Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance

Baptista, Roberto de Souza; Bó, Antônio Padilha Lanari; Hayashibe, Mitsuhiro

doi:10.1109/tnsre.2016.2591783

Cited by 25 publications

(5 citation statements)

References 19 publications

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“…We believe that this method can also be beneficial for other related biomechanical research by obtaining a GRF estimate in a scenario where we can not use the force place. In a future work, we plan to extend this method to be integrated with motion performance, motion discrimination information [ 24 ], and time-scale considered motion synergy information [ 25 , 26 ] for rehabilitation purposes.…”

Section: Discussionmentioning

confidence: 99%

Ground Reaction Force and Moment Estimation through EMG Sensing Using Long Short-Term Memory Network during Posture Coordination

et al. 2023

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Motion prediction based on kinematic information such as body segment displacement and joint angle has been widely studied. Because motions originate from forces, it is beneficial to estimate dynamic information, such as the ground reaction force (GRF), in addition to kinematic information for advanced motion prediction. In this study, we proposed a method to estimate GRF and ground reaction moment (GRM) from electromyography (EMG) in combination with and without an inertial measurement unit (IMU) sensor using a machine learning technique. A long short-term memory network, which is suitable for processing long time-span data, was constructed with EMG and IMU as input data to estimate GRF during posture control and stepping motion. The results demonstrate that the proposed method can provide the GRF estimation with a root mean square error (RMSE) of 8.22 ± 0.97% (mean ± SE) for the posture control motion and 11.17 ± 2.16% (mean ± SE) for the stepping motion. We could confirm that EMG input is essential especially when we need to predict both GRF and GRM with limited numbers of sensors attached under knees. In addition, we developed a GRF visualization system integrated with ongoing motion in a Unity environment. This system enabled the visualization of the GRF vector in 3-dimensional space and provides predictive motion direction based on the estimated GRF, which can be useful for human motion prediction with portable sensors.

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Section: Discussionmentioning

confidence: 99%

Ground Reaction Force and Moment Estimation through EMG Sensing Using Long Short-Term Memory Network during Posture Coordination

et al. 2023

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“…Nevertheless, given the issues discussed here, further methods to take into account the effect involuntary movements should be considered to enable robust replacement of AAC based on mechanical switches, particularly for those individuals with severe movement disorders. One of such methods would involve modeling the involuntary movement itself, such as in [13,14], while another approach would involve providing a more comprehensive framework for defining head movements to operate the HCI, such as in [15].…”

Section: Discussionmentioning

confidence: 99%

Tracking and Classification of Head Movement for Augmentative and Alternative Communication Systems

Gonçalves

Richa

Bó

2022

Sensors

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The use of assistive technologies can mitigate or reduce the challenges faced by individuals with motor disabilities to use computer systems. However, those who feature severe involuntary movements often have fewer options at hand. This work describes an application that can recognize the user’s head using a conventional webcam, track its motion, model the desired functional movement, and recognize it to enable the use of a virtual keyboard. The proposed classifier features a flexible structure and may be personalized for different user need. Experimental results obtained with participants with no neurological disorders have shown that classifiers based on Hidden Markov Models provided similar or better performance than a classifier based on position threshold. However, motion segmentation and interpretation modules were sensitive to involuntary movements featured by participants with cerebral palsy that took part in the study.

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“…Both HMM and SLDS can address such problems and have been widely used in several problem fields. Examples include human motion [6,7], computer vision [8], speech recognition [9], econometrics [10], machine learning [11] and neuroscience [12,13].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference of Recurrent Switching Linear Dynamical Systems with Higher-Order Dependence

Wang,

Chen

2024

Symmetry

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Many complicated dynamical events may be broken down into simpler pieces and efficiently described by a system that shifts among a variety of conditionally dynamical modes. Building on switching linear dynamical systems, we develop a new model that extends the switching linear dynamical systems for better discovering these dynamical modes. In the proposed model, the linear dynamics of latent variables can be described by a higher-order vector autoregressive process, which makes it feasible to evaluate the higher-order dependency relationships in the dynamics. In addition, the transition of switching states is determined by a stick-breaking logistic regression, overcoming the limitation of a restricted geometric state duration and recovering the symmetric dependency between the switching states and the latent variables from asymmetric relationships. Furthermore, logistic regression evidence potentials can appear as conditionally Gaussian potentials by utilizing the Pólya-gamma augmentation strategy. Filtering and smoothing algorithms and Bayesian inference for parameter learning in the proposed model are presented. The utility and versatility of the proposed model are demonstrated on synthetic data and public functional magnetic resonance imaging data. Our model improves the current methods for learning the switching linear dynamical modes, which will facilitate the identification and assessment of the dynamics of complex systems.

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Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance

Cited by 25 publications

References 19 publications

Ground Reaction Force and Moment Estimation through EMG Sensing Using Long Short-Term Memory Network during Posture Coordination

Ground Reaction Force and Moment Estimation through EMG Sensing Using Long Short-Term Memory Network during Posture Coordination

Tracking and Classification of Head Movement for Augmentative and Alternative Communication Systems

Bayesian Inference of Recurrent Switching Linear Dynamical Systems with Higher-Order Dependence

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