing data from steps of variable lengths to be compared. The Hilbert transform may be used to calculate phase, however a manual phase calculation may be better for aligning peak (swing and stance start) alignment.
UMAP embedding of leg joint angle kinematics in walking fruit flies. The variability across individual flies is shown by their distinct clustering (C), yet their coordination patterns are similar (D). Karashchuk et al 2021 fly umap.png
UMAP embedding of leg joint angle kinematics in walking fruit flies. The variability across individual flies is shown by their distinct clustering (C), yet their coordination patterns are similar (D).

Fruit flies have six legs and four joints per leg with many joints moving in multiple planes. Thus, there are many kinematic degrees of freedom. Therefore, the continuous variability in coordination patterns across walking speeds and across individual flies can be visualized in a low dimensional embedding, [8] using techniques such as principal components analysis and UMAP.

In addition to stability, the robustness of a walking gait is also thought to be important in determining the gait of a fly at a particular walking speed. Robustness refers to how much offset in the timing of a legs stance can be tolerated before the fly becomes statically unstable. For instance, a robust gait may be particularly important when traversing uneven terrain, as it may cause unexpected disruptions in leg coordination. Using a robust gait would help the fly maintain stability in this case. Analyses suggest that flies may exhibit a compromise between the most stable and most robust gait at a given walking speed. [15]

Speed-dependent kinematic changes

Many animals alter walking kinematics as they modulate walking speed. [16] [17] [18] An interlimb kinematic parameter that is commonly speed dependent is gait, the stepping pattern across legs. While some animals alternate between distinct gaits as a function of speed, [19] others move along a continuum of gaits. [20] Similarly, animals commonly modulate intralimb parameters across speed. For example, fruit flies decrease stance duration and increase step length as forward speed increases. [21] Importantly, kinematics are not only modulated across forward velocity, but also rotational and sideslip velocities. [22] In these cases, asymmetry in the modulation between left and right legs is common.

Flight

Aerial locomotion is a form of movement used by many organisms and is typically powered by at least one pair of wings. Some organisms, however, have other morphological features that allow them to glide. There are many different flight modes, such as takeoff, hovering, soaring, and landing. [23] Quantifying wing movements during these flight modes will provide insight about the body and wing maneuvers that are required to execute these behaviors. [23] Wing orientation is quantified throughout the flight cycle by three angles that are defined in a coordinate system relative to the base of the wing. [24] [25] The magnitude of these three angles are often compared for upstrokes and downstrokes. [24] [25] [26] [27] In addition, kinematic parameters are used to characterize the flight cycle, which consists of an upstroke and a downstroke. [24] [26] [27] [25] Aerodynamics are often considered when quantifying aerial locomotion, as aerodynamic forces (e.g. lift or drag) are able to influence flight performance. [28] Key parameters from these three categories are defined as follows:

Angles to quantify wing orientation

Wing orientation is described in the coordinate system centered at the wing hinge. [24] The x-y plane coincides with the stroke plane, the plane parallel to the plane that contains both wing tips and is centered at the wing base. [24] Assuming the wing can modeled by the vector passing through the wing base and wing tip, the following angles describe the orientation of the wing: [24]

  • Stroke position: angle describing the anterior-to-posterior motion of the wings relative to the stroke plane. This angle is computed as the projection of the wing vector onto the stroke plane.
  • Stroke deviation: angle describing the vertical amplitude of the wings relative to the stroke plane. This angle is defined as the angle between the wing vector and its projection onto the stroke plane.
  • Angle of attack: angular orientation of the wings (i.e. tilt) relative to the stroke plane. This angle is computed as the angle between the wing cross section vector and the stroke plane.

Kinematic parameters

  • Upstroke amplitude: angular distance through which the wings travel during an upstroke.
  • Downstroke amplitude: angular distance through which the wings travel during a downstroke.
  • Stroke duration: time elapsed between the onset of two consecutive upstrokes.
  • Wingbeat frequency: inverse of stroke duration. The number of wingbeats per second.
  • Flight distance per wingbeat: the distance covered during each wingbeat.
  • Upstroke duration: time elapsed between the onset of an upstroke and the onset of a downstroke.
  • Downstroke duration: time elapsed between the onset of a downstroke and the onset of an upstroke.
  • Phase: if an organism has both front and hind wings, the lag of a wing pair relative to the other (reference) wing pair.

Aerodynamic parameters

  • Reynolds number: ratio of inertial forces to viscous forces. This metric helps describe how wing performance changes with body size. [28]

Swimming

Aquatic locomotion is incredibly diverse, ranging from flipper and fin based movement [29] to jet propulsion. [30] Below are some common methods for characterizing swimming:

Fin and flipper locomotion

Body, tail, or fin angle: the curvature of the body or displacement of a fin or flipper. [31]

Tail or fin frequency: the frequency of a fin or tail completing one movement cycle.

Jet propulsion

Jet propulsion consists of two phases - a refill phase during which an animal fills a cavity with water, and a contraction phase when they squeeze water out of the cavity to push them in the opposite direction. The size of the cavity can be measured in these two phases to compare the amount of water cycled through each propulsion. [30]

Methods of study

Documentary film, shot at 1200 fps, used to study the locomotion of a cheetah. The end of the video shows the methods used for filming.

