6,241 research outputs found
Dynamics simulation of human box delivering task
Thesis (M.S.) University of Alaska Fairbanks, 2018The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.Chapter 1 Introduction -- Chapter 2 Skeletal Human Modeling -- Chapter 3 Kinematics and Dynamics -- Chapter 4 Lifting Simulation -- Chapter 5 Carrying Simulation -- Chapter 6 Delivering Simulation -- Chapter 7 Conclusion and Future Research -- Reference
Estimation of Human Body Shape and Posture Under Clothing
Estimating the body shape and posture of a dressed human subject in motion
represented as a sequence of (possibly incomplete) 3D meshes is important for
virtual change rooms and security. To solve this problem, statistical shape
spaces encoding human body shape and posture variations are commonly used to
constrain the search space for the shape estimate. In this work, we propose a
novel method that uses a posture-invariant shape space to model body shape
variation combined with a skeleton-based deformation to model posture
variation. Our method can estimate the body shape and posture of both static
scans and motion sequences of dressed human body scans. In case of motion
sequences, our method takes advantage of motion cues to solve for a single body
shape estimate along with a sequence of posture estimates. We apply our
approach to both static scans and motion sequences and demonstrate that using
our method, higher fitting accuracy is achieved than when using a variant of
the popular SCAPE model as statistical model.Comment: 23 pages, 11 figure
Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.
Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations
Working Posture Evaluation of Clinical Student in Faculty of Dentistry University of Indonesia for the Scaling Task in Sitting Position in a Virtual Environment
Musculoskeletal disorders (MSDs) are global issues in the dental profession. This research evaluated the MSDs risk caused by the sitting working posture of clinical students performing the task of scaling. The evaluation using the virtual environment approach shows risk of MSDs in the students’ upper extremities such as neck, shoulder, and trunk. Further simulation based on the ideal sitting working posture shows that ergonomic scaling could be achieved when the patient sits at a 15° angle. When scaling the 1st and 4th quadrant of the teeth, the 9 o’clock position is used. Hence, the 11 o’clock position is used when scaling the 2nd and 3rd quadrant
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
Experimental optimization of exposure index and quality of service in WLAN networks
This paper presents the first real-life optimization of the Exposure Index (EI). A genetic optimization algorithm is developed and applied to three real-life Wireless Local Area Network scenarios in an experimental testbed. The optimization accounts for downlink, uplink and uplink of other users, for realistic duty cycles, and ensures a sufficient Quality of Service to all users. EI reductions up to 97.5% compared to a reference configuration can be achieved in a downlink-only scenario, in combination with an improved Quality of Service. Due to the dominance of uplink exposure and the lack of WiFi power control, no optimizations are possible in scenarios that also consider uplink traffic. However, future deployments that do implement WiFi power control can be successfully optimized, with EI reductions up to 86% compared to a reference configuration and an EI that is 278 times lower than optimized configurations under the absence of power control
Multi-Objective Optimisation Method for Posture Prediction and Analysis with Consideration of Fatigue Effect and its Application Case
Automation technique has been widely used in manufacturing industry, but
there are still manual handling operations required in assembly and maintenance
work in industry. Inappropriate posture and physical fatigue might result in
musculoskeletal disorders (MSDs) in such physical jobs. In ergonomics and
occupational biomechanics, virtual human modelling techniques have been
employed to design and optimize the manual operations in design stage so as to
avoid or decrease potential MSD risks. In these methods, physical fatigue is
only considered as minimizing the muscle or joint stress, and the fatigue
effect along time for the posture is not considered enough. In this study,
based on the existing methods and multiple objective optimisation method (MOO),
a new posture prediction and analysis method is proposed for predicting the
optimal posture and evaluating the physical fatigue in the manual handling
operation. The posture prediction and analysis problem is mathematically
described and a special application case is demonstrated for analyzing a
drilling assembly operation in European Aeronautic Defence & Space Company
(EADS) in this paper
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on
Robotics and Automation (ICRA
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