User-Driven Functional Movement Training With a Wearable Hand Robot After Stroke

Park, Sangwoo; Fraser, Michaela; Weber, Lynne M.; Meeker, Cassie; Bishop, Lauri; Geller, Daniel; Stein, Joel; Ciocarlie, Matei

doi:10.1109/tnsre.2020.3021691

Cited by 33 publications

(19 citation statements)

References 45 publications

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“…In contrast to the reliability, the robustness describes how an IDS performs under varying conditions, e.g., invalid user inputs, or stressful or changing environments. Some studies did assess the robustness analogously to the reliability by measuring success or error rates under varying conditions, e.g., with changing arm positions (Park et al, 2020 ), when distracting the participant (Ortner et al, 2011 ), or when the ULO is used with or without arm support (Park et al, 2019 ). Others provided a qualitative indication of the robustness of the IDS by identifying factors that do or do not influence its performance (e.g., Siu et al, 2018 ).…”

Section: Reviewmentioning

confidence: 99%

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

et al. 2022

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Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.

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Section: Reviewmentioning

confidence: 99%

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

et al. 2022

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“…EMG has been widely used for pattern recognition for many years, especially for applications in prosthesis (Lee and Saridis, 1984 ; Ajiboye and Weir, 2005 ; Kuiken et al, 2009 ). The use of EMG in driving assistive and rehabilitation robotics post stroke is increasing recently (Hu et al, 2013 ; Leonardis et al, 2015 ; Park et al, 2020 ). However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory (Lu et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

Zhou

Zhang

et al. 2021

Front. Neurorobot.

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Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step toward the development of wearable hand robots. Electromyography (EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory. Moreover, low cost kinematic sensors such as Leap Motion is recently used for pattern recognition in various applications. This study proposes feature fusion and decision fusion method that combines EMG features and kinematic features for hand pattern recognition toward application in upper limb assistive and rehabilitation robotics. Ten normal subjects and five post stroke patients participating in the experiments were tested with eight hand patterns of daily activities while EMG and kinematics were recorded simultaneously. Results showed that average hand pattern recognition accuracy for post stroke patients was 83% for EMG features only, 84.71% for kinematic features only, 96.43% for feature fusion of EMG and kinematics, 91.18% for decision fusion of EMG and kinematics. The feature fusion and decision fusion was robust as three different levels of noise was given to the classifiers resulting in small decrease of classification accuracy. Different channel combination comparisons showed the fusion classifiers would be robust despite failure of specific EMG channels which means that the system has promising potential in the field of assistive and rehabilitation robotics. Future work will be conducted with real-time pattern classification on stroke survivors.

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“…Control type Accuracy Delay (2) [53] Point-in-Polygon (PIP) (3 predefined gestures) Accuracy: 0.944 - [57] EMG-driven (neural network to determine force) Force error = 20.7 % - [68] Threshold algorithm -- [69] Linear Bayes classifier (6 predefined gestures) Accuracy (1) : 98.1±4.9% Yes [70] Classification algorithm (6 predefines gestures) Accuracy: 86.38 % Yes [71] EMG-based intent inference method -- [72] Neural network -- [73] Forest classifier (3 predefined gestures) Accuracy: 77.9-85.2 % - [74] Neural Network (4 predefined gestures) Accuracy: 98.7±0.53 - (1) Accuracy for neurologically intact subjects. (2) Whether delay analysis of the system is carried out.…”

Section: Refmentioning

confidence: 99%

RobHand: A Hand Exoskeleton With Real-Time EMG-Driven Embedded Control. Quantifying Hand Gesture Recognition Delays for Bilateral Rehabilitation

et al. 2021

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User-Driven Functional Movement Training With a Wearable Hand Robot After Stroke

Cited by 33 publications

References 45 publications

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

RobHand: A Hand Exoskeleton With Real-Time EMG-Driven Embedded Control. Quantifying Hand Gesture Recognition Delays for Bilateral Rehabilitation

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