Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors

Zhang, Yizhai; Chen, Kuo; Yi, Jingang; Liu, Tao

doi:10.1109/tmech.2015.2490118

Cited by 41 publications

(30 citation statements)

References 29 publications

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“…After that, (33), (36), (37), and (39) were substituted into (35), and at the same time, the definitions of ς 3 , x 3d , ς 2 , x 2d and m 1 , m 2 , m 3 of the systems (25) and (22)- (24) were considered, respectively.…”

Section: Of 19mentioning

confidence: 99%

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Analysis and Verification of Finite Time Servo System Control with PSO Identification for Electric Servo System

Yang

Guo

et al. 2019

Energies

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Electric servo system (ESS) is a servo mechanism in a control system of an aircraft, a ship, etc., which controls efficiency and directly affects the energy consumption and the dynamic characteristics of the system. However, the control performance of the ESS is affected by uncertainties such as friction, clearance, and component aging. In order to improve the control performance of the ESS, a control technology combining particle swarm optimization (PSO) and finite time servo system control (FTSSC) was introduced into ESS. In fact, it is difficult to know the uncertain physical parameters of the real ESS. In this paper, the genetic algorithm (GA) was introduced into PSO and the inertia weight was improved, which increased the parameter optimization precision and convergence speed. A new feedback controller is proposed to improve response speed and reduce errors by using FTSSC theory. The performance of the controller based on PSO identification algorithm was verified by co-simulation experiments based on Automatic Dynamic Analysis of Mechanical Systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). Meanwhile, the proposed strategy was validated on the servo test platform in the laboratory. Compared with the existing control strategy, the control error was reduced by 75% and the steady-state accuracy was increased by at least 50%.

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Section: Of 19mentioning

confidence: 99%

“…Experiments showed that the proposed optimization algorithm had faster tracking speed and higher tracking accuracy. In [24], a technique with time-varying delay based on finite-time stability theory was applied to ESS. The results showed that the improved optimization method was effective and feasible for theoretical application.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Verification of Finite Time Servo System Control with PSO Identification for Electric Servo System

Yang

Guo

et al. 2019

Energies

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“…Fortunately, finite-time control has been widely used in control theory and applied research due to its advantages in convergence speed and convergence accuracy. In reference [16], the finite-time theoretical controller of linear parameter time-varying system and linear time-delay system is designed, and the effectiveness of the method is proved by the servo mechanism experiment. In [17], the theoretical idea of combining adaptive and finite-time theory is proposed.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and Verification of Finite-Time Rudder Control with GA Identification for Electric Rudder System

Yang

Guo

et al. 2020

Energies

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The electric rudder system (ERS) is the executive mechanism of the flight control system, which can make the missile complete the route correction according to the control command. The performance and quality of the ERS directly determine the dynamic quality of the flight control system. However, the transient and static characteristic of ERS is affected by the uncertainty of physical parameters caused by nonlinear factors. Therefore, the control strategy based on genetic algorithm (GA) identification method and finite-time rudder control (FTRC) theory is studied to improve the control accuracy and speed of the system. Differently from the existing methods, in this method, the difficulty of parameter uncertainty in the controller design is solved based on the ERS mathematical model parameter identification strategy. Besides, in this way, the performance of the FTRC controller was verified by cosimulation experiments based on automatic dynamic analysis of mechanical systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). In addition, the advantages of the proposed method are verified by comparing with the existing strategy results on the rudder test platform, indicating that the control accuracy is improved by 70% and the steady-state error is reduced by at least 50%.

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“…Accurate joint angle estimation may be achieved through model constraint methods [15]–[17], fusion of inertial and magnetic sensing [18], [19], and multi-sensor fusion using cameras, GPS, and laser range finders (see [20] and references therein). However, model constraint methods are not resistant to erroneous measurements and additional sensors such as laser range finders and cameras add bulk to the system.…”

Section: Introductionmentioning

confidence: 99%

“…However, model constraint methods are not resistant to erroneous measurements and additional sensors such as laser range finders and cameras add bulk to the system. Limb angle estimation algorithms have employed a Kalman filter [9], [19], [21], a complementary filter that uses acceleration measurements as correction signals [6], [22], an extended Kalman filter [15], [16], [18], [23], [24], unscented Kalman filter [17], and a resetting algorithm to correct drift in a gyroscope [10], [14]. However, these studies either use only limb kinematics (i.e., the underlying musculoskeletal dynamics is neglected) or linearize the measurement model.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Dynamics-Based Estimator for Functional Electrical Stimulation: Preliminary Results From Lower-Leg Extension Experiments

Allen

Zhong

Kirsch

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Miniature inertial measurement units (IMUs) are wearable sensors that measure limb segment or joint angles during dynamic movements. However, IMUs are generally prone to drift, external magnetic interference, and measurement noise. This paper presents a new class of nonlinear state estimation technique called state-dependent coefficient (SDC) estimation to accurately predict joint angles from IMU measurements. The SDC estimation method uses limb dynamics, instead of limb kinematics, to estimate the limb state. Importantly, the nonlinear limb dynamic model is formulated into state dependent matrices that facilitate the estimator design without performing a Jacobian linearization. The estimation method is experimentally demonstrated to predict knee joint angle measurements during functional electrical stimulation of the quadriceps muscle. The nonlinear knee musculoskeletal model was identified through a series of experiments. The SDC estimator was then compared to an Extended Kalman filter (EKF), which uses a Jacobian linearization and a rotation matrix method, which uses a kinematic model instead of the dynamic model. Each estimator’s performance was evaluated against the true value of the joint angle, which was measured through a rotary encoder. The experimental results showed that the SDC estimator, the rotation matrix method, and EKF had root mean square errors of 2.70°, 2.86°, and 4.42°, respectively. Our preliminary experimental results show the new estimator’s advantage over the EKF method but a slight advantage over the rotation matrix method. However, the information from the dynamic model allows the SDC method to use only one IMU to measure the knee angle compared to the rotation matrix method that uses 2 IMUs to estimate the angle.

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Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors

Cited by 41 publications

References 29 publications

Analysis and Verification of Finite Time Servo System Control with PSO Identification for Electric Servo System

Analysis and Verification of Finite Time Servo System Control with PSO Identification for Electric Servo System

Synthesis and Verification of Finite-Time Rudder Control with GA Identification for Electric Rudder System

A Nonlinear Dynamics-Based Estimator for Functional Electrical Stimulation: Preliminary Results From Lower-Leg Extension Experiments

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