Relevant bibliographies by topics / Joint torques estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Joint torques estimation'

Author: Grafiati
Published: 15 February 2025

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Journal articles on the topic "Joint torques estimation"

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Yousefizadeh, Shirin, and Thomas Bak. "Unknown External Force Estimation and Collision Detection for a Cooperative Robot." Robotica 38, no. 9 (December 20, 2019): 1665–81. http://dx.doi.org/10.1017/s0263574719001681.

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SUMMARYIn human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to compute the net torques at the joints. The considered observers are extended Kalman filter (EKF) and nonlinear disturbance observer (NDOB). Utilizing the computed torque obtained based on the loadcells measurements and the observer, the estimates of external torques applied on the robot end-effector can be achieved. Moreover, to improve the degree of safety, an algorithm is proposed to distinguish between intentional contact force from an operator and accidental collisions. The proposed algorithms are demonstrated on a robot, namely WallMoBot, which is designed to help the operator to install heavy glass panels. Simulation results and preliminary experimental results are presented to demonstrate the effectiveness of the proposed methods in estimating the joint space torques generated by the external forces applied to the WallMoBot end-effector and to distinguish between the user-input force and accidental collisions.
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Imamura, Yumeko, Ko Ayusawa, Eiichi Yoshida, and Takayuki Tanaka. "Evaluation Framework for Passive Assistive Device Based on Humanoid Experiments." International Journal of Humanoid Robotics 15, no. 03 (June 2018): 1750026. http://dx.doi.org/10.1142/s0219843617500268.

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This study presents an enhanced framework for evaluating an assistive effect generated by a passive assistive device using a humanoid robot. The humanoid robotic experiments can evaluate wearable devices by measuring the joint torque, which cannot be measured directly from the human body. In this paper, we introduce an “assistive torque estimation map” as an efficient means for estimating the supportive torque within the range of motions by interpolating the measured joint torques and joint angles of the robot. This map aims to estimate the supportive torques for complex motions without conducting humanoid experiments or human-subject experiments with these motions. We generated an estimation map for an actual assistive suit that decreases the load on the lumbar region and we verified the validity of the proposed method by experimentation. In addition, the geometric simulation model of the assistive suit was validated based on the proposed experiments by using the humanoid robot HRP-4. The proposed framework is expected to lead to an efficient design of such assistive devices so that fewer human-subject experiments need to be conducted.
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Lam, Shui Kan, and Ivan Vujaklija. "Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study." Sensors 21, no. 19 (October 2, 2021): 6597. http://dx.doi.org/10.3390/s21196597.

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Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.
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Kuo, A. D. "A Least-Squares Estimation Approach to Improving the Precision of Inverse Dynamics Computations." Journal of Biomechanical Engineering 120, no. 1 (February 1, 1998): 148–59. http://dx.doi.org/10.1115/1.2834295.

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A least-squares approach to computing inverse dynamics is proposed. The method utilizes equations of motion for a multi-segment body, incorporating terms for ground reaction forces and torques. The resulting system is overdetermined at each point in time, because kinematic and force measurements outnumber unknown torques, and may be solved using weighted least squares to yield estimates of the joint torques and joint angular accelerations that best match measured data. An error analysis makes it possible to predict error magnitudes for both conventional and least-squares methods. A modification of the method also makes it possible to reject constant biases such as those arising from misalignment of force plate and kinematic measurement reference frames. A benchmark case is presented, which demonstrates reductions in joint torque errors on the order of 30 percent compared to the conventional Newton–Euler method, for a wide range of noise levels on measured data. The advantages over the Newton–Euler method include making best use of all available measurements, ability to function when less than a full complement of ground reaction forces is measured, suppression of residual torques acting on the top-most body segment, and the rejection of constant biases in data.
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Latella, Claudia, Silvio Traversaro, Diego Ferigo, Yeshasvi Tirupachuri, Lorenzo Rapetti, Francisco Javier Andrade Chavez, Francesco Nori, and Daniele Pucci. "Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques." Sensors 19, no. 12 (June 21, 2019): 2794. http://dx.doi.org/10.3390/s19122794.

