Dynamics Of Human Motion Research Articles

Human Motion Analysis from Inertial Sensor Data Based on Nonlinear Dynamics

AbstractThis paper discusses the problem of human motion analysis from inertial sensor (accelerometer and gyroscope) data, which is the time sequence data obtained from human motion. The inertial sensors are widely used in the wearable sensing field for understanding human activities. Human motion can be divided into simple movements, such as swinging the arms or legs. These simple movements are fundamentally periodic, and they can be modeled with dynamical systems having periodic attractors. On the other hand, more complex motion like walking can be represented as a sequence of simple movements.To address the problem of analyzing human motion data, we propose a new framework based on a dynamical system. The human motions observed by the inertial sensors are divided into simple movements, and they can be often described as periodical signals. The periodical signals are described by a dynamical system, which can store the periodical or transitional trajectory in a state space by using basin of attraction. The attractors abstract a kind of proto-symbol. The dynamical system having periodical attractors is shown to characterize human motion effectively by exploiting spatiotemporal continuity, because it describes the flow of its transitions into the state space. Moreover, in the designed symbol space from the attractors, the information of human motion dynamics is described in the placement of the points. Thus, each point or transition between points in the symbol space corresponds to a specific type of motion; it can act as a notation method of motion characteristics.

Dynamics of human movement

The part of (bio)mechanics that studies the interaction of forces on the human skeletal system and its effect on the resulting movement is called rigid body dynamics. Some basic concepts are presented: A mathematical formulation to describe human movement and how this relates on the mechanical loads acting on the skeletal system. These equations of motion depend on the mechanical properties of the skeletal system, such as dimensions and mass distribution. It is applied to describe and analyze human gait.

A Computational Predictor of Human Episodic Memory Based on a Theta Phase Precession Network

In the rodent hippocampus, a phase precession phenomena of place cell firing with the local field potential (LFP) theta is called “theta phase precession” and is considered to contribute to memory formation with spike time dependent plasticity (STDP). On the other hand, in the primate hippocampus, the existence of theta phase precession is unclear. Our computational studies have demonstrated that theta phase precession dynamics could contribute to primate–hippocampal dependent memory formation, such as object–place association memory. In this paper, we evaluate human theta phase precession by using a theory–experiment combined analysis. Human memory recall of object–place associations was analyzed by an individual hippocampal network simulated by theta phase precession dynamics of human eye movement and EEG data during memory encoding. It was found that the computational recall of the resultant network is significantly correlated with human memory recall performance, while other computational predictors without theta phase precession are not significantly correlated with subsequent memory recall. Moreover the correlation is larger than the correlation between human recall and traditional experimental predictors. These results indicate that theta phase precession dynamics are necessary for the better prediction of human recall performance with eye movement and EEG data. In this analysis, theta phase precession dynamics appear useful for the extraction of memory-dependent components from the spatio–temporal pattern of eye movement and EEG data as an associative network. Theta phase precession may be a common neural dynamic between rodents and humans for the formation of environmental memories.

INTERACTIVE HUMAN POSE AND ACTION RECOGNITION USING DYNAMICAL MOTION PRIMITIVES

There is currently a division between real-world human performance and the decision making of socially interactive robots. This circumstance is partially due to the difficulty in estimating human cues, such as pose and gesture, from robot sensing. Towards bridging this division, we present a method for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies. Our notion of a motion vocabulary is comprised of movement primitives that structure a human's action space for decision making and predict human movement dynamics. Through prediction, such primitives can be used to both generate motor commands for specific actions and perceive humans performing those actions. In this paper, we focus specifically on the perception of human pose and performed actions using a known vocabulary of primitives. Given image observations over time, each primitive infers pose independently using its expected dynamics in the context of a particle filter. Pose estimates from a set of primitives inferencing in parallel are arbitrated to estimate the action being performed. The efficacy of our approach is demonstrated through interactive-time pose and action recognition over extended motion trials. Results evidence our approach requires small numbers of particles for tracking, is robust to unsegmented multi-action movement, movement speed, camera viewpoint and is able to recover from occlusions.

Viewpoint invariant exemplar-based 3D human tracking

This paper proposes a clustered exemplar-based model for performing viewpoint invariant tracking of the 3D motion of a human subject from a single camera. Each exemplar is associated with multiple view visual information of a person and the corresponding 3D skeletal pose. The visual information takes the form of contours obtained from different viewpoints around the subject. The inclusion of multi-view information is important for two reasons: viewpoint invariance; and generalisation to novel motions. Visual tracking of human motion is performed using a particle filter coupled to the dynamics of human movement represented by the exemplar-based model. Dynamics are modelled by clustering 3D skeletal motions with similar movement and encoding the flow both within and between clusters. Results of single view tracking demonstrate that the exemplar-based models incorporating dynamics generalise to viewpoint invariant tracking of novel movements.