A variety of methods and equipment are used to study animal locomotion:

Treadmills
are used to allow animals to walk or run while remaining stationary or confined with respect to external observers. This technique facilitates filming or recordings of physiological information from the animal (e.g., during studies of energetics [32] ). Some treadmills consist of a linear belt (single [33] or split belt [34] ) that constrains the animal to forward walking, while others allow 360 degrees of rotation. [35] [36] [34] Non-motorized treadmills move in response to an animal's self-initiated locomotion, while motorized treadmills externally drive locomotion and are often used to measure the endurance capacity (stamina) of animals. [37] [38]
Tethered locomotion
Animals may be fixed in place, allowing them to move while remaining stationary relative to their environment. Tethered animals can be lowered onto a treadmill to study walking, [36] suspended in air to study flight, [39] or submersed in water to study swimming. [40]
A fruit fly, Drosophila melanogaster, tethered and walking on a spherical treadmill. Slowed 6X. Drosophila tethered locomotion.gif
A fruit fly, Drosophila melanogaster, tethered and walking on a spherical treadmill. Slowed 6X.
Untethered locomotion
Animals may move through an environment without being held in place and their movement can be tracked for analysis of that behavior. [41] [42] [43] [44] However freely moving animals are more challenging to track in 3d for detailed kinematic analysis of intralimb coordination.
Visual arenas
locomotion can be prolonged and sometimes controlled using a visual arena displaying a particular pattern of light. Many animals use visual queues from their surroundings to control their locomotion and so presenting them with a pseudo optic flow or context-specific visual feature can prompt and prolong locomotion. [45] [36] [46] [47]
Racetracks
lined with photocells or filmed while animals run along them are used to measure acceleration and maximal sprint speed. [48] [49]
High-speed videography
for the study of the motion of an entire animal or parts of its body (i.e. Kinematics) is typically accomplished by tracking anatomical locations on the animal and then recording video of its movement from multiple angles. Traditionally, anatomical locations have been tracked using visual markers that have been placed on the animal's body. However, it is becoming increasingly more common to use computer vision techniques to achieve markerless pose estimation.
  • Marker-based pose estimation: Visual markers must be placed on an animal at the desired regions of interest. The location of each marker is determined for each video frame, and data from multiple views is integrated to give positions of each point through time. The visual markers can then be annotated in each frame manually. However, this is a time-consuming task, so computer vision techniques are often used to automate the detection of the markers.
  • Markerless pose estimation: User-defined body parts must be manually annotated in a series of frames to use as training data. [6] Deep learning and computer vision techniques are then employed to learn the location of the body parts in the training data. Next, the trained model is used to predict the location of the body parts in each frame on newly collected videos. The resulting time series data consists of the positions of the visible body parts at each frame in the video. Model parameters can be optimized to minimize tracking error and increase robustness.
The kinematic data obtained from either of these methods can be used to determine fundamental motion attributes such as velocity, acceleration, joint angles, and the sequencing and timing of kinematic events. These fundamental attributes can be used to quantify various higher level attributes, such as the physical abilities of the animal (e.g., its maximum running speed, how steep a slope it can climb), gait, neural control of locomotion, and responses to environmental variation. These can aid in formulation of hypotheses about the animal or locomotion in general.
Simultaneous measurement of ground forces (blue) and kinematics such as petiole trajectories (red) and stepping pattern (yellow) of walking desert ants in a laboratory environment in order to describe the alternating tripod gait. Recording rate: 500 fps, Playback rate: 10 fps.
Marker-based and markerless pose estimation approaches have advantages and disadvantages, so the method that is best suited for collecting kinematic data may be largely dependent on the animal of study. Marker-based tracking methods tend to be more portable than markerless methods, which require precise camera calibration. [50] Markerless approaches, however, overcome several weaknesses of marker-based tracking, since placing visual markers on the animal of study may be impractical, expensive, or time-consuming. [50] There are many publicly accessible software packages that provide support for markerless pose estimation. [6]
Force plates
are platforms, usually part of a trackway, that can be used to measure the magnitude and direction of forces of an animal's step. When used with kinematics and a sufficiently detailed model of anatomy, inverse dynamics solutions can determine the forces not just at the contact with the ground, but at each joint in the limb.
Electromyography
(EMG) is a method of detecting the electrical activity that occurs when muscles are activated, thus determining which muscles an animal uses for a given movement. This can be accomplished either by surface electrodes (usually in large animals) or implanted electrodes (often wires thinner than a human hair). Furthermore, the intensity of electrical activity can correlate to the level of muscle activity, with greater activity implying (though not definitively showing) greater force.
Optogenetics
is a method used to control the activity of targeted neurons that have been genetically modified to respond to light signals. Optogenetic activation and silencing of neurons can help determine which neurons are required to carry out certain locomotor behaviors, as well as the function of these neurons in the execution of the behavior.
Sonomicrometry
employs a pair of piezoelectric crystals implanted in a muscle or tendon to continuously measure the length of a muscle or tendon. This is useful because surface kinematics may be inaccurate due to skin movement. Similarly, if an elastic tendon is in series with the muscle, the muscle length may not be accurately reflected by the joint angle.
Tendon force buckles
measure the force produced by a single muscle by measuring the strain of a tendon. After the experiment, the tendon's elastic modulus is determined and used to compute the exact force produced by the muscle. However, this can only be used on muscles with long tendons.
Particle image velocimetry
is used in aquatic and aerial systems to measure the flow of fluid around and past a moving aquatic organism, allowing fluid dynamics calculations to determine pressure gradients, speeds, etc.
Fluoroscopy
allows real-time X-ray video, for precise kinematics of moving bones. Markers opaque to X-rays can allow simultaneous tracking of muscle length.