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The paper presents a stochastic methodology for the simultaneous floating-base estimation of the human whole-body kinematics and dynamics (i.e., joint torques, internal and external forces). The paper builds upon our former work where a fixed-base formulation had been developed for the human estimation problem. The presented approach is validated by presenting experimental results of a health subject equipped with a wearable motion tracking system and a pair of shoes sensorized with force/torque sensors while performing different motion tasks, e.g., walking on a treadmill. The results show that joint torque estimates obtained by using floating-base and fixed-base approaches match satisfactorily, thus validating the present approach.
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Petró, Bálint, and Rita M. Kiss. "Validation of the Estimated Torques of an Open-chain Kinematic Model of the Human Body." Periodica Polytechnica Mechanical Engineering 66, no. 2 (March 22, 2022): 175–82. http://dx.doi.org/10.3311/ppme.19920.

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The standing human body is frequently modeled as an inverted double pendulum restricted to a single plane. In order to capture the coordination efforts and interplay between spatial dimensions, the model has to capture motion and joint torques in all spatial dimensions. Our two-segment model covers two degrees of freedom (ML and AP revolutions) at the ankle and the hip level and utilizes the Denavit-Hartenberg convention. This work aimed to validate the model's torque estimation on a diverse group of participants (11 women, 22–56 years, 11 men, 22–61 years). The inverse dynamic calculations provide estimated joint torques for a motion capture recorded trial, while standing on a force platform enables the indirect measurement of ankle torques. A 60-second-long visually guided balancing task was recorded and repeated three times. The estimated and the indirectly measured torques were compared, and offset and variance type errors ( normalized RMSE and R2 ) were analyzed. The R2-values were excellent (R2 > 0.90) 64 out of the 66 cases (97%) for AP torques and 58 out of the 66 cases (88%) for ML torques. Normalized RMSE values were dominantly under the 0.35 value with some outliers. RMSE showed no evident connection with age, body height, body mass, or BMI. An open-chain kinematic model with two segments, following the Denavit-Hartenberg convention, is well suited to estimate the control torque traces of the human body during standing balancing and needs only three tracked positions.
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Ajayi, Michael Oluwatosin, Karim Djouani, and Yskandar Hamam. "Bounded Control of an Actuated Lower-Limb Exoskeleton." Journal of Robotics 2017 (2017): 1–20. http://dx.doi.org/10.1155/2017/2423643.

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A bounded control strategy is employed for the rehabilitation and assistance of a patient with lower-limb disorder. Complete and partial lower-limb motor function disorders are considered. This application is centered on the knee and the ankle joint level, thereby considering a user in a sitting position. A high gain observer is used in the estimation of the angular position and angular velocities which is then applied to the estimation of the joint torques. The level of human contribution is feedback of a fraction of the estimated joint torque. This is utilised in order to meet the demands for a bounded human torque; that is, τh≤N2,n≤N1,n. The asymptotic stability of the bounded control law without human contribution and the convergence analysis of the high gain observer is verified using Lyapunov-based analysis. Simulations are performed to verify the proposed control law. Results obtained guarantee a fair trajectory tracking of the physiotherapist trajectory.
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Haraguchi, Naoto, and Kazunori Hase. "Multibody Model with Foot-Deformation Approach for Estimating Ground Reaction Forces and Moments and Joint Torques during Level Walking through Optical Motion Capture without Optimization Techniques." Sensors 24, no. 9 (April 27, 2024): 2792. http://dx.doi.org/10.3390/s24092792.

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The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings.
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Muhammad Isa, Munawwarah Solihah, Nurhidayah Omar, Mohammad Shahril Salim, Saidatul Ardeenawatie Awang, and Suhizaz Sudin. "Dynamic Modelling of the Spine for the Estimation of Vertebral Joint Torques using Gordon’s Method." Journal of Advanced Research in Applied Mechanics 125, no. 1 (October 2, 2024): 42–57. http://dx.doi.org/10.37934/aram.125.1.4257.

