Human–Robot Interaction for Rehabilitation Robotics

Guo, Yao; Gu, Xiao; Yang, Guang‐Zhong

doi:10.1007/978-3-030-65896-0_23

Cited by 11 publications

(5 citation statements)

References 97 publications

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“…Electromyography (EMG) sensors attached to the skin measure the electrical signals introduced by muscle activities, which can be contaminated by noise originating from cross-talk and motion artifacts (Guo et al, 2021a ). Traditional surface EMG systems are inconvenient to set up and constrained in specific scenarios.…”

Section: Gait Analysis Methodologymentioning

confidence: 99%

“…In specific, Robot-Assisted Gait Training (RAGT) received wide attention for helping patients recover from gait impairments induced by stroke, spinal cord injury, cerebral palsy, PD, and Alzheimer's disease (Morone et al, 2017 ; Alwardat et al, 2018 ; Fang et al, 2020 ). Existing therapeutic robots for RAGT can be categorized into end-effector-based, grounded exoskeleton, wearable exoskeleton, and overground gait trainer (or robotic walker) (Hobbs and Artemiadis, 2020 ; Guo et al, 2021a ).…”

Section: Gait Intervention and Rehabilitationmentioning

confidence: 99%

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Detection and assessment of Parkinson's disease based on gait analysis: A survey

Guo

Yang

Liu

et al. 2022

Front. Aging Neurosci.

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Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

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Section: Gait Analysis Methodologymentioning

confidence: 99%

Section: Gait Intervention and Rehabilitationmentioning

confidence: 99%

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Guo

Yang

Liu

et al. 2022

Front. Aging Neurosci.

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“…In recent years, research efforts have been made to develop upper-limb prostheses (90,91,107,108), lower-limb prostheses (92,93), and prosthetic knee joints (98). Compared with passive prostheses, the motor control of active prostheses typically incorporates a bidirectional human-machine interface (109), where the prostheses can detect users' motion intention and conduct the desired movements, and the sensory information perceived by the prostheses is transferred to users as feedback. Research on bidirectional neural pathways (90,91), human-machine interaction (80,81), and human-machine interfaces (97) has been supported.…”

Section: Prostheses and Human-machine Interfacesmentioning

confidence: 99%

Medical Robotics: Opportunities in China

Guo

Chen

Zhao

et al. 2022

Annu. Rev. Control Robot. Auton. Syst.

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Medical robotics is a rapidly advancing discipline that is leading the evolution of robot-assisted surgery, personalized rehabilitation and assistance, and hospital automation. In China, both research and commercial developments in medical robotics have undergone exponential growth in recent years. In this review, we first give an overview of the clinical and social demands that motivate the rapid development in medical robotics. For each subdiscipline (surgery, rehabilitation and personal assistance, and hospital automation), we then summarize the major research projects sponsored by National Key Research and Development Programs. The remaining technical, commercial, and regulatory challenges are highlighted. This review also outlines some of the new opportunities in endoluminal and interventional robotics, micro- and nanorobotics, soft exoskeletons, intelligent human–robot interaction, and telemedicine and telesurgery, which may support the general uptake of robotics in medicine.

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“…Over the past decade, paradigm shifts have been made towards the way healthcare is delivered and managed. In particular, emerging pervasive sensing technologies, ranging from flexible, ultra-thin sensors, to low-power modules, coupled with advanced data analytics [1], [2], have enabled real-time personalized health monitoring, which transforms healthcare practice in terms of diagnostics [3], preventive healthcare [4], and rehabilitative and assistive technologies [5], as shown in Figure 1. Systems that are capable of acquiring physiological and behavioral signals are established, and they gradually turn into a common practice to draw information that indicates the states of human health and well being [6].…”

Section: Introductionmentioning

confidence: 99%

Beyond Supervised Learning for Pervasive Healthcare

Deligianni

Han

et al. 2024

IEEE Rev. Biomed. Eng.

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The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity, quality, and heterogeneity, hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this paper, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.

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Human–Robot Interaction for Rehabilitation Robotics

Cited by 11 publications

References 97 publications

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Medical Robotics: Opportunities in China

Beyond Supervised Learning for Pervasive Healthcare

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