Many of the above methods can be combined to enhance the study of locomotion. For example, studies frequently combine EMG and kinematics to determine motor pattern, the series of electrical and kinematic events that produce a given movement. Optogenetic perturbations are also frequently combined with kinematics to study how locomotor behaviors and tasks are affected by the activity of a certain group of neurons. Observations resulting from optogenetic experiments may provide insight into the neural circuitry that underlies different locomotor behaviors. It is also common for studies to collect high-speed videos of animals on a treadmill. Such a setup may allow for increased accuracy and robustness when determining an animal's poses across time.

Modeling animal locomotion

Models of animal locomotion are important for gaining new insights and predications on how kinematics arise from the interactions of the nervous, skeletal, and/or muscular systems that would otherwise be difficult to glean from experiments. The following are types of animal locomotion models:

Neuromechanical models

Neuromechanics is a field that combines biomechanics and neuroscience to understand the complex interactions between the physical environment, nervous system, and the muscular and skeletal systems that consequently result in anticipated body movement. [51] Therefore, neuromechanical models aim to simulate movement given the neural commands to specific muscles, and how those muscles are connected to the animal's skeleton. [52] [53] [54] The key components of neuromechanical models are:

  1. A morphologically accurate 3D model of the animal's skeleton consisting of rigid bodies (i.e. bones) that are arranged in a naturalistic manner. In these models, the properties of each rigid body, like mass, length, and width, need to be prescribed. Additionally, the joints between rigid bodies need to be defined, both in terms of type (e.g. hinge and ball-in-socket) and degrees of freedom (i.e. how the rigid bodies move relative to one another). The final step is to assign a mesh object to each rigid body that determines the appearance (e.g. outer surface of a bone) and other contact properties of the rigid bodies. These skeletal models can be built using a variety of 3D modeling programs, such as Blender and Opensim Creator.
  2. After the skeletal model is built, the next step is to accurately define the attachment points of muscle to the rigid bodies. This assignment is crucial for the rigid bodies to be articulated in a naturalistic way. There are several type of muscle models that simulate the dynamics of muscle activation, contraction, and relaxation, which include Hill-type and Ekeberg-type muscle models. [53] [55]
  3. Neural controllers that simulate motor neuron recruitment and activity by central commands are used to dictate the timing and strength of modeled muscle activation. There are many flavors of these controllers, such as coupled phase oscillator and neural network models.
  4. An environment that incorporates physics is essential in simulating realistic movement of neuromechanical models because they will abide by the laws of physics. Environments used for physics simulation include, Opensim, [56] PyBullet, and MuJoCo.

Related Research Articles

<span class="mw-page-title-main">Walking</span> Gait of locomotion among legged animals

Walking is one of the main gaits of terrestrial locomotion among legged animals. Walking is typically slower than running and other gaits. Walking is defined by an "inverted pendulum" gait in which the body vaults over the stiff limb or limbs with each step. This applies regardless of the usable number of limbs—even arthropods, with six, eight, or more limbs, walk. In humans, walking has health benefits including improved mental health and reduced risk of cardiovascular disease and death.

<span class="mw-page-title-main">Gait</span> Pattern of movement of the limbs of animals

Gait is the pattern of movement of the limbs of animals, including humans, during locomotion over a solid substrate. Most animals use a variety of gaits, selecting gait based on speed, terrain, the need to maneuver, and energetic efficiency. Different animal species may use different gaits due to differences in anatomy that prevent use of certain gaits, or simply due to evolved innate preferences as a result of habitat differences. While various gaits are given specific names, the complexity of biological systems and interacting with the environment make these distinctions "fuzzy" at best. Gaits are typically classified according to footfall patterns, but recent studies often prefer definitions based on mechanics. The term typically does not refer to limb-based propulsion through fluid mediums such as water or air, but rather to propulsion across a solid substrate by generating reactive forces against it.

<span class="mw-page-title-main">Gait analysis</span> Study of locomotion

Gait analysis is the systematic study of animal locomotion, more specifically the study of human motion, using the eye and the brain of observers, augmented by instrumentation for measuring body movements, body mechanics, and the activity of the muscles. Gait analysis is used to assess and treat individuals with conditions affecting their ability to walk. It is also commonly used in sports biomechanics to help athletes run more efficiently and to identify posture-related or movement-related problems in people with injuries.

<span class="mw-page-title-main">Gait (human)</span> A pattern of limb movements made during locomotion

A gait is a manner of limb movements made during locomotion. Human gaits are the various ways in which humans can move, either naturally or as a result of specialized training. Human gait is defined as bipedal forward propulsion of the center of gravity of the human body, in which there are sinuous movements of different segments of the body with little energy spent. Varied gaits are characterized by differences such as limb movement patterns, overall velocity, forces, kinetic and potential energy cycles, and changes in contact with the ground.

Biarticular muscles are muscles that cross two joints rather than just one, such as the hamstrings which cross both the hip and the knee. The function of these muscles is complex and often depends upon both their anatomy and the activity of other muscles at the joints in question. Their role in movement is currently poorly understood.

In terrestrial animals, plantigrade locomotion means walking with the toes and metatarsals flat on the ground. It is one of three forms of locomotion adopted by terrestrial mammals. The other options are digitigrade, walking on the toes with the heel and wrist permanently raised, and unguligrade, walking on the nail or nails of the toes with the heel/wrist and the digits permanently raised. The leg of a plantigrade mammal includes the bones of the upper leg (femur/humerus) and lower leg. The leg of a digitigrade mammal also includes the metatarsals/metacarpals, the bones that in a human compose the arch of the foot and the palm of the hand. The leg of an unguligrade mammal also includes the phalanges, the finger and toe bones.