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World Health Organization (WHO) recognised musculoskeletal disorder (MSD) as the main contributor to disability worldwide, with low back pain as the major disorder globally. The occupational disorder normally occurs during lifting. The weight of the load and manual handling tasks during lifting has an impact on the spine and joint torque. The purpose of this study is to propose a dynamic model of the spine that can estimate the vertebral joint torques. This study is a bimodal approach that consists of the experimental and theoretical parts. Ten healthy UniMAP students (10 males) participated in this study. The subjects were required to lift a 3kg weight plate for kinematics and EMG data collection. Retro-reflective markers were attached to the subject body, and then, the data was collected and stored in QTM software. Kinematic data was processed using C-Motion Visual3D. Eight Trigno Wireless Sensors were attached on the back muscles (left and right erector spinae, latissimus dorsi, external oblique and internal oblique). The EMG data were stored in EMG Acquisition software and subsequently, were processed using EMG Analysis software. Gordon’s method was used to develop a mathematical model of the spine. The model comprises of five kinematic chains which connected three lumbar, two thoracic and one cervical. The model calculated the value of joint torque on flexion/extension movement using Matlab and Microsoft Excel. When calculated on L5, the model gives an estimation within 0 – 30 kgm2s-2. The model was further used to estimate value of L3, L1, MAI and T2. The estimate average value of joint torque at L3 is within 5 – 25 kgm2s-2, MAI is within 0 – 6 kgm2s-2 and T2 is within 0 – 1 kgm2s-2. The average RMS values show the highest muscle activity on the right internal oblique muscle (1519 µV), followed by the right external oblique (1166 µV) and left external oblique (418 µV). The results obtained gives an insight on the value of joint torque that have been applied by the spine and the most activated back muscles during lifting.
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Lim, T. G., H. S. Cho, and W. K. Chung. "A parameter identification method for robot dynamic models using a balancing mechanism." Robotica 7, no. 4 (October 1989): 327–37. http://dx.doi.org/10.1017/s026357470000672x.

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SUMMARYAccurate modeling of robot dynamics is a prerequisite for the design of model-based control schemes and enhancement of the performance of the robot. The dynamic parameters associated with a pseudo-inertia matrix are often difficult to identify accurately because the inertia torques are small in comparison to gravity loadings, thus creating signal processing problem. The identification method presented in this paper utilizes a balancing mechanism which increases the estimation accuracy of the dynamic parameters. The balancing mechanism has the effect of amplifying the inertia-related torque signal by eliminating gravity loadings acting on the robot joints. A series of motion data were experimentally obtained through sequential test steps. By incorporating the measured information about joint torques, angular positions, velocities and accelerations the least square algorithm was used to identify the dynamic parameters. The estimated values were converted to those of the original robot model to obtain its dynamic model parameters. The identified robot dynamic model was shown to be accurate enough to predict the actual robot motions.
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Dissertations / Theses on the topic "Joint torques estimation"

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Ouadoudi, Belabzioui Hasnaa. "Contributions to the in-situ biomechanical and physical ergonomic analysis of workstations using machine learning and deep learning techniques." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENE005.

