Dataset Information

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study.

ABSTRACT: Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electromyography. Specifically, the use of FMG to control different grips of a commercially available robotic hand, Bebionic3, is investigated. Four male transradially amputated subjects participated in the study, and a protocol was developed to assess the prediction accuracy of 11 grips. Different combinations of grips were examined, ranging from 6 up to 11 grips. The results indicate that it is possible to classify six primary grips important in activities of daily living using FMG with an accuracy of above 70% in the residual limb. Additional strategies to increase classification accuracy, such as using the available modes on the Bebionic3, allowed results to improve up to 88.83 and 89.00% for opposed thumb and non-opposed thumb modes, respectively.

SUBMITTER: Cho E

PROVIDER: S-EPMC4782664 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study.

Cho Erina E Chen Richard R Merhi Lukas-Karim LK Xiao Zhen Z Pousett Brittany B Menon Carlo C

Frontiers in bioengineering and biotechnology 20160308

Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electromyography. Specifically, the use of FMG to control different grips of ...[more]

PMID: 27014682

Similar Datasets

Project description: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.

Project description:BackgroundPartial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee's life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously.MethodsTen able-bodied participants performed three hand movements while their wrist was fixed in one of six predefined positions. The angle between Thumb and Index finger ([Formula: see text]), and Thumb and Middle finger ([Formula: see text]) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated.ResultsThe first approach had a correlation of determination ([Formula: see text]) of 0.871 for [Formula: see text] and [Formula: see text]. Using the second approach [Formula: see text] and [Formula: see text] were obtained. The first approach is over two times faster than the second approach while having similar performance; thus the first approach was selected to investigate the effect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically significant. A statistically significant decrease in performance resulted when less than five wrist positions were used for training.ConclusionsThe results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase.

Project description:BackgroundThe development of touchscreen-based assessments of upper extremity function could benefit people with multiple sclerosis (MS) by allowing convenient, quantitative assessment of their condition. The Pinching Test forms a part of the Floodlight smartphone app (F. Hoffmann-La Roche Ltd, Basel, Switzerland) for people with MS and was designed to capture upper extremity function.ObjectiveThis study aimed to evaluate the Pinching Test as a tool for remotely assessing upper extremity function in people with MS.MethodsUsing data from the 24-week, prospective feasibility study investigating the Floodlight Proof-of-Concept app for remotely assessing MS, we examined 13 pinching, 11 inertial measurement unit (IMU)-based, and 13 fatigability features of the Pinching Test. We assessed the test-retest reliability using intraclass correlation coefficients [second model, first type; ICC(2,1)], age- and sex-adjusted cross-sectional Spearman rank correlation, and known-groups validity (data aggregation: median [all features], SD [fatigability features]).ResultsWe evaluated data from 67 people with MS (mean Expanded Disability Status Scale [EDSS]: 2.4 [SD 1.4]) and 18 healthy controls. In this cohort of early MS, pinching features were reliable [ICC(2,1)=0.54-0.81]; correlated with standard clinical assessments, including the Nine-Hole Peg Test (9HPT) (|r|=0.26-0.54; 10/13 features), EDSS (|r|=0.25-0.36; 7/13 features), and the arm items of the 29-item Multiple Sclerosis Impact Scale (MSIS-29) (|r|=0.31-0.52; 7/13 features); and differentiated people with MS-Normal from people with MS-Abnormal (area under the curve: 0.68-0.78; 8/13 features). IMU-based features showed similar test-retest reliability [ICC(2,1)=0.47-0.84] but showed little correlations with standard clinical assessments. In contrast, fatigability features (SD aggregation) correlated with 9HPT time (|r|=0.26-0.61; 10/13 features), EDSS (|r|=0.26-0.41; 8/13 features), and MSIS-29 arm items (|r|=0.32-0.46; 7/13 features).ConclusionsThe Pinching Test provides a remote, objective, and granular assessment of upper extremity function in people with MS that can potentially complement standard clinical evaluation. Future studies will validate it in more advanced MS.Trial registrationClinicalTrials.gov NCT02952911; https://clinicaltrials.gov/study/NCT02952911.

Project description:Although it is known that swimming training can improve upper extremity performance, the force-time characteristics of the upper extremity during different training periods are not well understood. The objective of this study was to measure changes in the force-time characteristics of the upper extremity of young swimmers during different training periods within a season. Seventeen young swimmers, comprising 5 males (age: 15.4 ± 0.54 years); 12 females (16.4 ± 2.6 years) participated in this study. They were tested at four experimental test time points: baseline (E1), post-general preparation (E2), post-specific preparation (E3), and taper season (E4). The countermovement push-up test was performed using a force plate to measure force time parameters. Differences in force, time, velocity and impulse parameters were evaluated between the different periods. The study found that vertical take off velocity significantly increased across the assessed periods (F = 11.79; p = .001; η2 = .424), with significant increases from E1 to E2 (p < .001) and from E3 to E4 (p = .016). Flight Time also significantly increased across the assessed periods (F = 11.79; p = .001; η2 = .424), with significant increases from E1 to E2 (p < .001), from E1 to E4(p = .001), and from E3 to E4 (p = .005). The Force Impulse significantly increased throughout the assessed periods (F = 5.84; p = .012; η2 = .267), with significant increases from E1 to E2, (p = .006), from E1 to E3 (p = .016), and from E1 to E4 (p = .003). As this study shows, periods of increased training intensity can affect athletic progression, even though training aims to improve strength, speed, and performance. While some practical aspects such as strength, flight time, and impulse parameters may change during a macrocycle, the countermovement push-up test can provide trainers with an alternative and convenient way to monitor anaerobic force, speed, and performance, as well as measure explosive force-time performance in the upper body.

Dataset Information

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study.

Publications

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets