Single Task Optimization-Based Planar Box Delivery Motion Simulation and Experimental Validation

Xiang, Yujiang; Tahmid, Shadman; Owens, Paul; Yang, James

doi:10.1115/1.4049647

Cited by 3 publications

(2 citation statements)

References 33 publications

(36 reference statements)

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“…It can be understood that the traditional arti cial sh swarm algorithm strengthens the aggregation of sh swarms but reduces the role of individual sh in the swarm. e free swimming of the sh will lead to a larger range of results in the subsequent algorithm, which in turn a ects the foraging behavior of the sh [21]. When seeking the optimal solution, it enters a local circle, which is not perfect enough.…”

Section: Arti Cial Fish Swarm Algorithm Chinese Researchers Proposed ...mentioning

confidence: 99%

Deconstruction and Realization of Sports Entity Simulation Based on Fish Swarm Algorithm

2022

Computational Intelligence and Neuroscience

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Due to the limitation of sports movement, the current simulation technology of sports entities is prone to deficiencies in capturing dynamic motion figures and is prone to lack of accuracy. It is also affected by external noise and brightness. To solve these problems, this paper proposes a sports entity simulation based on the fish swarm algorithm and compares the figure effectiveness, figure segmentation, core point, and noise reduction effect of the two in the shooting figure. Through the comparison, it is found that the figure is more appropriate to the real moving figure, the motion capture is more accurate, and the number of core points is related to the accuracy of motion capture. The more core points, the more accurate the motion capture, and the noise reduction effect is also increased by 20.3%, which reduces the impact of brightness on the motion simulation. The difference in the effect of the traditional simulation technology (particle swarm algorithm) and the entity simulation based on the fish swarm algorithm was also compared. The combination with the artificial fish swarm algorithm is to simulate the moving entity and learn from some reference data. By comparing the data between the two after the experiment, it is concluded that the fish swarm algorithm is more effective in the simulation of sports entities.

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Section: Arti Cial Fish Swarm Algorithm Chinese Researchers Proposed ...mentioning

confidence: 99%

Deconstruction and Realization of Sports Entity Simulation Based on Fish Swarm Algorithm

2022

Computational Intelligence and Neuroscience

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“…The motion capture system was comprised of 8 Eagle-4 cameras with 10 retroreflective markers placed on the right arm during each task as shown in Figure 3 (Motion Analysis Corporation, California, USA). Details on the motion capture environment are explained in [30,31]. Analog EMG signals were recorded with Trigno Wireless Biofeedback System (DELSYS Incorporated, Massachusetts, USA) containing 16 surface EMG sensors.…”

Section: Experimental Data Collectionmentioning

confidence: 99%

Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model

Tahmid

Font-Llagunes

Yang

2022

Journal of Computing and Information Science in Engineering

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Cerebrovascular accidents like a stroke can affect lower limb as well as upper extremity joints (i.e., shoulder, elbow or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles ability to generate forces reduces, thus affecting the joints torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion-extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results.

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Optimization-based two-dimensional symmetric tossing motion prediction and validation

Lee,

Yang

2024

Proc Inst Mech Eng H

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Human motion has been analyzed for decades based on experimentally collected subject data, serving various purposes, from enhancing athletic performance to assisting patients’ recovery in rehabilitation and many individuals can benefit significantly from study advancements. Human motion prediction, is a more challenging task because no experimental data are available in advance, particularly concerning repetitive tasks, such as box lifting and tossing, to prevent injury risks. Tossing, a common task in various industries, involves the simultaneous vertical and horizontal movement of objects but often results in bodily strain. This paper presents an optimization-based method for predicting two-dimensional (2D) symmetric tossing motion without relying on experimental data. The method employs sequential quadratic programming, which optimizes dynamic effort by incorporating both static and dynamic joint torque limits. To validate the proposed model, experimental data were collected from 10 subjects performing tossing tasks using a motion capture system and force plates. The predicted joint angles and ground reaction forces considering dynamic joint strength constraints were compared with their corresponding experimental data to validate the model. In addition, the predicted joint torques differences are compared between joint dynamics strengths and static strengths. The results showed that the predicted optimal tossing motions range between the maximum and minimum of the experimental standard deviation for kinematic data across all subjects and the ground reaction forces are also within the experimental data range. This supports the validity of the prediction model. The findings of this study could have practical applications, especially in preventing the potential risk of injuries among workers who have daily tossing jobs.

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Single Task Optimization-Based Planar Box Delivery Motion Simulation and Experimental Validation

Cited by 3 publications

References 33 publications

Deconstruction and Realization of Sports Entity Simulation Based on Fish Swarm Algorithm

Deconstruction and Realization of Sports Entity Simulation Based on Fish Swarm Algorithm

Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model

Optimization-based two-dimensional symmetric tossing motion prediction and validation

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