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L’évaluation du risque de troubles musculosquelettiques en milieu industriel représente un défi en raison de la complexité des processus de fabrication modernes. Ces environnements comprennent divers facteurs influençant l’activité des opérateurs, tels que les éléments organisationnels, managériaux et environnementaux, ainsi que le rythme de travail. Il est crucial d’évaluer les contraintes physiques auxquelles sont soumis les opérateurs pour prévenir ces troubles. Bien que de nombreux systèmes monitorent actuellement les mouvements des opérateurs et évaluent les contraintes posturales pour fournir un aperçu de l’activité physique, ils échouent souvent à analyser les forces physiques subies ou générées par l’opérateur. Par conséquent, il est essentiel de quantifier ces forces afin d’identifier les facteurs de risque physique liés à l’effort. Cependant, les méthodes classiques de mesure impliquent souvent des processus complexes, invasifs et peu pratiques en milieu industriel. Cette thèse relève ces défis en évaluant des approches d’apprentissage pour estimer les contraintes physiques sans recourir à des mesures invasives, ce qui est fondamental pour améliorer les outils et les pratiques ergonomiques. Nous avons commencé par comparer la précision et la robustesse des systèmes de mesure basés sur la vision par ordinateur pour l’évaluation du RULA, en nous focalisant particulièrement sur les évaluations ergonomiques sur site. Notre analyse s’est principalement concentrée sur l’évaluation des systèmes basés sur la vision par ordinateur, y compris ceux dotés d’une ou plusieurs caméras, utilisant des images RVB ou des images de profondeur, et les systèmes qui s’appuient uniquement sur des données visuelles ou qui intègrent des capteurs portables (systèmes hybrides). Ensuite, nous avons développé et évalué plusieurs architectures d’apprentissage conçues pour émuler l’étape de la dynamique inverse dans l’analyse du mouvement. Ces dernières prédisent les couples articulaires à partir des données squelettiques de l’opérateur et son poids et la masse de la charge transportée, offrant ainsi une nouvelle alternative aux mé- thodes classiques de dynamique inverse. Enfin, nous avons examiné la généralisabilité des outils basés sur l’apprentissage profond, tels qu’OpenCap, dans les tâches industrielles. En utilisant le fine-tuning - une technique courante dans l’apprentissage profond pour adapter les modèles à de nouveaux ensembles de données avec des échantillons minimaux - nous avons cherché à adapter les modèles d’apprentissage d’OpenCap à un nouveau type de mouvement et à un nouvel ensemble de marqueurs
Assessing the risk of musculoskeletal disorders in industrial environments is a challenging task, given the complexity of modern manufacturing processes. These environments include various factors influencing operator activity, such as organizational, managerial and environmental elements, as well as the pace of work. Assessing the physical constraints to which operators are subjected is crucial to preventing these disorders. Although many systems currently monitor operator movements and assess postural constraints to provide an overview of physical activity, they often fail to analyze the physical forces experienced or generated by the operator. Consequently, it is essential to quantify these forces in order to identify effort-related physical risk factors. However, conventional measurement methods are often complex, invasive and impractical in industrial environments. This thesis addresses these challenges by evaluating learning approaches for estimating physical stresses without resorting to invasive measurements, which is fundamental to improving ergonomic tools and practices. We began by comparing the accuracy and robustness of computer vision-based measurement systems for RULA assessment, focusing particularly on on-site ergonomic evaluations. Our analysis focused primarily on the evaluation of computer vision-based systems, including those with one or more cameras, using RGB or depth images, and systems that rely solely on visual data or incorporate wearable sensors (hybrid systems). Next, we developed and evaluated several learning architectures designed to emulate the inverse dynamics step in motion analysis. These predict joint torques from the operator’s skeletal data and the weight and mass of the load carried, thus offering a new alternative to classical inverse dynamics methods. Finally, we examined the generalizability of deep learningbased tools, such as OpenCap, in industrial tasks. Using fine-tuning - a common technique in deep learning for adapting models to new data sets with minimal samples - we sought to adapt OpenCap’s learning models to a new type of motion and a new set of markers
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Herrmann, Christine. "Estimating Joint Torques on a Biodex System 3 Dynamometer." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/43529.

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The purpose of this investigation is to propose a method for determining resultant joint moments from a Biodex System 3 isokinetic dynamometer. Previous investigations using a dynamometers determined that measured moments from a dynamometer do not equate to the moment applied by the joint. Using Biodex System 3 dimensions and equipment, the proposed method corrects for gravity, acceleration and inertia moments, and relative angular movement between the dynamometer and the joint axis of rotation for the knee and ankle. The current method includes gravitations correction using a 3rd order polyfit method to a 4°/s passive trial, and inclusion of inertial moments from the dynamometer arm and limb segment. A method is also proposed to correct for gravitational moments, acceleration and inertia moments, and distal joint moments while testing the hip.

