Musculoskeletal-see-through mirror: Computational modeling and algorithm for whole-body muscle activity visualization in real time

Murai, Akihiko; Kurosaki, Kosuke; Yamane, Katsu; Nakamura, Yoshihiko

doi:10.1016/j.pbiomolbio.2010.09.006

Cited by 64 publications

(37 citation statements)

References 22 publications

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“…On the other hand, several musculoskeletal parameters may be more useful for improving the asymmetrical gait. System visualization of several human musculoskeletal data, such as muscle tension information [19], will be collected in future work.…”

Section: Resultsmentioning

confidence: 99%

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Ogata

Mita

Shimizu

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYSome unilateral lower-limb amputees, have through continued exertion, increase the foot reaction force of the sound leg. The asymmetric gait with a prosthetic leg may thus negatively affect the musculoskeletal health of the leg on the healthy side. Therefore, it is important for these amputees to learn how to adjust the balance of each foot load in training. The aim of this study is to develop a training support system visualizing floor-reaction forces using a color-depth sensor. The pose of the entire body of the amputee is measured by the depth sensor, and the floor reaction force is estimated based on Zero Moment Point (ZMP), which is calculated using the center of mass of the amputee. Evaluation experiments of the proposed method were performed and they confirmed the effectiveness of the estimation method and the training with the visualization of reaction force.

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Section: Resultsmentioning

confidence: 99%

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Ogata

Mita

Shimizu

et al. 2015

IEICE Trans. Inf. & Syst.

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“…• Maximal muscle force can be estimated from measurements of physiological cross-sectional areas of muscles (Murai et al, 2010).…”

Section: Musculoskeletal Modelmentioning

confidence: 99%

“…For all kinds of rehabilitation therapies, the human-robot interface stands out because it can accurately and quickly simulate the structure and movement of the human body ( Figure 1) (Lo and Xie, 2012). Traditionally, biomechanists have developed complex human limb models which combine neural signals with kinematic and kinetic data to study human control strategies (Buchanan et al, 2004a;Piazza and Delp, 1996;Schutte et al, 1993;Murai et al, 2010;Lloyd and Buchanan, 1996). However, due to their complexity, the computational blocks inside the models can only execute individually and tuning methods are also required to accommodate changes and variations.…”

Section: Introductionmentioning

confidence: 99%

Review of EMG-based neuromuscular interfaces for rehabilitation: elbow joint as an example

Tao

Xie

Zhang

et al. 2013

IJBBR

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Assistive robots have made great contributions to disabled people in physiotherapy and rehabilitation areas. The interface between patients and medical devices plays a significant role for patients to operate these kinds of robots. This review introduces the current research and development of neuromuscular interfaces, especially the new research directions with special focus on modelling of musculoskeletal systems for interfacing purposes. The paper also summarises the function and prominent advantage of using surface electromyography (sEMG) signals for the interface. The elbow joint was used as an example to go through the working steps of both human biological systems and neuromuscular interfaces. Further developments were also discussed to improve the interface to meet medical demands.

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“…Musculoskeletal models based on inverse dynamics are currently operated offline and available in software packages such as OpenSim [5], AnyBody [6] and Biomechanics of Bodies [7]. Recent studies proposed online solutions, facilitating translation to clinical scenarios [8], [9]. In these methods, the multi-muscle recruitment problem is solved by navigating the solution space and selecting one muscle activation strategy that is optimal according to a priori defined physiological criteria, i.e.…”

Section: Introductionmentioning

confidence: 99%

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

Durandau

Farina

Sartori

2018

IEEE Trans. Biomed. Eng.

132

113

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Abstract-Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses. Current biomechanical models cannot be used for real-time control in man-machine interfaces. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e. predicting accurate joint moments during six unseen motor tasks and about one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient interfacing with wearable assistive devices.

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Musculoskeletal-see-through mirror: Computational modeling and algorithm for whole-body muscle activity visualization in real time

Cited by 64 publications

References 22 publications

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Review of EMG-based neuromuscular interfaces for rehabilitation: elbow joint as an example

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

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