MYO Armband for physiotherapy healthcare: A case study using gesture recognition application

Sathiyanarayanan, Mithileysh; Rajan, Sharanya

doi:10.1109/comsnets.2016.7439933

Cited by 100 publications

(56 citation statements)

References 5 publications

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“…Researchers have noted that gesture classification with Myo has real-world application and benefits (Kaur et al 2016), showing that physiotherapy patients often exhibit much higher levels of satisfaction when interfacing via EMG and receiving digital feedback (Sathiyanarayanan and Rajan 2016). Likewise in the medical field, Myo has shown to be competitively effective with far more expensive methods of non-invasive electromyography in the rehabilitation of amputation patients (Abduo and Galster 2015), and following this, much work has explored the application of gesture classification for the control of a robotic hand (Ganiev et al 2016;Tatarian et al 2018).…”

Section: Emg Gesture Classification and Calibrationmentioning

confidence: 99%

Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning

Kobylarz

Bird

Faria

et al. 2020

J Ambient Intell Human Comput

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In this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of 'thumbs up', 'thumbs down', and 'relax' in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a '20 Questions' game.

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Section: Emg Gesture Classification and Calibrationmentioning

confidence: 99%

Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning

Kobylarz

Bird

Faria

et al. 2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…3, consists of the following components: 1 -robot; 2connecting cables; 3 -robot controller, 4 -teach pendant (KCP -KUKA Control Panel), software, options and accessories. All motor units and current-carrying cables are protected against dirt and moisture beneath screwed on cover plates [6].…”

Section: Kuka Kr15 Industrial Robotmentioning

confidence: 99%

Applications based on electromyography sensors

et al. 2019

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This paper presents our work regarding two different applications that use the electromyography sensors incorporated in the Thalmic Labs Myo Armband. The first application is about the HumanMachine Interface (HMI) for controlling an industrial robot bycreating the environment for the user to control a robot’s gripper position just by moving his arm. The second application refers to the real time control of a tracked mobile robot that is built with the Arduino development board. For each application thesystem design and the experimental results are presented.

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“…In this work we capture muscle signals and inertial movement data using the Thalmic MYO armband, a wearable device shown in Figure 3 and described in [8]. The MYO Fig.…”

Section: Automatic Recognition Of Irish Sign Languagementioning

confidence: 99%

Recognising Irish Sign Language Using Electromyography

Galea

Smeaton

2019

2019 International Conference on Content-Based Multimedia Indexing (CBMI)

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Sign language is the non-verbal communication used by people with hearing and speaking impairments. The automatic recognition of sign languages is usually based on video analysis of the signer though this is difficult when considering different light levels or the surrounding environment. The work in this paper uses electromyography (EMG) and focuses on letters of the Irish Sign Language (ISL) alphabet. EMG is the recording of the electrical activity produced to stimulate movement in the skeletal muscles. We capture muscle signals and inertial movement data using the Thalmic MYO armband and, in real time, recognise the ISL alphabet. Our implementation is based on signal processing, feature extraction and machine learning. The only input required to translate the ISL gestures are EMG and movement data, thus our approach is usable in scenarios where using video for automatic recognition video is not possible.

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MYO Armband for physiotherapy healthcare: A case study using gesture recognition application

Cited by 100 publications

References 5 publications

Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning

Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning

Applications based on electromyography sensors

Recognising Irish Sign Language Using Electromyography

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