Santos: A New Kind of Virtual Human

Substantial effort has been directed towards a variety of aspects concerning virtual prototyping and analysis, with significant advances in the fields of computer aided design (CAD), finite element analysis (FEA), dynamic analysis, etc. However, less attention has been paid to developing virtual humans that interact with and evaluate virtual prototypes. Ideally, such avatars would serve as design aids that actually work with and provide feedback for engineers. In addition, they would provide a tool for studying the human body. SantosTM is a new kind of complete whole-body virtual human that is responding to these needs, through the efforts of the Virtual Soldier Research (VSR) lab at The University of Iowa. SantosTM combines a highly realistic appearance, real-time simulations, a high number of degrees of freedom (a relatively complex musculoskeletal structure), and a new direct optimization-based approach that yields predictive autonomy and a robust platform for extensive simulation and ergonomic-analysis capabilities. How this virtual human is modeled will be discussed, and our approach to kinematic and dynamic human posture and motion prediction will be presented. In this capacity, the advantages of the direct optimization-based approach are detailed and a palette of capabilities is presented. In addition to the fundamentals of our methods for human motion simulation, an overview of additional projects at VSR is given, touching on hand modeling, clothing modeling, simulation of physiological indices, and a few different efforts concerning muscle modeling.

Prediction and analysis of human motion dynamics performing various tasks

Several digital human softwares have shown the capabilities of simulating simple reach motions. However, predicting the dynamic effects on human motion due to different task loads is still immature. This paper presents an optimisation-based algorithm for simulating the dynamic motion of a digital human. The hypothesis is that human performance measures such as the total energy consumption governs human motion; thus the process of human motion simulation can be formulated as an optimisation problem that minimises human performance measures given at different constraints and hand loads, corresponding to a number of tasks. General equations of motion using Lagrangian dynamics method are derived for the digital human, and human metabolic energy is formulated in terms of joint space. Joint actuator torques and metabolic energy expenditure during motion are formulated and calculated within the algorithm, and it is applied to Santos™, a kinematically realistic digital human, developed at the University of Iowa. Results show that different external loads and tasks lead to different human motions and actuator torque distributions.

Modeling kinematics and dynamics of human arm movements.

A central problem in motor control relates to the coordination of the arm's many degrees of freedom. This problem concerns the many arm postures (kinematics) that correspond to the same hand position in space and the movement trajectories between begin and end position (dynamics) that result in the same arm postures. The aim of this study was to compare the predictions for arm kinematics by various models on human motor control with experimental data and to study the relation between kinematics and dynamics. Goal-directed arm movements were measured in 3-D space toward far and near targets. The results demonstrate that arm postures for a particular target depend on previous arm postures, contradicting Donders's law. The minimum-work and minimum-torque-change models, on the other hand, predict a much larger effect of initial posture than observed. These data suggest that both kinematics and dynamics affect postures and that their relative contribution might depend on instruction and task complexity.

Initiation of the human heave linear vestibulo-ocular reflex.

The linear vestibulo-ocular reflex (LVOR) was studied in eight normal human subjects of average age 24+/-5 years. Subjects underwent a sudden heave (mediolateral) translation delivered by a pneumatic servo-driven chair with a peak acceleration of 0.5 g while viewing earth-fixed targets at 15, 25, 50, and 200 cm. Stimuli were provided both with targets continuously visible or extinguished just prior to motion. Cancellation was tested using chair-fixed targets at each viewing distance. Eye movements were recorded using binocular magnetic search coils. Head translation was measured using a linear accelerometer attached to the upper teeth, to which also was attached a magnetic search coil verifying absence of head rotation. Vergence angles achieved by all subjects were appropriate to interpupillary distance and target distance. Heave translations evoked horizontal ocular rotations in the opposite direction following a brief latency. Latency of the LVOR was determined by automated algorithms based on identification of times when eye position and head acceleration exceeded three standard deviations (SDs) of baseline noise, and was corrected for differing transducer delays. Mean LVOR latency was 30+/-16 ms (mean +/- SD), range 12-53 ms. Slow phase LVOR amplitude was greater for near and less for more distant targets, although all observed responses were suboptimal. Measured 100 ms after head translation onset, mean response was 20% of ideal for the target at 15 cm, 22% at 25 cm, 31% at 50 cm, and 53% at 200 cm. Mean latency was significantly longer than the previously reported values for both the human angular VOR and the monkey LVOR, and had significant inverse correlation with response magnitude. The relatively longer latency of the human LVOR than angular VOR may be tailored to match human head movement dynamics.