<span class="mw-page-title-main">Animal locomotion</span> Self-propulsion by an animal

Animal locomotion, in ethology, is any of a variety of methods that animals use to move from one place to another. Some modes of locomotion are (initially) self-propelled, e.g., running, swimming, jumping, flying, hopping, soaring and gliding. There are also many animal species that depend on their environment for transportation, a type of mobility called passive locomotion, e.g., sailing, kiting (spiders), rolling or riding other animals (phoresis).

Robot locomotion is the collective name for the various methods that robots use to transport themselves from place to place.

In physiology, motor coordination is the orchestrated movement of multiple body parts as required to accomplish intended actions, like walking. This coordination is achieved by adjusting kinematic and kinetic parameters associated with each body part involved in the intended movement. The modifications of these parameters typically relies on sensory feedback from one or more sensory modalities, such as proprioception and vision.

<span class="mw-page-title-main">Campaniform sensilla</span> Class of mechanoreceptors found in insects

Campaniform sensilla are a class of mechanoreceptors found in insects, which respond to local stress and strain within the animal's cuticle. Campaniform sensilla function as proprioceptors that detect mechanical load as resistance to muscle contraction, similar to mammalian Golgi tendon organs. Sensory feedback from campaniform sensilla is integrated in the control of posture and locomotion.

A facultative biped is an animal that is capable of walking or running on two legs (bipedal), as a response to exceptional circumstances (facultative), while normally walking or running on four limbs or more. In contrast, obligate bipedalism is where walking or running on two legs is the primary method of locomotion. Facultative bipedalism has been observed in several families of lizards and multiple species of primates, including sifakas, capuchin monkeys, baboons, gibbons, gorillas, bonobos and chimpanzees. Several dinosaur and other prehistoric archosaur species are facultative bipeds, most notably ornithopods and marginocephalians, with some recorded examples within sauropodomorpha. Different facultatively bipedal species employ different types of bipedalism corresponding to the varying reasons they have for engaging in facultative bipedalism. In primates, bipedalism is often associated with food gathering and transport. In lizards, it has been debated whether bipedal locomotion is an advantage for speed and energy conservation or whether it is governed solely by the mechanics of the acceleration and lizard's center of mass. Facultative bipedalism is often divided into high-speed (lizards) and low-speed (gibbons), but some species cannot be easily categorized into one of these two. Facultative bipedalism has also been observed in cockroaches and some desert rodents.

<span class="mw-page-title-main">Arboreal locomotion</span> Movement of animals through trees

Arboreal locomotion is the locomotion of animals in trees. In habitats in which trees are present, animals have evolved to move in them. Some animals may scale trees only occasionally, but others are exclusively arboreal. The habitats pose numerous mechanical challenges to animals moving through them and lead to a variety of anatomical, behavioral and ecological consequences as well as variations throughout different species. Furthermore, many of these same principles may be applied to climbing without trees, such as on rock piles or mountains.

<span class="mw-page-title-main">Comparative foot morphology</span> Comparative anatomy

Comparative foot morphology involves comparing the form of distal limb structures of a variety of terrestrial vertebrates. Understanding the role that the foot plays for each type of organism must take account of the differences in body type, foot shape, arrangement of structures, loading conditions and other variables. However, similarities also exist among the feet of many different terrestrial vertebrates. The paw of the dog, the hoof of the horse, the manus (forefoot) and pes (hindfoot) of the elephant, and the foot of the human all share some common features of structure, organization and function. Their foot structures function as the load-transmission platform which is essential to balance, standing and types of locomotion.

<span class="mw-page-title-main">Undulatory locomotion</span>

Undulatory locomotion is the type of motion characterized by wave-like movement patterns that act to propel an animal forward. Examples of this type of gait include crawling in snakes, or swimming in the lamprey. Although this is typically the type of gait utilized by limbless animals, some creatures with limbs, such as the salamander, forgo use of their legs in certain environments and exhibit undulatory locomotion. In robotics this movement strategy is studied in order to create novel robotic devices capable of traversing a variety of environments.

Human locomotion is considered to take two primary forms: walking and running. In contrast, many quadrupeds have three distinct forms of locomotion: walk, trot, and gallop. Walking is a form of locomotion defined by a double support phase when both feet are on the ground at the same time. Running is a form of locomotion that does not have this double support phase.

X-ray motion analysis is a technique used to track the movement of objects using X-rays. This is done by placing the subject to be imaged in the center of the X-ray beam and recording the motion using an image intensifier and a high-speed camera, allowing for high quality videos sampled many times per second. Depending on the settings of the X-rays, this technique can visualize specific structures in an object, such as bones or cartilage. X-ray motion analysis can be used to perform gait analysis, analyze joint movement, or record the motion of bones obscured by soft tissue. The ability to measure skeletal motions is a key aspect to one's understanding of vertebrate biomechanics, energetics, and motor control.

A metachronal swimming or metachronal rowing is the swimming technique used by animals with multiple pairs of swimming legs. In this technique, appendages are sequentially stroked in a back-to-front wave moving along the animal’s body. In literature, while metachronal rhythm or metachronal wave usually refer to the movement of cilia; metachronal coordination, metachronal beating, metachronal swimming or metachronal rowing usually refer to the leg movement of arthropods, such as mantis shrimp, copepods, antarctic krill etc. though all of them refer to the similar locomotion pattern.

Hair-plates are a type of proprioceptor found in the folds of insect joints. They consist of a cluster of hairs, in which each hair is innervated by a single mechanosensory neuron. Functionally, hair-plates operate as "limit-detectors" by signaling the extreme ranges of motion of a joint.

