A Modular Robotic System for Assessment and Exercise of Human Movement

Baca, José; Ambati, Mohan Sai; Dasgupta, Prithviraj; Mukherjee, Mukul

doi:10.1007/978-3-319-54377-2_6

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…Similarly, authors of [38] used EEG and EMG signals for brain intent recognition to develop a brain machine interface (BMI) to enhance the functionality of an exoskeleton. Baca et al [39] suggested a wearable, modular robotic system, whereas Lu et al [40] proposed an EMG-driven exoskeleton hand controlled by the patients' intentions. Apart from [39], which did not have any clinical validations to date, all the others indicate significant improvement in all metrics.…”

Section: A Robot-assisted Systemsmentioning

confidence: 99%

“…Baca et al [39] suggested a wearable, modular robotic system, whereas Lu et al [40] proposed an EMG-driven exoskeleton hand controlled by the patients' intentions. Apart from [39], which did not have any clinical validations to date, all the others indicate significant improvement in all metrics. Some studies have sought low-cost alternatives to roboticassisted rehabilitation systems.…”

Section: A Robot-assisted Systemsmentioning

confidence: 99%

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AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapistlike assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

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Section: A Robot-assisted Systemsmentioning

confidence: 99%

Section: A Robot-assisted Systemsmentioning

confidence: 99%

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Our proposed solution is based on the modularity concept and brings benefits such as versatility, robustness and scalability to the tasks. A Modular Multi-Motor Exercise System that can both, (1) rearrange their modules to adapt to different upper body and lower body parts, and (2) provide strength training and aerobic exercises, makes it ideal for this challenge in space. To increase safety of crewmembers during execution of the exercises, Magneto-Rheological fluid has been explored as main component in the design of the modules (elemental unit of the system).…”

Section: Objectivementioning

confidence: 99%

“…This project aims to develop a system that can be used to perform several resistive and aerobic exercises while using the same space, therefore, reducing size and mass of current ISS equipment. To address the problem, we explore a new type of strategy via modular design [1,2]. The system consists of homogeneous modules that can be assembled and disassembled around a platform (cage-like frame that can eventually be attached to the current Vibration Isolation System) by crewmembers to workout different muscle groups.…”

Section: Modular Multi-motor Exercise Systemmentioning

confidence: 99%

Modular multi-motor exercise system for space exploration

et al. 2020

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The goal of this project is to develop new technology that will support crew health and performance during long duration space missions. While there are current methods for maintaining fitness aboard the International Space Station, the results are not ideal; muscle mass and bone density continue to degrade, and there is still room for improvement. Moreover, new exploration missions need to maximize the use of the equipment due to storage limitations. A system that can be used for multiple purposes such as for providing multiple strength training exercises, as well as, providing aerobic exercises while using the same workout space is desirable. This project aims to address these challenges by introducing a modular system that will bring versatility, scalability, and robustness, by using mountable and reconfigurable modules to maximize the number of exercises that can be performed. In this paper we present the development of a Modular Multi-Motor Exercise System concept based on Magneto-Rheological fluid-based modules.

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“…Sensorimotor control is a nervous system mechanism within the human body that receives sensory stimuli from its environment, and it transmits, processes, and integrates the neural signal as motor command outputs to accomplish a goal [14]. As people age, daily life activities such as walking, cooking, showering, and manipulating objects become difficult tasks to achieve due to a reduction in their functional performance [2]. As a result, many older adults are at risk of accidents or injuries, causing an individual to relinquish their independence.…”

Section: Introductionmentioning

confidence: 99%

Towards predicting sensorimotor disorders in older adults via Bayesian probabilistic theory and mixed reality

2019

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In this work, we present the Overwatch-M platform, a novel approach that combines Bayesian theory with mixed reality (MR) to assess the user's sensorimotor control. This platform is composed of a set of simple MR tests, which are short, and guides the user through a series of activities while monitoring the user's interaction. The main advantage of this strategy relays on its ability to combine prior and observed user's interaction to determine the impact on the user's functional performance. This approach aims to detect changes in the user's functional performance that could lead to sensorimotor problems in older adults at an early stage. For validation, a Bayesian model is implemented into the system to collect and analyze datasets and to provide a quantitative estimation of functional performance by providing a probabilistic conditioning assessment of their test-interaction over time. The strategy has been implemented to test the sensorimotor function, eye-hand coordination.

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A Modular Robotic System for Assessment and Exercise of Human Movement

Cited by 7 publications

References 30 publications

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Modular multi-motor exercise system for space exploration

Towards predicting sensorimotor disorders in older adults via Bayesian probabilistic theory and mixed reality

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