Development of an Untethered Adaptive Thumb Exoskeleton for Delicate Rehabilitation Assistance

Li, Guotao; Cheng, Long; Ziwen, Gao; Xia, Xiuze; Jiang, Jingang

doi:10.1109/tro.2022.3180832

Cited by 29 publications

(15 citation statements)

References 46 publications

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“…The chain has an RPR architecture, characterized by the decomposition of the first rotational joint into a series of hinges, to accommodate for a physiological ROM of the finger within a compact structure. The RRP structure, with the prismatic joint housed on the finger, is employed by systems in recent literature [27], [28]. Here, an RPR architecture was preferred because it provides for a slider with a stroke independent of the first phalanx's length and can thus accommodate various anthropometries.…”

Section: Discussionmentioning

confidence: 99%

Self-Aligning Finger Exoskeleton for the Mobilization of the Metacarpophalangeal Joint

Peperoni

Capitani

Fiumalbi

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: Discussionmentioning

confidence: 99%

Self-Aligning Finger Exoskeleton for the Mobilization of the Metacarpophalangeal Joint

Peperoni

Capitani

Fiumalbi

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Besides post-operative rehabilitation after trauma or injuries, numerous wearable devices were presented in the scientific literature for rehabilitation after damage to the nervous system due to stroke. In particular, G. Li et al presented an untethered adaptable thumb exoskeleton that actively supports the thumb’s three degrees of freedom of motion [ 114 ]. An adaptive thumb and spherical mechanisms constitute the exoskeleton, including sliders made of ball-type linear bearings to reduce the rolling friction.…”

Section: Overview Of Available Wearable Technologies For Body Motion ...mentioning

confidence: 99%

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

Fazio

Mastronardi

Vittorio

et al. 2023

Sensors

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A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

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“…According to the Global Stroke Organization [3], 5.5 M will die from strokes, and 13 M will suffer from a stroke annually. Stroke is the leading cause of death and functional impairment globally, so it significantly affects all facets of life [4]. Anyone at any age, regardless of gender or physical condition, can be affected by stroke [5].…”

Section: Introductionmentioning

confidence: 99%

A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

et al. 2023

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A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.

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Development of an Untethered Adaptive Thumb Exoskeleton for Delicate Rehabilitation Assistance

Cited by 29 publications

References 46 publications

Self-Aligning Finger Exoskeleton for the Mobilization of the Metacarpophalangeal Joint

Self-Aligning Finger Exoskeleton for the Mobilization of the Metacarpophalangeal Joint

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

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