Integrating Posture Control in Assistive Robotic Devices to Support Standing Balance

Mergner, Thomas; Lippi, Vittorio

doi:10.1007/978-3-030-01887-0_61

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…(Choi and Kim, 2007;Abedi and Shoushtari, 2012;Zebenay et al, 2015;. The CNN can also be helpful in setting up wearable robots using the control parameters identified on the user (Chugo et al, 2019;Mergner and Lippi, 2019). The parameters can be a tool to benchmark humanoids and wearable devices (Torricelli et al, 2020), in particular, in the framework of the COMTEST project (Lippi et al, 2019a; that aims to make a posture control testbed available for the humanoid robotics community, and to define performance metrics.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids

Lippi

2021

Preprint

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This work presents a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control using the DEC (Disturbance Estimation and Compensation) parametric model. The modular structure of the proposed control model inspired the design of a modular identification procedure, in the sense that the same neural network is used to identify the parameters of the modules controlling different degrees of freedom. In this way the presented examples of body sway induced by external stimuli provide several training samples at once.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids

Lippi

2021

Preprint

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“…The modeling and the analysis of human posture control and balance provides and get inspirations from the study of humanoid robots control, e.g. (Choi and Kim, 2007;Abedi and Shoushtari, 2012;Zebenay et al, 2015;, or can be used to improve the design of assistive systems and devices (Chugo et al, 2019;Mergner and Lippi, 2019). The proposed deep-learning-based tool will be also published as a tool to benchmark humanoids and wearable devices (Torricelli et al, 2020), within the framework of the COMTEST project (Lippi et al, 2019) that aims to make a posture control testbed available for the humanoid robotics community.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Posture Control Nonlinear Model System and Noise Identification

Lippi¹,

Mergner²,

Maurer³

2020

Preprint

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In this work we present a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control models. A usual approach to the study of human posture control consists in the identification of parameters for a control system. In this context, linear models are particularly popular due to the relative simplicity in identifying the required parameters and to analyze the results. Nonlinear models, conversely, are required to predict the real behavior exhibited by human subjects and hence it is desirable to use them in posture control analysis. The use of CNN aims to overcome the heavy computational requirement for the identification of nonlinear models, in order to make the analysis of experimental data less time consuming and, in perspective, to make such analysis feasible in the context of clinical tests. Some potential implications of the method for humanoid robotics are also discussed.

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Deep Learning based Model Identification System Exploits the Modular Structure of a Bio-inspired Posture Control Model for Humans and Humanoids

Lippi

2021

Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods

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Integrating Posture Control in Assistive Robotic Devices to Support Standing Balance

Cited by 3 publications

References 7 publications

Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids

Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids

Deep Learning for Posture Control Nonlinear Model System and Noise Identification

Deep Learning based Model Identification System Exploits the Modular Structure of a Bio-inspired Posture Control Model for Humans and Humanoids

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