Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends

Khalid, Umamah; Naeem, Muddasar; Stasolla, Fabrizio; Syed, Madiha; Abbas, Musarat; Coronato, Antonio

doi:10.2147/ijgm.s453903

Cited by 2 publications

(1 citation statement)

References 117 publications

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“…In clinical applications, machine learning algorithms are proving to be very useful ( Bao et al, 2019 ; Eichler et al, 2022 ). Machine learning (ML) (a) is a type of artificial intelligence (AI) focused on building computer systems that learn from data, (b) is a powerful tool for solving problems, streamlining various complex operations, and automating tasks, and (c) has broad applications in many areas, for example, science, engineering, industry, economics, databases, healthcare, and medicine ( Michalski et al, 2013 ; Alpaydin, 2016 ; Zhu et al, 2020 ; Sarker, 2021 ; Singh et al, 2021 ; Barton et al, 2024 ; Haimovich et al, 2024 ; Khalid et al, 2024 ). ML offers a wide range of techniques, such as decision trees, rule induction, neural networks, support vector machines (SVMs), clustering and classification methods, association rules, feature selection procedures, visualization, graphical models, or genetic algorithms; which are many more complex and use techniques well beyond traditional statistical techniques [i.e., hypothesis testing, experimental design, ANOVA, linear/logistic regression, generalized linear model (GLM), or principal component analysis (PCA)] ( Mitchel, 1997 ; Ben-David and Shalev-Shwartz, 2014 ; Marsland, 2015 ; Arnold et al, 2019 ; Bradley and Trevor, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Sokołowska,

Świderski,

Smolis-Bąk

et al. 2024

Front. Comput. Neurosci.

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IntroductionNovel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.Materials and methodsThe study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.Results and conclusionThe study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

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Section: Introductionmentioning

confidence: 99%

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Sokołowska,

Świderski,

Smolis-Bąk

et al. 2024

Front. Comput. Neurosci.

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Elevating Performance for Enhancing AI-Powered Humanoid Robots Through Innovation

Kunduru,

Dwivedi,

Aruna

et al. 2024

Applied AI and Humanoid Robotics for the Ultra-Smart Cyberspace

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This chapter explores strategies for enhancing the performance of AI-powered humanoid robots through innovation. It begins with an overview of the current landscape of humanoid robots, highlighting their diverse applications across industries. The review examines existing performance metrics and identifies areas for improvement. Subsequent sections delve into specific avenues for innovation, including advancements in cognitive capabilities, motor skills, emotional intelligence, and human-robot interaction. Leveraging machine learning techniques for continuous improvement is also explored. Ethical considerations, such as privacy concerns and bias mitigation, are addressed, along with challenges associated with societal impact. The chapter concludes with case studies showcasing successful implementations of performance-enhancing strategies and outlines potential future directions for research and development in the field.

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Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends

Cited by 2 publications

References 117 publications

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Elevating Performance for Enhancing AI-Powered Humanoid Robots Through Innovation

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