Local Adaptive Subspace Regression

Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as biological movement systems. So far, due to sparsity of data in high dimensional spaces, learning in such settings required a significant amount of prior knowledge about the learning task, usually provided by a human expert. In this paper we suggest a partial revision of the view. Based on empirical studies, we observed that, despite being globally high dimensional and sparse, data distributions from physical movement systems are locally low dimensional and dense. Under this assumption, we derive a learning algorithm, Locally Adaptive Subspace Regression, that exploits this property by combining a dynamically growing local dimensionality reduction technique as a preprocessing step with a nonparametric learning technique, locally weighted regression, that also learns the region of validity of the regression. The usefulness of the algorithm and the validity of its assumptions are illustrated for a synthetic data set, and for data of the inverse dynamics of human arm movements and an actual 7 degree-of-freedom anthropomorphic robot arm.

Chaos in Human Rhythmic Movement

Rhythmic movements typical of locomotory actions are usually modeled as limit cycle dynamics, and their deviations from pure periodicity are attributed to stochastic physiological noise. In the present study, the dynamics of human rhythmic movements were found to contain more than the 2 dynamically active variables expected from limit cycle dynamics; the number depended upon the size of the limb oscillator. Observed positive Lyapunov exponents and fractal attractor dimensions indicated that the gross variability of human rhythmic movements may stem largely from low-dimensional chaotic motion on strange attractors.

Offset dynamics of human smooth pursuit eye movements: Effects of target presence and subject attention

Subjects made smooth pursuit eye movements with a target moving horizontally at 15 deg/sec. At a specified location the target either: (1) suddenly vanished; or (2) jumped to the fovea with target retinal velocity and feedback becoming 0 (target stabilized at the fovea). In each type of trial, the subjects either: “looked” at the target, “pushed” the target, or “passively” gazed. When the target vanished, eye velocity decreased exponentially with a short time-constant (τ ≈ 0.10 sec), regardless of whether the subjects were “looking,” “pushing” or “passively” gazing. However, some subjects while “pushing” (using an imaginary target) did generate low velocity smooth movement (1–2.5 deg/sec) late in the offset. When the target was stabilized at the fovea, eye velocity also decreased, but with a relatively long time-constant (τ = 0.4–0.8 sec). The time-constant was the same with both “looking,” and “pushing”, but was shorter for some subjects with “passive” gazing (τ = 0.1–0.5 sec). These findings show that smooth pursuit offset is influenced by the presence of a target, but is relatively independent of attentional mode. All of the pursuit offset responses can be simulated using a model of the pursuit system with target velocity and position inputs, and an internal positive feedback loop enabled by target presence.

Biomechanics of manual material handling through simulation: Computational aspects

Computerized human motion simulation allows generation of dynamic human motions on computers. Biomechanical stresses can be estimated using the motions generated on a computer without actually collecting joint coordinate data. A two-dimensional whole-body lifting simulation model is presented in this paper. The model assumes that humans perform lifting activities based on minimization of physical work, subject to various constraints. The simulation method contains three major computation units: trajectory formation unit, dynamics of motion unit, and nonlinear optimization unit. The trajectory formation unit generates smooth polynomials representing motion characteristics of human lifting. Kinematics and kinetics are calculated in the dynamics unit. Objective and constraint functions are evaluated in the optimization unit. Optimal motions are generated by minimizing the objective function, subject to the constraints. Computation methods of the three units and simulation results are presented.

Manipulating symmetry in the coordination dynamics of human movement.

Manipulating symmetry in the coordination dynamics of human movement.

動画像を用いた人体の動作解析へのビデオセオドライトの利用に関する研究

The video imagery gives important information to the dynamic analysis of human motion in the field of sports training or rehabilitation. For understanding the dynamics of human motion from TV or video image sequences, however, there are two complicated subjects. One is an image processing, for example, automated recognition of human's feature points such as head, elbow, knee etc.. The second is, how to estimate camera orientation parameters in the photogrammetry.The authors analyzed the sports dynamics, e. g. Carl Lewis and Boat Rowing using TV images. In these cases, the camera orientation were achieved by utilizing special information such as goal line, course lines and buoys which marks course line. However, the camera orientation for images without any information became important subjects. If CCD camera is mounted on the theodolite, ω andκcan be acquird as a vertical and horizontal angle, and φis regarded as 0 degree because the theodolite is leveled. Furthermore, these rotation parameters can be recorded as a image data by means of superimpose. With this in mind, the authors developed the video theodolite system which consist of CCD camera, theodolite and video recorder to estimate camera rotation parameters with real time and to record image data. This paper presents the application of video theodolite system for the dynamic analysis of human motion with sequential images.

