Two DoF Robotic Platform for Balance Rehabilitation Tasks

Rivera, Oswaldo; Abril, Juan D.; Niño-Suárez, Paola Andrea; Sánchez, Oscar Fernando Avilés; Castillo-Castañeda, Eduardo

doi:10.1007/978-3-030-60372-4_37

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…For its part, This system shows the possibility of using similar solutions to support the processes of diagnosis and evaluation of physical activity by specialized personnel. The integration of this classifier to the assistive robotics system for balance training presented in (Rivera, Abril, Niño-Suarez, Avilés, & Castillo-Castañeda, 2021), is intended to mitigate the problem of subjectivity present in balance evaluation systems, allowing them to adjust the assistance parameters of the robotic platform according to the user's performance.…”

Section: Discussionmentioning

confidence: 99%

Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

Rivera

Castillo-Castañeda²,

Sánchez³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

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The rapid increase of the elderly population and chronic diseases have augmented disability in today's world. This situation has led researchers and engineers to create tools and technologies that allow health caregivers, physical trainers, and health policymakers to understand, measure, and treat people with disabilities. Nowadays, artificial intelligence techniques have been applied to improve the performance of these technologies. This article shows the development of a novel classifier that utilizes Machine Learning (ML) algorithms and biomechanical signals to predict a subject's International Physical Activity Questionnaire (IPAQ) and Falls Efficacy Scale (FES). Three ML algorithms were applied K-Nearest Neighbors (KNN), Decision tree, and Support Vector Machine (SVM). Results show the accuracy of classification over 95%, 99%, and 89%, respectively, and validate the correlation between qualitative scales and biomechanical responses in balance training. This classifier poses as an innovative tool to help professionals adjust and improve physical training programs.

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Section: Discussionmentioning

confidence: 99%

Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

Rivera

Castillo-Castañeda²,

Sánchez³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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Devices for Gait and Balance Rehabilitation: General Classification and a Narrative Review of End Effector-Based Manipulators

Diego,

Herrero,

Macho

et al. 2024

Applied Sciences

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Gait and balance have a direct impact on patients’ independence and quality of life. Due to a higher life expectancy, the number of patients suffering neurological disorders has increased exponentially, with gait and balance impairments being the main side effects. In this context, the use of rehabilitation robotic devices arises as an effective and complementary tool to recover gait and balance functions. Among rehabilitation devices, end effectors present some advantages and have shown encouraging outcomes. The objective of this study is twofold: to propose a general classification of devices for gait and balance rehabilitation and to provide a review of the existing end effectors for such purposes. We classified the devices into five groups: treadmills, exoskeletons, patient-guided systems, perturbation platforms, and end effectors. Overall, 55 end effectors were identified in the literature, of which 16 were commercialized. We found a disproportionate number of end effectors capable of providing both types of rehabilitation (2/55) and those focused on either balance (21/55) or gait (32/55). The analysis of their features from a mechanical standpoint (degrees of freedom, topology, and training mode) allowed us to identify the potential of parallel manipulators as driving mechanisms of end effector devices and to suggest several future research directions.

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Two DoF Robotic Platform for Balance Rehabilitation Tasks

Cited by 2 publications

References 17 publications

Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

Devices for Gait and Balance Rehabilitation: General Classification and a Narrative Review of End Effector-Based Manipulators

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