Previously proposed methods are then compared to the proposed method in isometric and isokinetic exertions. The comparison to a known moment concluded that the results for the isometric exertion are accurate for the proposed method. If the torque measurements from the dynamometer are independent of the velocity, as reported by the manufacturer, the validation of the proposed method for isometric testing holds true for isokinetic as well. The results from isokinetic testing show reasonable results for determining the resultant joint moments.

The proposed method can be simplified for clinical or experimental testing. If inertial and acceleration moments are not of concern, than using the propsed gravitational correction will account for the COM (Center of Mass). No additional measurements of the limb segement and dynamometer attachment are needed. The proposed method is recommended for Biodex System 3 isokinetic dynamometer correction in obtaining resultant joint moments at the knee, ankle, and hip.
Master of Science

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Liu, Pu. "Effect of Joint Angle on EMG-Torque Model During Constant-Posture, Quasi-Constant-Torque Contractions." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/376.

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The electrical activity of skeletal muscle¡ªthe electromyogram (EMG)¡ªis of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system and peripheral joints and muscles. This thesis mainly describes an experimental study which relates the simultaneous biceps/triceps surface EMG of 12 subjects to elbow torque at seven joint angles (ranging from 45¡ÃƒÂ£to 135¡ÃƒÂ£) during constant-posture, quasi-constant-torque contractions. The contractions ranged between 50% maximum voluntary contractions (MVC) extension and 50% MVC flexion. Advanced EMG amplitude (EMG¦Ãƒâ€™) estimation processors were investigated, and three nonlinear EMG¦Ãƒâ€™-torque models were evaluated. Results show that advanced (i.e., whitened, multiple-channel) EMG¦Ãƒâ€™ processors lead to improved joint torque estimation, compared to unwhitened, single-channel EMG¦Ãƒâ€™ processors. Depending on the joint angle, use of the multiple-channel whitened EMG¦Ãƒâ€™ processor with higher polynomial degrees produced a median error that was 50%-66% that found when using the single-channel, unwhitened EMG¦Ãƒâ€™ processor with a polynomial degree of 1. The best angle-specific model achieved a minimum error of 3.39% MVCF90 (i.e., error referenced to MVC at 90¢X flexion), yet it does not allow interpolation across angles. The best model which parameterizes the angle dependence achieved an error of 3.55% MVCF90. This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA]. (2) EMG-force modeling of force produced at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (3) Identification of a nonlinear, dynamic EMG-torque relationship about the elbow was studied [WPI]. (4) Signal whitening preprocessing for improved classification accuracies in myoelectric control of a prosthesis was studied [with WPI and the University of New Brunswick].
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Ruan, Tian-You, and 阮天佑. "Isotonic elbow joint torques estimation from surface EMG signal using an artificial neural network model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09015951867088665100.

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碩士
國立中央大學
機械工程研究所
96
The long term goal of this research is to develop the highly manipulated and accessible device. Until now, there are many countries in the world have started to research the exoskeleton system which will facilitate the daily activities of the disables. This device can be used in the military in the future to reduce the burden of soldiers and improve the operational capability. From medical perspective, the system can also assist physical disabled patients by accessing the feeble electromyographic signal data to support their physical operations, autonomous actions and improve their quality of daily activities. The electromyographic signal data measured from the contraction of the joint and muscle is used as the main parameter for estimate the joint torque. Considering the relation between the muscle strength result from the contractions of the triceps and biceps and the measured electromyogrphic signal is nonlinear, plus the muscle fiber length and the muscle contracted velocity also affect the elbow torque. Therefore, this research will use the electromyographic signal data, joint degree, and joint angular velocity as the input parameter, substitute the training steps to evaluated the weighting value of the backpropagation neural network to precisely estimate the joint torque.
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Books on the topic "Joint torques estimation"

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Estimation of internal consistency and stability reliability using isokinetic segmental curve analysis. 1990.

