A Learning Scheme for Reach to Grasp Movements: On EMG-Based Interfaces Using Task Specific Motion Decoding Models

Liarokapis, Minas; Artemiadis, Panagiotis; Kyriakopoulos, Kostas J.; Μανωλάκος, Ηλίας Σ.

doi:10.1109/jbhi.2013.2259594

Cited by 58 publications

(39 citation statements)

References 21 publications

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“…Previous studies [17,19,26,36], presented different approaches for mapping EMG signals to reaching and grasping motions according to object's features and locations and during static or dynamic gestures. In these approaches, the system is trained as the subject is asked to perform a grasp type, with or without holding an object, and stay there for a few seconds.…”

Section: Discussionmentioning

confidence: 99%

“…As result, these devices would increase their effectiveness and usability, and consequently increase the natural transition between the reaching and grasping phase on the prostheses increasing their acceptance by patients. However, at the moment only a limited number of studies focused on the detection of different grasp movements during reaching and grasping motions [25][26][27], and no measurement were performed to assess when a good classification was achieved respect to the hand's preshape .…”

Section: Introductionmentioning

confidence: 99%

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EMG-based decoding of grasp gestures in reaching-to-grasping motions

Batzianoulis

El-Khoury

Pirondini

et al. 2017

Robotics and Autonomous Systems

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Batzianoulis

El-Khoury

Pirondini

et al. 2017

Robotics and Autonomous Systems

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“…One of these approaches is based on surface electromyography (sEMG) recorded from the forearm [20]. While such an approach could potentially be used in a large variety of environments, including outdoors, its signal quality may degrade due to many environmental factors, such as sweating and electrical noise, which has been shown to drastically affect its performance [21].…”

Section: Introductionmentioning

confidence: 99%

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Xiao

Menon

2017

JMIR Rehabil Assist Technol

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BackgroundFunctional arm movements generally require grasping an object. The possibility of detecting and counting the action of grasping is believed to be of importance for individual with motor function deficits of the arm, as it could be an indication of the number of the functional arm movements performed by the individuals during rehabilitation. In this exploratory work, the feasibility of using armbands recording radial displacements of forearm muscles and tendons (ie, force myography, FMG) to estimate hand grasping with healthy individuals was investigated. In contrast to previous studies, this exploratory study investigates the feasibility of (1) detecting grasping when the participants move their arms, which could introduce large artifacts to the point of potentially preventing the practical use of the proposed technology, and (2) counting grasping during arm-reaching tasks.ObjectiveThe aim of this study was to determine the usefulness of FMG in the detection of functional arm movements. The use of FMG straps placed on the forearm is proposed for counting the number of grasping actions in the presence of arm movements.MethodsTen healthy volunteers participated in this study to perform a pick-and-place exercise after providing informed consent. FMG signals were simultaneously collected using 2 FMG straps worn on their wrist and at the midposition of their forearm, respectively. Raw FMG signals and 3 additional FMG features (ie, root mean square, wavelength, and window symmetry) were extracted and fed into a linear discriminant analysis classifier to predict grasping states. The transition from nongrasping to grasping states was detected during the process of counting the number of grasping actions.ResultsThe median accuracy for detecting grasping events using FMG recorded from the wrist was 95%, and the corresponding interquartile range (IQR) was 5%. For forearm FMG classification, the median accuracy was 92%, and the corresponding IQR was 3%. The difference between the 2 median accuracies was statistically significant (P<.001) when using a paired 2-tailed sign test. The median percentage error for counting grasping events when FMG was recorded from the wrist was 1%, and the corresponding IQR was 2%. The median percentage error for FMG recorded from the forearm was 2%, and the corresponding IQR was also 2%. While the median percentage error for the wrist was lower than that of the forearm, the difference between the 2 was not statistically significant based on a paired 2-tailed sign test (P=.29).ConclusionsThis study reports that grasping can reliably be counted using an unobtrusive and simple FMG strap even in the presence of arm movements. Such a result supports the foundation for future research evaluating the feasibility of monitoring hand grasping during unsupervised ADL, leading to further investigations with individuals with motor function deficits of the arm.

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“…Because the support from the assistive system is always provided after the initiation of the actual movement when using "assistance-as-needed" strategy, the operator and the system are not well-coupled, and the process of corporation between them demonstrates some degree of passivity [14], [15]. Other ways to predict the wearers' movement intention of the powered assistive exoskeletons are based on physiological signals, such as electromyography (EMG) signals [16], [17] and electroencephalography (EEG) signals [18], [19]. EMG signals can reflect the levels of activation of muscles and are highly related to the muscle contraction force [20].…”

Section: Introductionmentioning

confidence: 99%

Movement Performance of Human–Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot

Song

Gao

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

130

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Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD). The two control models could estimate the real-time ankle joint torque, and HNM is more accurate and can account for the change of the joint angle and muscle dynamics. Then, eight healthy volunteers were recruited to wear the ankle exoskeleton robot and complete a series of sinusoidal tracking tasks in the vertical plane. With the various levels of assist based on the two calibrated models, the subjects were instructed to track the target displayed on the screen as accurately as possible by performing ankle dorsiflexion and plantarflexion. Two measurements, the root mean square error (RMSE) and root mean square jerk (RMSJ), were derived from the assistant torque and kinematic signals to characterize the movement performances, whereas the amplitudes of the recorded EMG signals from the tibialis anterior (TA) and the gastrocnemius (GAS) were obtained to reflect the muscular efforts. The results demonstrated that the muscular effort and smoothness of tracking movements decreased with an increase in the assistant ratio. Compared with LPM, subjects made lower physical efforts and generated smoother movements when using HNM, which implied that a more physiologically appropriate model could enable more natural and human-like human-robot cooperation and has potential value for improvement of human-exoskeleton interaction in future applications.

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A Learning Scheme for Reach to Grasp Movements: On EMG-Based Interfaces Using Task Specific Motion Decoding Models

Cited by 58 publications

References 21 publications

EMG-based decoding of grasp gestures in reaching-to-grasping motions

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Movement Performance of Human–Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot

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