Arachnid locomotion is the various means by which arachnids walk, run, or jump; they make use of more than muscle contraction, employing additional methods like hydraulic compression. Another adaptation seen especially in larger arachnid variants is inclusion of elastic connective tissues.

<span class="mw-page-title-main">Femoral chordotonal organ</span> Sensory organ in insect legs

The femoral chordotonal organ is a group of mechanosensory neurons found in an insect leg that detects the movements and the position of the femur/tibia joint. It is thought to function as a proprioceptor that is critical for precise control of leg position by sending the information regarding the femur/tibia joint to the motor circuits in the ventral nerve cord and the brain

References

  1. 1 2 Darmohray, Dana M.; Jacobs, Jovin R.; Marques, Hugo G.; Carey, Megan R. (2019-04-03). "Spatial and Temporal Locomotor Learning in Mouse Cerebellum". Neuron. 102 (1): 217–231.e4. doi: 10.1016/j.neuron.2019.01.038 . ISSN   0896-6273. PMID   30795901.
  2. 1 2 3 DeAngelis, Brian D.; Zavatone-Veth, Jacob A.; Clark, Damon A (2019-06-28). Calabrese, Ronald L. (ed.). "The manifold structure of limb coordination in walking Drosophila". eLife. 8: e46409. doi: 10.7554/eLife.46409 . ISSN   2050-084X. PMC   6598772 . PMID   31250807.
  3. 1 2 Mendes, César S.; Bartos, Imre; Akay, Turgay; Márka, Szabolcs; Mann, Richard S. (2013-01-08). Calabrese, Ron (ed.). "Quantification of gait parameters in freely walking wild type and sensory deprived Drosophila melanogaster". eLife. 2: e00231. doi: 10.7554/eLife.00231 . ISSN   2050-084X. PMC   3545443 . PMID   23326642.
  4. 1 2 Pfeffer, Sarah Elisabeth; Wahl, Verena Luisa; Wittlinger, Matthias; Wolf, Harald (2019-10-15). "High-speed locomotion in the Saharan silver ant, Cataglyphis bombycina". Journal of Experimental Biology. 222 (20): jeb198705. doi: 10.1242/jeb.198705 . ISSN   0022-0949. PMID   31619540.
  5. 1 2 Wosnitza, Anne; Bockemühl, Till; Dübbert, Michael; Scholz, Henrike; Büschges, Ansgar (2013-02-01). "Inter-leg coordination in the control of walking speed in Drosophila". Journal of Experimental Biology. 216 (3): 480–491. doi: 10.1242/jeb.078139 . ISSN   0022-0949. PMID   23038731.
  6. 1 2 3 Mathis, Alexander; Mamidanna, Pranav; Cury, Kevin M.; Abe, Taiga; Murthy, Venkatesh N.; Mathis, Mackenzie Weygandt; Bethge, Matthias (September 2018). "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning". Nature Neuroscience. 21 (9): 1281–1289. doi:10.1038/s41593-018-0209-y. ISSN   1546-1726.
  7. ↑ Pereira, Talmo D.; Tabris, Nathaniel; Matsliah, Arie; Turner, David M.; Li, Junyu; Ravindranath, Shruthi; Papadoyannis, Eleni S.; Normand, Edna; Deutsch, David S.; Wang, Z. Yan; McKenzie-Smith, Grace C.; Mitelut, Catalin C.; Castro, Marielisa Diez; D’Uva, John; Kislin, Mikhail (April 2022). "SLEAP: A deep learning system for multi-animal pose tracking". Nature Methods. 19 (4): 486–495. doi: 10.1038/s41592-022-01426-1 . ISSN   1548-7105. PMC   9007740 .
  8. 1 2 3 Karashchuk, Pierre; Rupp, Katie L.; Dickinson, Evyn S.; Walling-Bell, Sarah; Sanders, Elischa; Azim, Eiman; Brunton, Bingni W.; Tuthill, John C. (September 2021). "Anipose: A toolkit for robust markerless 3D pose estimation". Cell Reports. 36 (13): 109730. doi:10.1016/j.celrep.2021.109730. PMC   8498918 . PMID   34592148.
  9. ↑ Gosztolai, Adam; Günel, Semih; Lobato-Ríos, Victor; Pietro Abrate, Marco; Morales, Daniel; Rhodin, Helge; Fua, Pascal; Ramdya, Pavan (August 2021). "LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals". Nature Methods. 18 (8): 975–981. doi:10.1038/s41592-021-01226-z. ISSN   1548-7105. PMC   7611544 .
  10. ↑ Jin, Li; Hahn, Michael E. (2019-04-03). "Comparison of lower extremity joint mechanics between healthy active young and middle age people in walking and running gait". Scientific Reports. 9 (1): 5568. Bibcode:2019NatSR...9.5568J. doi: 10.1038/s41598-019-41750-9 . ISSN   2045-2322. PMC   6447628 . PMID   30944360.
  11. ↑ Cruse, H.; Bartling, Ch. (1995-09-01). "Movement of joint angles in the legs of a walking insect, Carausius morosus". Journal of Insect Physiology. 41 (9): 761–771. doi:10.1016/0022-1910(95)00032-P. ISSN   0022-1910.
