Robust Control of Physiologically Relevant Sit-to-Stand Motion Using Reduced Order Measurements

Rafique, Samina; Mahmood, A.; Najam-ul-Islam, Muhammad

doi:10.1007/978-3-030-02683-7_56

Cited by 4 publications

(10 citation statements)

References 18 publications

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“…General anthropometric parameters are borrowed from Iqbal and Pai, 16 which have been extensively used in studies on STS motion. 5,8,25,26 Analytical modeling framework…”

Section: Anthropometric Conversionmentioning

confidence: 99%

Neuro-fuzzy control of sit-to-stand motion using head position tracking

Rafique

Najam-ul-Islam

Shafique

et al. 2020

Measurement and Control

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Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.

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“…General anthropometric parameters are borrowed from Iqbal and Pai, 16 which have been extensively used in studies on STS motion. 5,8,25,26 Analytical modeling framework…”

Section: Anthropometric Conversionmentioning

confidence: 99%

Neuro-fuzzy control of sit-to-stand motion using head position tracking

Rafique

Najam-ul-Islam

Shafique

et al. 2020

Measurement and Control

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“…Figure 2) is used to simulate STS motion. The physiological parameters of the model (as shown in Table 1) have been borrowed from literature including our previous work [11,18,19,21]. The model has three degrees of freedom (DoF).…”

Section: Methodsmentioning

confidence: 99%

“…We develop an analytical human CNS modeling framework to generate STS motion. Our modeling scheme comprises the following steps: A general four-segment human biomechanical model in the sagittal plane based on BSP from the literature [ 2 , 9 – 11 , 19 – 21 ] is realized in SimMechanics We analytically generate head trajectory [ 22 ] to be used as the reference We design the STS controller to emulate human CNS, capable of (a) estimating joint angles using inverse kinematics based on head position measurements 𝒳 and (b) generating joint actuation torque commands τ by Cartesian control based on head position error δ𝒳 …”

Section: Methodsmentioning

confidence: 99%

“…This study is the part of an ongoing study that is aimed at determining different variables involved in human STS motion. The previous work [ 11 , 18 , 19 ] comprised the analytical approach, and this work is based on experimental analysis of STS motion. This paper is organized as follows: first, we provide the details of the analytical modeling framework for STS motion synthesis followed by the experimental setup and data collection of STS motion on human subjects.…”

Section: Introductionmentioning

confidence: 99%

“…We realize the CNS model as an STS controller having two subsystems: an Estimator to automatically plan joint level motions and the Cartesian controller to generate appropriate joint torque commands to reduce head position error. Our previous work [11,18,19] and some work from the literature [2,9,10,20,26] were based on the same analytical human model (realized in mathematical or simulation frameworks) using different combinations of measurements, feedbacks and controllers. We did the analytical design in the first phase to relate and compare our current study with the previous work.…”

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confidence: 99%

See 2 more Smart Citations

Cartesian Control of Sit-to-Stand Motion Using Head Position Feedback

Rafique

Najam-ul-Islam

Shafique

et al. 2020

Applied Bionics and Biomechanics

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Sit-to-stand (STS) motion is an indicator of an individual’s physical independence and well-being. Determination of various variables that contribute to the execution and control of STS motion is an active area of research. In this study, we evaluate the clinical hypothesis that besides numerous other factors, the central nervous system (CNS) controls STS motion by tracking a prelearned head position trajectory. Motivated by the evidence for a task-oriented encoding of motion by the CNS, we adopt a robotic approach for the synthesis of STS motion and propose this scheme as a solution to this hypothesis. We propose an analytical biomechanical human CNS modeling framework where the head position trajectory defines the high-level task control variable. The motion control is divided into low-level task generation and motor execution phases. We model CNS as STS controller and its Estimator subsystem plans joint trajectories to perform the low-level task. The motor execution is done through the Cartesian controller subsystem that generates torque commands to the joints. We do extensive motion and force capture experiments on human subjects to validate our analytical modeling scheme. We first scale our biomechanical model to match the anthropometry of the subjects. We do dynamic motion reconstruction through the control of simulated custom human CNS models to follow the captured head position trajectories in real time. We perform kinematic and kinetic analyses and comparison of experimental and simulated motions. For head position trajectories, root mean square (RMS) errors are 0.0118 m in horizontal and 0.0315 m in vertical directions. Errors in angle estimates are 0.55 rad, 0.93 rad, 0.59 rad, and 0.0442 rad for ankle, knee, hip, and head orientation, respectively. RMS error of ground reaction force (GRF) is 50.26 N, and the correlation between ground reaction torque and the support moment is 0.72. Low errors in our results validate (1) the reliability of motion/force capture methods and anthropometric technique for customization of human models and (2) high-level task control framework and human CNS modeling as a solution to the hypothesis. Accurate modeling and detailed understanding of human motion can have significant scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level task control schemes.

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