Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm

Kanitz, Gunter; Antfolk, Christian; Cipriani, Christian; Sebelius, Fredrik; Carrozza, Maria Chiara

doi:10.1109/iembs.2011.6090465

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…In fact, as demonstrated by Farrell and Weir with an extensive work on pattern recognition 29 that compared different feature sets and recording techniques (targeted surface, targeted intramuscular, and untargeted surface electrodes), the fundamental requirement is the ability of the algorithm to accurately identify a pattern, regardless of which feature set or recording technique is used. Other studies 11,30 reached the same conclusions when dealing with individuated finger movements.…”

Section: Discussionsupporting

confidence: 73%

The Effects of Weight and Inertia of the Prosthesis on the Sensitivity of Electromyographic Pattern Recognition in Relax State

Cipriani¹,

Controzzi²,

Kanitz³

et al. 2012

JPO Journal of Prosthetics and Orthotics

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For transradial amputees, the muscles in the residual forearm physiologically used for flexing/extending the hand fingers are the most appropriate targets for multifingered prostheses control. However, once the prosthetic socket is manufactured and fitted on the residual forearm, the electromyographic (EMG) signals recorded from the residual limb might not be originated only by the intention of performing finger movements but also by the muscular activity needed to sustain the prosthesis itself. In this work, we show that in eight healthy subjects wearing a prosthetic socket emulator, 1) variations in the weight of the prosthesis and 2) upper arm movements significantly influence the robustness of a traditional classifier based on a k-nn algorithm, causing a significant drop in performance. We demonstrate in simulated conditions that traditional pattern recognition does not allow the separation of the effects of the weight of the prosthesis because a surface recorded EMG pattern due only to the lifting or moving of the prosthesis is misclassified into a hand control movement. This suggests that a robust classifier should add to myoelectric signals, inertial transducers like multi-axes position, acceleration sensors, or sensors able to monitor the interaction forces between the socket and the end-effector. ( J Prosthet Orthot. 2012;24:86Y92.)KEY INDEXING TERMS: upper limb prosthetics, pattern recognition, myoelectric control T o myoelectrically control a multifingered dexterous prosthesis, such as the recently marketed RSL Steeper BeBionic, 1 the iLimb, 2 or research prototypes like the SmartHand, 3 VU Hand, 4 or the DARPA RP 2009 Intrinsic Hand, 5 it is necessary to map electromyographic (EMG) signals corresponding to different muscle contractions to the different existing degrees of freedom (DoFs) of the hand using a suitable algorithm. Myoelectric control techniques can be divided into two categories: nonYpattern recognition based and pattern recognition based. 6,7 NonYpattern recognition control includes proportional control, threshold control, onset analysis, and finite state machines. These schemes have a simple structure and have been mostly deployed in on/off or proportional control. In particular, in proportional control, the strength of muscle contractions controls the prosthesis speed or force. This type of control scheme has received widespread clinical acceptance but provides reduced functionality, typically limited to only one or two DoFs. In research laboratories, sophisticated algorithms implement pattern recognition. 6 This is based on the condition that amputees can voluntarily activate repeatable and distinct EMG signal patterns for each class of motion, which, in turn, can be mapped to physiologically appropriate prosthesis commands.A multitude of groups have implemented and designed controllers using different combinations of extracted features and classification methods (for a review of the EMG processing techniques, refer to the work by Oskoei and Hu 7 ) showing the feasibility of controllin...

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Section: Discussionsupporting

confidence: 73%

The Effects of Weight and Inertia of the Prosthesis on the Sensitivity of Electromyographic Pattern Recognition in Relax State

Cipriani¹,

Controzzi²,

Kanitz³

et al. 2012

JPO Journal of Prosthetics and Orthotics

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“…Also, almost all of the current works only deals with small number of classes (less than 10) [11,[17][18][19][20]23,29,30]. Only an extremely small number of studies have reported classification results for more than 10 classes but they require large number of electrodes [21,[31][32][33]. However, this option may not be possible with an amputee.…”

Section: Related Workmentioning

confidence: 99%

“…At the same time, it is also inconvenient for healthy people to wear many electrodes. Works by Tenore et al [21] Kanitz et al [33] show that accurate classification of 12 finger motions is possible but they also face the disadvantage of having very large number of electrodes. In contrast, our work involves a smaller number of electrodes on the forearm which contributes to reducing equipment costs and preparation time, hence increases the usability of the system.…”

Section: Classification Of 17 Voluntary Movementsmentioning

confidence: 99%

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

et al. 2017

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Featured Application: This work is intended for the development of myoelectric prosthetic hand systems. Furthermore, the outcome of this study may also benefit other electromyographic based human-machine interfaces. Abstract:The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.

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“…Proportional EMG control [20] and pattern recognition based EMG control [21] of prosthetics are paradigms also used in orthotics. Prosthetic EMG control of individual finger motions [22], [23] and prosthetic EMG control which determines force and grasp type [24] have been explored. Control optimization studies have informed both signal processing [25], [26] and control hierarchy [27] of prostheses.…”

Section: Introductionmentioning

confidence: 99%

EMG pattern classification to control a hand orthosis for functional grasp assistance after stroke

Meeker

Park

Bishop

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

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Wearable orthoses can function both as assistive devices, which allow the user to live independently, and as rehabilitation devices, which allow the user to regain use of an impaired limb. To be fully wearable, such devices must have intuitive controls, and to improve quality of life, the device should enable the user to perform Activities of Daily Living. In this context, we explore the feasibility of using electromyography (EMG) signals to control a wearable exotendon device to enable pick and place tasks. We use an easy to don, commodity forearm EMG band with 8 sensors to create an EMG pattern classification control for an exotendon device. With this control, we are able to detect a user's intent to open, and can thus enable extension and pick and place tasks. In experiments with stroke survivors, we explore the accuracy of this control in both non-functional and functional tasks. Our results support the feasibility of developing wearable devices with intuitive controls which provide a functional context for rehabilitation.

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Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm

Cited by 17 publications

References 12 publications

The Effects of Weight and Inertia of the Prosthesis on the Sensitivity of Electromyographic Pattern Recognition in Relax State

The Effects of Weight and Inertia of the Prosthesis on the Sensitivity of Electromyographic Pattern Recognition in Relax State

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

EMG pattern classification to control a hand orthosis for functional grasp assistance after stroke

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