  12. ↑ Tsang, Dorothea J.; Lukac, Meghan; Martin, Anne E. (2019-12-01). "Characterization of statistical persistence in joint angle variation during walking". Human Movement Science. 68: 102528. doi:10.1016/j.humov.2019.102528. ISSN   0167-9457. PMID   31706119. S2CID   207944935.
  13. ↑ Szczecinski, Nicholas S.; Bockemühl, Till; Chockley, Alexander S.; Büschges, Ansgar (2018-11-16). "Static stability predicts the continuum of interleg coordination patterns in Drosophila". Journal of Experimental Biology. 221 (22): jeb189142. doi: 10.1242/jeb.189142 . ISSN   0022-0949. PMID   30274987. S2CID   52903595.
  14. ↑ "Dynamic Stability - an overview | ScienceDirect Topics". www.sciencedirect.com. Retrieved 2022-06-01.
  15. ↑ Szczecinski, Nicholas S.; Bockemühl, Till; Chockley, Alexander S.; Büschges, Ansgar (2018-01-01). "Static stability predicts the continuum of interleg coordination patterns inDrosophila". Journal of Experimental Biology. doi: 10.1242/jeb.189142 . ISSN   1477-9145.
  16. ↑ Mendes, César S.; Bartos, Imre; Márka, Zsuzsanna; Akay, Turgay; Márka, Szabolcs; Mann, Richard S. (2015-07-22). "Quantification of gait parameters in freely walking rodents". BMC Biology. 13 (1): 50. doi: 10.1186/s12915-015-0154-0 . ISSN   1741-7007. PMC   4511453 . PMID   26197889.
  17. ↑ Chun, Chanwoo; Biswas, Tirthabir; Bhandawat, Vikas (2021-02-03). Berman, Gordon J; Calabrese, Ronald L; Berman, Gordon J (eds.). "Drosophila uses a tripod gait across all walking speeds, and the geometry of the tripod is important for speed control". eLife. 10: e65878. doi: 10.7554/eLife.65878 . ISSN   2050-084X. PMC   7932689 . PMID   33533718.
  18. ↑ Fukuchi, Claudiane Arakaki; Fukuchi, Reginaldo Kisho; Duarte, Marcos (2019-06-27). "Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis". Systematic Reviews. 8 (1): 153. doi: 10.1186/s13643-019-1063-z . ISSN   2046-4053. PMC   6595586 . PMID   31248456.
  19. ↑ Robilliard, Justine J.; Pfau, Thilo; Wilson, Alan M. (2007-01-15). "Gait characterisation and classification in horses". Journal of Experimental Biology. 210 (2): 187–197. doi: 10.1242/jeb.02611 . ISSN   0022-0949. PMID   17210956. S2CID   28652969.
  20. ↑ Dürr, Volker; Theunissen, Leslie M.; Dallmann, Chris J.; Hoinville, Thierry; Schmitz, Josef (2017-12-29). "Motor flexibility in insects: adaptive coordination of limbs in locomotion and near-range exploration". Behavioral Ecology and Sociobiology. 72 (1): 15. doi:10.1007/s00265-017-2412-3. ISSN   1432-0762. S2CID   22745483.
  21. ↑ Mendes, César S; Bartos, Imre; Akay, Turgay; Márka, Szabolcs; Mann, Richard S (2013-01-08). "Quantification of gait parameters in freely walking wild type and sensory deprived Drosophila melanogaster". eLife. 2: e00231. doi: 10.7554/eLife.00231 . ISSN   2050-084X. PMC   3545443 . PMID   23326642.
  22. ↑ DeAngelis, Brian D; Zavatone-Veth, Jacob A; Clark, Damon A (2019-06-28). Calabrese, Ronald L (ed.). "The manifold structure of limb coordination in walking Drosophila". eLife. 8: e46409. doi: 10.7554/eLife.46409 . ISSN   2050-084X. PMC   6598772 . PMID   31250807.
  23. 1 2 Berg Angela, M.; Biewener, Andrew A. (2010). "Wing and body kinematics of takeoff and landing flight in the pigeon (Columba livia)". Journal of Experimental Biology. 213 (10): 1651–1658. doi: 10.1242/jeb.038109 . PMID   20435815.
  24. 1 2 3 4 5 6 Cheng, X; Sun, M. (2016). "Wing-kinematics measurement and aerodynamics in a small insect in hovering flight". Scientific Reports. 6: 25706. Bibcode:2016NatSR...625706C. doi: 10.1038/srep25706 . PMC   4863373 . PMID   27168523.
  25. 1 2 3 Fry, S. N.; Sayaman R; Dickinson, M. H. (2005). "The aerodynamics of hovering flight in Drosophila". Journal of Experimental Biology. 208 (12): 2303–2318. doi: 10.1242/jeb.01612 . PMID   15939772.
  26. 1 2 Ruppel, G. (1989). "Kinematic Analysis of Symmetrical Flight Manoeuvers of Odanata" (PDF). Journal of Experimental Biology. 144: 13–42. doi: 10.1242/jeb.144.1.13 .
  27. 1 2 Fontaine, E. I.; Zabala, F.; Dickinson M. H.; Burdick, J. W. (2009). "Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking". Journal of Experimental Biology. 212 (9): 1307–23. doi: 10.1242/jeb.025379 . PMID   19376952.
  28. 1 2 Birch, J. M.; Dickson, W. B.; Dickinson, M. H. (2004). "Force production and flow structure of the leading edge vortex on flapping wings at high and low Reynolds numbers". Journal of Experimental Biology. 207 (7): 1063–1072. doi: 10.1242/jeb.00848 . PMID   14978049.
