Machine Learning in Robot-Assisted Upper Limb Rehabilitation: A Focused Review

Ai, Qingsong; Liu, Zemin; Meng, Wei; Liu, Quan; Xie, Shane

doi:10.1109/tcds.2021.3098350

Cited by 33 publications

(17 citation statements)

References 98 publications

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“…Several biomechanical parameters have been proposed in the scientific literature to date for assessing the quality of movement in healthy and diseased subjects in different rehabilitation settings and tasks [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Moreover, several studies have aimed to exploit motion analysis data and compare different instrumentations for diagnostic purposes [32,33].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Ponsiglione

Ricciardi

Amato

et al. 2022

Sensors

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The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson’s disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson’s disease.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Ponsiglione

Ricciardi

Amato

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…Moreover, deep learning approaches have been shown to improve classification accuracy. Deep networks can also detect latent structures or patterns in raw data [ 92 ], and robots can study innate movement patterns and estimate human intentions when combined with MLAs [ 93 ].…”

Section: Eeg Control Strategiesmentioning

confidence: 99%

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

et al. 2022

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Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.

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“…IMU signals are used to predict the joint information of the upper limb, such as joint angles and velocities. Normally, a mapping model can be established to match the relationship between past movement and intended motion [ 23 ]. A common method is to use human kinematic movement to predict the motion pattern.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang

Garg

Gao

et al. 2023

Sensors

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The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.

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Machine Learning in Robot-Assisted Upper Limb Rehabilitation: A Focused Review

Cited by 33 publications

References 98 publications

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

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