Assistive Exoskeleton Hand Glove

El-Agroudy, Mohammed N.; Gaber, Mohammed; Joseph, D. S.; Ibrahim, M. B.; El-Amin, Mohamed F.; Helmy, Doaa; Hanafy, Mahmoud; Hisham, Soha; Awad, Mohammed I.; Youssef, Aliaa Rehan; Maged, Shady A.

doi:10.1109/itce48509.2020.9047803

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…All algorithms utilized the same set of features extracted by the data glove. This is a continuation of our previous work on home rehabilitation and assessment [26,27]. The outline of this paper is as follows.…”

Section: Introductionmentioning

confidence: 67%

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Sarwat

Maged

et al. 2021

Sensors

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The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

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Section: Introductionmentioning

confidence: 67%

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Sarwat

Maged

et al. 2021

Sensors

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“…The model is drawn as 4 parts: tube, outer layer, clockwise cable, and anti-clockwise cables, as shown in Figure 6 . The tube has a semi-circular cross-section [ 15 ]. It is also segmented so that 0.5 mm from the bottom is non-extendable, using a fiber glass material to prevent the actuator from elongating.…”

Section: Methodsmentioning

confidence: 99%

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove

2021

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The soft pneumatic actuators of an assistive exoskeleton hand glove are here designed. The design of the actuators focuses on allowing the actuator to perform the required bending and to restrict elongation or twisting of the actuator. The actuator is then modeled using ABAQUS/CAE, a finite element modeling software, and the open loop response of the model is obtained. The parameters of the actuator are then optimized to reach the optimal parameters corresponding to the best performance. Design of experiment (DOE) techniques are then approached to study the robustness of the system. Software-in-the-loop (SiL) is then approached to control the model variables via a proportional-integral-derivative (PID) control generated by FORTRAN code. The link between the two programs is to be achieved by the user subroutine that is written, where the subroutine receives values from ABAQUS/CAE, performs calculations, and passes values back to the software. The controller’s parameters are tuned and then the closed loop response of the model is obtained by setting the desired bending angle and running the model. Furthermore, a concentrated force at the tip of the actuator is added to observe the actuator’s response to external disturbance.

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Assessment of Post-Stroke Patients Using Smartphones and Gradient Boosting

Sarwat

Awad

et al. 2020

2020 15th International Conference on Computer Engineering and Systems (ICCES)

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Assistive Exoskeleton Hand Glove

Cited by 4 publications

References 8 publications

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove

Assessment of Post-Stroke Patients Using Smartphones and Gradient Boosting

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