  29. ↑ Jeanniard-du-Dot, Tiphaine; Trites, Andrew W.; Arnould, John P. Y.; Speakman, John R.; Guinet, Christophe (2016-09-23). "Flipper strokes can predict energy expenditure and locomotion costs in free-ranging northern and Antarctic fur seals". Scientific Reports. 6 (1): 33912. Bibcode:2016NatSR...633912J. doi:10.1038/srep33912. ISSN   2045-2322. PMC   5034273 . PMID   27658718.
  30. 1 2 York, Carly A.; Bartol, Ian K.; Krueger, Paul S.; Thompson, Joseph T. (2020-11-05). "Squids use multiple escape jet patterns throughout ontogeny". Biology Open. 9 (11). doi:10.1242/bio.054585. ISSN   2046-6390. PMC   7657469 . PMID   32973078.
  31. ↑ McClenahan, Phil; Troup, Michael; Scott, Ethan K. (2012-02-16). "Fin-Tail Coordination during Escape and Predatory Behavior in Larval Zebrafish". PLOS ONE. 7 (2): e32295. Bibcode:2012PLoSO...732295M. doi: 10.1371/journal.pone.0032295 . ISSN   1932-6203. PMC   3281131 . PMID   22359680.
  32. ↑ Taylor, C. Richard; Schmidt-Nielsen, Knut; Raab, J. L. (1970). "Scaling of energy cost of running to body size in mammals". American Journal of Physiology. 219 (4): 1104–1107. doi:10.1152/ajplegacy.1970.219.4.1104. PMID   5459475.
  33. ↑ Yao, Jie; Guo, Ning; Xiao, Yanqiu; Li, Zhili; Li, Yinghui; Pu, Fang; Fan, Yubo (2019-08-22). "Lower limb joint motion and muscle force in treadmill and over-ground exercise". BioMedical Engineering OnLine. 18 (1): 89. doi: 10.1186/s12938-019-0708-4 . ISSN   1475-925X. PMC   6704526 . PMID   31438944.
  34. 1 2 Lee, Song Joo; Hidler, Joseph (2008-03-01). "Biomechanics of overground vs. treadmill walking in healthy individuals". Journal of Applied Physiology. 104 (3): 747–755. doi:10.1152/japplphysiol.01380.2006. ISSN   8750-7587. PMID   18048582. S2CID   7076431.
  35. ↑ Soni, Smit; Lamontagne, Anouk (2020-11-23). "Characterization of speed adaptation while walking on an omnidirectional treadmill". Journal of NeuroEngineering and Rehabilitation. 17 (1): 153. doi: 10.1186/s12984-020-00787-y . ISSN   1743-0003. PMC   7684919 . PMID   33228761.
  36. 1 2 3 Seelig, Johannes D.; Chiappe, M. Eugenia; Lott, Gus K.; Dutta, Anirban; Osborne, Jason E.; Reiser, Michael B.; Jayaraman, Vivek (July 2010). "Two-photon calcium imaging from head-fixed Drosophila during optomotor walking behavior". Nature Methods. 7 (7): 535–540. doi:10.1038/nmeth.1468. ISSN   1548-7105. PMC   2945246 . PMID   20526346.
  37. ↑ Garland Jr., Theodore (1984). "Physiological correlates of locomotory performance in a lizard: an allometric approach" (PDF). American Journal of Physiology. 247 (5): R806–R815. doi:10.1152/ajpregu.1984.247.5.r806. PMID   6238543.
  38. ↑ Meek, Thomas H.; Lonquich, Brian P.; Hannon, Robert M.; Garland Jr., Theodore (2009). "Endurance capacity of mice selectively bred for high voluntary wheel running" (PDF). Journal of Experimental Biology. 212 (18): 2908–2917. doi: 10.1242/jeb.028886 . PMID   19717672. S2CID   127701.
  39. ↑ Currier, Timothy A.; Nagel, Katherine I. (2018-11-19). "Multisensory Control of Orientation in Tethered Flying Drosophila". Current Biology. 28 (22): 3533–3546.e6. doi:10.1016/j.cub.2018.09.020. ISSN   0960-9822. PMC   6503675 . PMID   30393038.
  40. ↑ Amaro, Nuno; Marinho, Daniel A; Batalha, Nuno; Marques, Mário C; Morouço, Pedro (2014-07-08). "Reliability of Tethered Swimming Evaluation in Age Group Swimmers". Journal of Human Kinetics. 41: 155–162. doi:10.2478/hukin-2014-0043. ISSN   1640-5544. PMC   4120449 . PMID   25114742.
  41. ↑ Kong, Zhaodan; Fuller, Nathan; Wang, Shuai; Özcimder, Kayhan; Gillam, Erin; Theriault, Diane; Betke, Margrit; Baillieul, John (2016-06-06). "Perceptual Modalities Guiding Bat Flight in a Native Habitat". Scientific Reports. 6 (1): 27252. Bibcode:2016NatSR...627252K. doi:10.1038/srep27252. ISSN   2045-2322. PMC   4893665 . PMID   27264498.
  42. ↑ Yamada, Yasufumi; Mibe, Yurina; Yamamoto, Yuya; Ito, Kentaro; Heim, Olga; Hiryu, Shizuko (2020-07-01). "Modulation of acoustic navigation behaviour by spatial learning in the echolocating bat Rhinolophus ferrumequinum nippon". Scientific Reports. 10 (1): 10751. Bibcode:2020NatSR..1010751Y. doi:10.1038/s41598-020-67470-z. ISSN   2045-2322. PMC   7329871 . PMID   32612132.