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Estimation of internal consistency and stability reliability using isokinetic segmental curve analysis. 1988.

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Estimation of internal consistency and stability reliability using isokinetic segmental curve analysis. 1990.

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Estimation of internal consistency and stability reliability using isokinetic segmental curve analysis. 1990.

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Book chapters on the topic "Joint torques estimation"

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Messaoui, Ali Zakaria, Mohamed Amine Alouane, Mohamed Guiatni, and Fazia Sbargoud. "Continuous Joint Movements and Torques Estimation Using an Optimized State-Space EMG Model." In Lecture Notes in Electrical Engineering, 91–99. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0045-5_9.

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Bordron, Olivier, Clément Huneau, Éric Le Carpentier, and Yannick Aoustin. "Human Squat Motion: Joint Torques Estimation with a 3D Model and a Sagittal Model." In Mechanisms and Machine Science, 247–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58104-6_28.

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Ramachandra, K., and Sourav Rakshit. "Estimation of Internal Joint Forces and Resisting Torques for Impact of Walking Robot Model." In Lecture Notes in Mechanical Engineering, 559–75. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3716-3_45.

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Ouadoudi Belabzioui, Hasnaa, Charles Pontonnier, Georges Dumont, Pierre Plantard, and Franck Multon. "Estimation of Upper-Limb Joint Torques in Static and Dynamic Phases for Lifting Tasks." In Advances in Digital Human Modeling, 71–80. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37848-5_8.

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Wang, P. R., Y. H. Chiu, M. S. Tsai, and K. C. Chung. "Estimation and Evaluation of Upper Limb Endpoint Stiffness and Joint Torques for Post-stroke Rehabilitation." In IFMBE Proceedings, 44–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03889-1_12.

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Wang, Zhi-qiang, Yu-kun Ren, and Hong-yuan Jiang. "A Mathematical Model of the Knee Joint for Estimation of Forces and Torques During Standing-up." In Lecture Notes in Electrical Engineering, 21–28. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_3.

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Liu, Xing, Fei Zhao, Baolin Liu, and Xuesong Mei. "Multi-point Interaction Force Estimation for Robot Manipulators with Flexible Joints Using Joint Torque Sensors." In Intelligent Robotics and Applications, 499–508. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27535-8_45.

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Ding, Zhongyi, Jianmin Li, and Lizhi Pan. "Comparing of Electromyography and Ultrasound for Estimation of Joint Angle and Torque." In Intelligent Robotics and Applications, 257–68. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6495-6_22.

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Zhou, Weigang, Qiang Hua, Chao Cheng, Xingyu Chen, Yunchang Yao, Lingyu Kong, Anhuan Xie, Shiqiang Zhu, and Jianjun Gu. "Joint Torque and Ground Reaction Force Estimation for a One-Legged Hopping Robot." In Intelligent Robotics and Applications, 529–41. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6495-6_45.

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Knežević, Nikola, Maja Trumić, Kosta Jovanović, and Adriano Fagiolini. "Input-Observer-Based Estimation of the External Torque for Single-Link Flexible-Joint Robots." In Advances in Service and Industrial Robotics, 97–105. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32606-6_12.

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Conference papers on the topic "Joint torques estimation"

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Bodo, Giulia, Christian Vassallo, Luca De Guglielmo, and Matteo Laffranchi. "Improving SEA Joint Torque Sensing for Enhanced Torque Estimation in Human-Machine Interaction." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 1295–302. IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711809.

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Zhang, Haocheng, Asta Kizyte, and Ruoli Wang. "Enhancing Dynamic Ankle Joint Torque Estimation Through Combined Data Augmentation Techniques." In 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 198–203. IEEE, 2024. http://dx.doi.org/10.1109/biorob60516.2024.10719753.