人体の動作解析に対するビデオセオドライトの利用

Video image sequences often give important information about the dynamics of human motion in the field of sports, training or rehabilitation. In understanding of the dynamics of human motion from general TV or video image sequences, there are two complicated subjects. One is image processing, for example automated recognition of features on the human body, such as e.g. the head, elbows or knees. The second is how to estimate the photogrammetric camera orientation parameters on a moving camera.This paper describes the development of a video theodolite system, consisting of a CCD camera, a theodolite and a video recorder. The system makes it possible to record image data with a moving camera and simultaneously determine the camera rotation parameters in real time.The authors first analyzed the dynamics of the sprinter Carl Lewis and that of boat rowing by using TV images. In both these cases however, the camera orientation parameters could be determined by utilizing fixed information in the images such as the goal line and course lines in the first case, and the buoys marking the rowing course line in the second case. But often, there exist no such fixed information in the images why the authors concentrated on developing a system where the camera orientation parameters could be determined in real time while recording a moving object.In order to measure the rotation parameters, a CCD camera was mounted on top of a theodolite.ωand κwere defined as the horizontal and vertical angles respectively, φalways being 0 degrees as the theodolite was levelled. The current values of the parameters were then superimposed continuously on the image frames and thus recorded as a part of the image data.

A procedure to validate three-dimensional motion assessment systems

The automation provided by computer-assisted motion-tracking systems allows for three-dimensional motion and force analysis. These systems combined with mathematical modelling are able to analyse quickly the intricate dynamics of human movement. Understanding the limitations of human motion analysis as performed by the present measurement techniques is essential for proper application of the results. It is necessary to validate the analysis system prior to subject testing. This paper provides a validation of an optoelectric motion-tracking system used in a dynamic knee assessment study. While the validation is shown with one particular system only, it is suggested that all systems used in two- or three-dimensional motion analysis should be tested similarly in the actual configuration used. Three simple mechanical representations of the human knee have been used in this validation. The first model provided an understanding of the source and behaviour of the error introduced to the accuracy of defining a vector between the recorded coordinates of two markers. The other two models investigated the effect of processing methods specific to the knee analysis project. Separating the markers by at least 180 mm is recommended to produce stable vectors. Relative joint angles could be calculated in all three planes of rotation. The error in calculating flexion and longitudinal rotation was less than 2.0°, while calculating adduction introduced errors of 4.0°. Force calculations were found to be within 8%. The system behaviour was found to be consistent within the calibrated volume about the force platform. Simple mechanical models combined with straightforward procedures can provide validation in terms of clinically relevant parameters.

ビデオ画像を用いたボート漕法の動的解析

The video imagery gives important information to the dynamic analysis of human motion in the field of sports training or rehabilitation. For understanding the dynamics of human motion from image sequences, however, there are two complicated subjects. One is an image processing, for example, automated recognition of human's feature points such as head, shoulder, elbow, knee etc.. The second is, how to estimate the camera parameter in the photogrammetry.This paper presents a method to estimate the camera parameter, and shows the results of dynamic analysis in the case of boat rowing. In this study, the bow and no. 2, gold medalists in the Canadian eight team on the Barcelona olympic games at 1992 were selected.

The dynamics of human fetal breathing movements; a multiscan echofetographic approach

Abstract Fetal breathing movements are characterized by retractions of the thoracic cage, an increase in the thoracic kyphosis and an expansion of the abdominal wall during each such thoracic movement. These findings can be explained by assuming that the movements of the thoracic cage and abdominal wall are initiated by contractions of the diaphragm itself, as well as the intrathoracic and intraabdominal pressure changes belonging to them. When an echographic technique is used, a positive identification of the start of each inspiratory phase will only be possible if these breathing movements can be clearly differentiated.

Dynamics of Human Motion

Journal Article Dynamics of Human Motion Get access Physical Therapy, Volume 42, Issue 1, January 1962, Page 41, https://doi.org/10.1093/ptj/42.1.41 Published: 01 January 1962