  43. ↑ Simon, Jasper C.; Dickinson, Michael H. (2010-01-27). "A New Chamber for Studying the Behavior of Drosophila". PLOS ONE. 5 (1): e8793. Bibcode:2010PLoSO...5.8793S. doi: 10.1371/journal.pone.0008793 . ISSN   1932-6203. PMC   2811731 . PMID   20111707.
  44. ↑ Kim, Dal Hyung; Kim, Jungsoo; Marques, João C.; Grama, Abhinav; Hildebrand, David G C.; Gu, Wenchao; Li, Jennifer M.; Robson, Drew N. (2017). "Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish". Nature Methods. 14 (11): 1107–1114. doi:10.1038/nmeth.4429. PMID   28892088. S2CID   26906300.
  45. ↑ Guo, Aike; Lu, Huimin; Zhang, Ke; Ren, Qingzhong; Chiang Wong, Yah-Num (2013-01-01). "Visual Learning and Decision Making in Drosophila melanogaster". Handbook of Behavioral Neuroscience. 22: 378–394. doi:10.1016/B978-0-12-415823-8.00028-9. ISBN   9780124158238. ISSN   1569-7339.
  46. ↑ Markov, Daniil A.; Petrucco, Luigi; Kist, Andreas M.; Portugues, Ruben (2020-09-14). "A cerebellar internal model calibrates a feedback controller involved in sensorimotor control". bioRxiv   10.1101/2020.02.12.945956 .
  47. ↑ Dehmelt, Florian A.; Meier, Rebecca; Hinz, Julian; Yoshimatsu, Takeshi; Simacek, Clara A.; Wang, Kun; Baden, Tom; Arrenberg, Aristides B. (2020-09-23). "Gaze stabilisation behaviour is anisotropic across visual field locations in zebrafish". bioRxiv   10.1101/754408 .
  48. ↑ Huey, Raymond B.; Hertz, Paul E. (1982). "Effects of body size and slope on sprint speed of a lizard (Stellio (Agama) stellio)" (PDF). Journal of Experimental Biology. 97: 401–409. doi:10.1242/jeb.97.1.401.
  49. ↑ Huey, Raymond B.; Hertz, Paul E. (1984). "Effects of body size and slope on acceleration of a lizard (Stellio stellio)" (PDF). Journal of Experimental Biology. 110: 113–123. doi: 10.1242/jeb.110.1.113 .
  50. 1 2 Xu, Tong (October 2019). "Single-view and Multi-view Methods in Marker-less 3D Human Motion Capture". Journal of Physics: Conference Series. 1335 (1): 012022. Bibcode:2019JPhCS1335a2022X. doi: 10.1088/1742-6596/1335/1/012022 . ISSN   1742-6588.
  51. ↑ Nishikawa, K.; Biewener, A. A.; Aerts, P.; Ahn, A. N.; Chiel, H. J.; Daley, M. A.; Daniel, T. L.; Full, R. J.; Hale, M. E.; Hedrick, T. L.; Lappin, A. K.; Nichols, T. R.; Quinn, R. D.; Satterlie, R. A.; Szymik, B. (2007-05-10). "Neuromechanics: an integrative approach for understanding motor control". Integrative and Comparative Biology. 47 (1): 16–54. doi: 10.1093/icb/icm024 . ISSN   1540-7063. PMID   21672819.
  52. ↑ Isakov, Alexander; Buchanan, Sean M.; Sullivan, Brian; Ramachandran, Akshitha; Chapman, Joshua K. S.; Lu, Edward S.; Mahadevan, L.; de Bivort, Benjamin (2016-01-01). "Recovery of locomotion after injury inDrosophiladepends on proprioception". Journal of Experimental Biology. 219 (Pt 11): 1760–1771. doi: 10.1242/jeb.133652 . ISSN   1477-9145. PMID   26994176. S2CID   6829850.
  53. 1 2 Lobato-Rios, Victor; Ramalingasetty, Shravan Tata; Özdil, Pembe Gizem; Arreguit, Jonathan; Ijspeert, Auke Jan; Ramdya, Pavan (May 2022). "NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster". Nature Methods. 19 (5): 620–627. doi:10.1038/s41592-022-01466-7. ISSN   1548-7105. PMID   35545713. S2CID   233328653.
  54. ↑ Charles, James P.; Cappellari, Ornella; Spence, Andrew J.; Wells, Dominic J.; Hutchinson, John R. (October 2016). "Muscle moment arms and sensitivity analysis of a mouse hindlimb musculoskeletal model". Journal of Anatomy. 229 (4): 514–535. doi:10.1111/joa.12461. ISSN   0021-8782. PMC   5013061 . PMID   27173448.
  55. ↑ Siebert, T.; Rode, C. (2014-01-01). "Computational modeling of muscle biomechanics". Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System: 173–204. doi:10.1533/9780857096739.2.173. ISBN   9780857096616.
  56. ↑ Seth, Ajay; Hicks, Jennifer L.; Uchida, Thomas K.; Habib, Ayman; Dembia, Christopher L.; Dunne, James J.; Ong, Carmichael F.; DeMers, Matthew S.; Rajagopal, Apoorva; Millard, Matthew; Hamner, Samuel R.; Arnold, Edith M.; Yong, Jennifer R.; Lakshmikanth, Shrinidhi K.; Sherman, Michael A. (2018-07-26). "OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement". PLOS Computational Biology. 14 (7): e1006223. Bibcode:2018PLSCB..14E6223S. doi: 10.1371/journal.pcbi.1006223 . ISSN   1553-7358. PMC   6061994 . PMID   30048444.
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