Stable force-myographic control of a prosthetic hand using incremental learning

Rasouli, Mahdi; Ghosh, Rohan; Lee, Wang Wei; Thakor, Nitish V.; Kukreja, Sunil L.

doi:10.1109/embc.2015.7319474

Cited by 22 publications

(15 citation statements)

References 8 publications

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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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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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“…Various invasive and non-invasive HMIs have been introduced for the control of upper limb prostheses. Some of the non-invasive HMIs for this application that have gained interest in the research community are: gaze tracking (Castellini and Koiva, 2013), electromyography (EMG) (Castellini et al, 2009;Scheme and Englehart, 2011), electroneurography (ENG) (Cloutier and Yang, 2013), mechanomyography (MMG) (Xiloyannis et al, 2015), force myography (FMG) (Rasouli et al, 2015), etc.…”

Section: Application Backgroundmentioning

confidence: 99%

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Ahmadizadeh

Pousett²,

Menon

2019

Front. Bioeng. Biotechnol.

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Background: Various human machine interfaces (HMIs) are used to control prostheses, such as robotic hands. One of the promising HMIs is Force Myography (FMG). Previous research has shown the potential for the use of high density FMG (HD-FMG) that can lead to higher accuracy of prosthesis control. Motivation: The more sensors used in an FMG controlled system, the more complicated and costlier the system becomes. This study proposes a design method that can produce powered prostheses with performance comparable to that of HD-FMG controlled systems using a fewer number of sensors. An HD-FMG apparatus would be used to collect information from the user only in the design phase. Channel selection would then be applied to the collected data to determine the number and location of sensors that are vital to performance of the device. This study assessed the use of multiple channel selection (CS) methods for this purpose. Methods: In this case study, three datasets were used. These datasets were collected from force sensitive resistors embedded in the inner socket of a subject with transradial amputation. Sensor data were collected as the subject carried out five repetitions of six gestures. Collected data were then used to asses five CS methods: Sequential forward selection (SFS) with two different stopping criteria, minimum redundancy-maximum relevance (mRMR), genetic algorithm (GA), and Boruta. Results: Three out of the five methods (mRMR, GA, and Boruta) were able to decrease channel numbers significantly while maintaining classification accuracy in all datasets. Neither of them outperformed the other two in all datasets. However, GA resulted in the smallest channel subset in all three of the datasets. The three selected methods were also compared in terms of stability [i.e., consistency of the channel subset chosen by the method as new training data were introduced or some training data were removed (Chandrashekar and Sahin, 2014)]. Boruta and mRMR resulted in less instability compared to GA when applied to the datasets of this study. Conclusion: This study shows feasibility of using the proposed design method that can produce prosthetic systems that are simpler than HD-FMG systems but have performance comparable to theirs.

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“…The sensor is made of a bidirectionally stretchy, knitted nylon (72%)/spandex (28%) material coated with a proprietary conductive formation. (8) The resistance of the sensor decreases as its length increases. In this study, the sensor was cut into a single piece with a length of 100 mm and a width of 20 mm, and had a resistance of about 200 kΩ in the relaxed state.…”

Section: Sensor Data Acquisition Systemmentioning

confidence: 99%

Human Joint Angle Estimation with Multiple-strip E-textile Stretching Sensor

Han¹,

Ryu²,

Kim³

2018

Sensors and Materials

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There have been many studies on strain-resistance or piezoresistance e-textile sensors. However, the important and critical factor, i.e., the elongation capability of the sensor against the actual stretching of the skin, has not been addressed yet. In this study, we propose a human joint angle estimation method with e-textile strain-resistance sensors. The stretching of the skin, on which the sensor is attached, is visually analyzed in terms of the stretching capability of the sensor and the actual stretching length of the skin. The overall system platform used to obtain and process the sensor data is described. The proposed system is implemented and applied to the elbow joint of a healthy subject. It was found that the sensors should be attached to the skin properly, especially when the skin is not stretched. It is necessary to consider the stretching capability of the sensor to accommodate the actual stretching of the skin. The two sensors attached on the lateral and medial sides provide complementary information, which can be utilized in a sensor fusion context.

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Stable force-myographic control of a prosthetic hand using incremental learning

Cited by 22 publications

References 8 publications

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

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

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Human Joint Angle Estimation with Multiple-strip E-textile Stretching Sensor

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