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Tahmid, Shadman, Josep Maria Font-Llagunes, and James Yang. "Upper Extremity Joint Torque Estimation Through an EMG-Driven Model." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89952.

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Abstract 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 force reduces, thus affecting the joint’s torque production. Understanding how muscles generate force is a key element to injury detection. Researchers 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 research 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 were used to determine the elbow joint torque. Experimental EMG signals and motion capture data were collected for a healthy subject. The musculoskeletal model was scaled to match the geometric parameters of the subject. First, the approach calculated muscle forces and joint moment for simple elbow flexion-extension. Later, the same approach was applied to an exercise called triceps kickback, which trains the triceps muscle group. Individual muscle forces and net joint torques for both tasks were estimated.
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Traversaro, Silvio, Andrea Del Prete, Serena Ivaldi, and Francesco Nori. "Inertial parameters identification and joint torques estimation with proximal force/torque sensing." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139476.

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Garofalo, Gianluca, Nico Mansfeld, Julius Jankowski, and Christian Ott. "Sliding Mode Momentum Observers for Estimation of External Torques and Joint Acceleration." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8793529.

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O'Sullivan, Patricia, Matteo Menolotto, Brendan O'Flynn, and Dimitrios Sokratis Komaris. "Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9870906.

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Ferlibas, Mehmet, and Reza Ghabcheloo. "Load weight estimation on an excavator in static and dynamic motions." In SICFP’21 The 17:th Scandinavian International Conference on Fluid Power. Linköping University Electronic Press, 2021. http://dx.doi.org/10.3384/ecp182p90.

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Excavators are one of the commonly used types of hydraulic machines in earth moving operations. The material handled is often transferred by dump trucks having a payload capacity that should not be exceeded. Payload monitoring systems are needed in order to prevent the possible problems during the delivery, increase the work efficiency, reduce the cost, and obtain the product information automatically without the requirement of truck scales. In this study, we propose a novel approach to estimate the load weight in the bucket of the excavator when the machine links are in motion. We consider the excavator as a three-revolute joint manipulator in vertical plane with the boom, the stick, and the bucket links. We rewrite the dynamic torque equations in a decoupled form as the linear combination of dynamic parameters and functions of joint angles, velocities, and accelerations. We perform least squares estimation to identify these parameters allowing us to predict the no load joint torques for any configuration of the links. We show that the most accurate torque prediction is the difference between the boom torque and the stick torque. We then derive the relation between the joint torques with and without the load, which are functions of the dynamic parameters. Using these equations, we can estimate the load weight. The relation becomes simpler when the links are stationary, since only the gravitational parameters remain present in the torque equations. The relation in dynamic case requires the parameters of the polar coordinates for the center of gravity of the bucket and we show that these parameters can be estimated with the knowledge of the empty bucket mass. We summarize our findings on load weight estimation for different cases including stationary poses and dynamic trajectories on free space and discuss the results. Although the friction is neglected throughout the modeling, the results obtained indicate that the effect of the static friction plays an important role in the accuracy of the estimated payload mass. We show that our dynamic model based solution is very promising, and exhibit only 2% error for high enough velocities.
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Tahamipour-Z., S. Mohammad, Iman Kardan, Hadi Kalani, and Alireza Akbarzadeh. "A PSO-MLPANN Hybrid Approach for Estimation of Human Joint Torques from sEMG Signals." In 2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS). IEEE, 2020. http://dx.doi.org/10.1109/cfis49607.2020.9238724.

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Roveda, Loris, Daniele Riva, Giuseppe Bucca, and Dario Piga. "External Joint Torques Estimation for a Position-Controlled Manipulator Employing an Extended Kalman Filter." In 2021 18th International Conference on Ubiquitous Robots (UR). IEEE, 2021. http://dx.doi.org/10.1109/ur52253.2021.9494674.

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Bueno, D. R., and L. Montano. "An optimized model for estimation of muscle contribution and human joint torques from sEMG information." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346686.

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