High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand, Ashkan; Scheme, Erik; Englehart, Kevin

doi:10.1682/jrrd.2015.03.0041

Cited by 106 publications

(82 citation statements)

References 28 publications

(72 reference statements)

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“…The voltage across the sense resistors was sampled between the 0 and 5 V range, with a 12-bit resolution, at 20 Hz, using a National Instruments DAQ Device (National Instruments, 2016) interfaced with custom LabVIEW software. The 20 Hz sampling rate was selected as it has been shown to be sufficient to achieve high FMG classification accuracies in the literature (Radmand et al, 2016). The resulting data acquisition system is schematically depicted in Figure 1C.…”

Section: Methodsmentioning

confidence: 99%

Force Myography for Monitoring Grasping in Individuals with Stroke with Mild to Moderate Upper-Extremity Impairments: A Preliminary Investigation in a Controlled Environment

Sadarangani

Jiang

Simpson

et al. 2017

Front. Bioeng. Biotechnol.

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There is increasing research interest in technologies that can detect grasping, to encourage functional use of the hand as part of daily living, and thus promote upper-extremity motor recovery in individuals with stroke. Force myography (FMG) has been shown to be effective for providing biofeedback to improve fine motor function in structured rehabilitation settings, involving isolated repetitions of a single grasp type, elicited at a predictable time, without upper-extremity movements. The use of FMG, with machine learning techniques, to detect and distinguish between grasping and no grasping, continues to be an active area of research, in healthy individuals. The feasibility of classifying FMG for grasp detection in populations with upper-extremity impairments, in the presence of upper-extremity movements, as would be expected in daily living, has yet to be established. We explore the feasibility of FMG for this application by establishing and comparing (1) FMG-based grasp detection accuracy and (2) the amount of training data necessary for accurate grasp classification, in individuals with stroke and healthy individuals. FMG data were collected using a flexible forearm band, embedded with six force-sensitive resistors (FSRs). Eight participants with stroke, with mild to moderate upper-extremity impairments, and eight healthy participants performed 20 repetitions of three tasks that involved reaching, grasping, and moving an object in different planes of movement. A validation sensor was placed on the object to label data as corresponding to a grasp or no grasp. Grasp detection performance was evaluated using linear and non-linear classifiers. The effect of training set size on classification accuracy was also determined. FMG-based grasp detection demonstrated high accuracy of 92.2% (σ = 3.5%) for participants with stroke and 96.0% (σ = 1.6%) for healthy volunteers using a support vector machine (SVM). The use of a training set that was 50% the size of the testing set resulted in 91.7% (σ = 3.9%) accuracy for participants with stroke and 95.6% (σ = 1.6%) for healthy participants. These promising results indicate that FMG may be feasible for monitoring grasping, in the presence of upper-extremity movements, in individuals with stroke with mild to moderate upper-extremity impairments.

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Section: Methodsmentioning

confidence: 99%

Force Myography for Monitoring Grasping in Individuals with Stroke with Mild to Moderate Upper-Extremity Impairments: A Preliminary Investigation in a Controlled Environment

Sadarangani

Jiang

Simpson

et al. 2017

Front. Bioeng. Biotechnol.

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“…Since then, the use of FMG for controlling hand prosthesis has gained some interest in the research community [27-33]. At the same time, researchers also explored the use of FMG for individuals with intact limbs for various applications.…”

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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“…In daily life, however, skin surface deformation normally reflects the contraction of other muscle groups, and can be used to recognize patterns of motion (Radmand et al, 2016). Further research will be needed to collect skin deformation data with dynamic movements of other joints if the technique is to be used in clinical practice.…”

Section: Resultsmentioning

confidence: 99%

“…The best results for people with upper-limb amputations are achieved by locating the sensor where the change in skin deformation is greatest. There is substantial interest in using skin deformation measured over a large area by high-resolution tactile sensors to control intended hand and wrist activity (Koiva et al, 2015) and gestures (Radmand et al, 2016, Li et al, 2008. By applying a strain gauge to the stump tip, the change in the extent of skin deformation in response to rotation of the bone within the stump can be used to control the extent of hand motion (Cho et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimating wrist joint angle with limited skin deformation information

Kato

Matsumoto

Kato

et al. 2018

JBSE

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Ascertaining a person's motion intentions through muscle activity is important for controlling various assistive devices for people with disabilities. Several techniques have been proposed for estimating the extent of intended joint angle motion using skin deformation information derived from muscle contractions. The objective of this study is to verify our signal processing procedure for estimating intended wrist joint angle with skin deformation information in able-bodied subjects and subjects with an upper-limb amputation. Skin deformation was measured with a tactile sensor consisting of 48 distance sensors over a large measurement area. The root-mean-square error (RMSE) of the measured and estimated angles are evaluated offline using multiple linear regression in one individual with an upper-limb amputation and five able-bodied participants. In all tests, subjects undertook a wrist flexion and extension task guided by visual feedback, measured in real time. Sensors are selected in descending order of the standard deviation of each sensor's value. Strong relationships occur between the position and displacement of the area of greatest skin deformation and the intended wrist joint angle in all subjects. The minimum RMSE was 8.19° for the individual with an upper-limb amputation using 48 sensors as input, and 2.24° for able-bodied individuals using 16 sensors. One-way repeated-measures analysis of variance showed that at least 16 sensors are needed to reliably record skin deformation. Skin deformation analyzed with multiple linear regression is a plausible means of estimating intended wrist joint angle in persons with an upperlimb amputation. Even when a limited number of sensors (≥16) are used, continuous joint angle can be estimated reliably. These findings will inform the design of assistive devices that must noninvasively determine muscle activity.

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High-density force myography: A possible alternative for upper-limb prosthetic control

Cited by 106 publications

References 28 publications

Force Myography for Monitoring Grasping in Individuals with Stroke with Mild to Moderate Upper-Extremity Impairments: A Preliminary Investigation in a Controlled Environment

Force Myography for Monitoring Grasping in Individuals with Stroke with Mild to Moderate Upper-Extremity Impairments: A Preliminary Investigation in a Controlled Environment

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

Estimating wrist joint angle with limited skin deformation information

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