The Merits of Dynamic Data Acquisition for Realistic Myocontrol

Gigli, Andrea; Gijsberts, Arjan; Castellini, Claudio

doi:10.3389/fbioe.2020.00361

Cited by 13 publications

(25 citation statements)

References 42 publications

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“…A good deal of upper limb movement pattern recognition techniques based on sEMG have been carried out in offline environment with satisfactory results. However, the system with good offline training performance does not always have excellent real-time performance (Ortiz-Catalan et al, 2015 ; Vujaklija et al, 2017 ; Gigli et al, 2020 ). This is because offline performance is obtained by processing offline data of sEMG.…”

Section: Hand Gesture Recognition Challengesmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

102

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Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

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Section: Hand Gesture Recognition Challengesmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

102

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“…Open-loop dynamic data acquisition (OL-DA) was based on the dynamic data acquisition procedure presented in [3]. Subjects performed the target hand gestures while moving their arm in a helical trajectory spanning the reachable space.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…Solutions to this problem involve acquiring training data for the desired hand posture in multiple areas of the reachable space, i.e., in multiple arm configurations [2]. A possibility is to move the arm during the data acquisition to reduce the amount of time and the physical effort required to cover the reachable space [3].…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Acquisition of Training Data Improves Myocontrol of a Prosthetic Hand

Brusamento

Gigli

Meattini

et al. 2021

Biosystems &Amp; Biorobotics

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Modern myocontrol of prosthetic upper limbs employs pattern recognition models to map the muscular activity of the residual limb onto control commands for the prosthesis. The quality of pattern-recognition-based myocontrol, and that of the resulting user experience, depend on the quality of the data used to build the model. Surprisingly, the prosthetic community has so far given marginal attention to this aspect, especially as far as the involvement of the user in the data acquisition process is concerned. This work shows that closed-loop data acquisition strategies using a feedbackaided approach outperform the standard open-loop acquisition by helping users detect areas of the input space that need more training data. The experiment was conducted in realistic settings, involving one prosthetic hand and tasks inspired by activities of daily living.

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“…the estimation of user intent from muscle activity in the users residual limb via the electromyogram [1]. A growing body of evidence shows that offline performance in myoelectric control has weak correlation to online control [2], [3]. Accordingly, there is increased demand for user-in-the-loop tests of new and novel systems, necessitating increased participant involvement in system development.…”

Section: Introductionmentioning

confidence: 99%

“…Closed-loop myoelectrics systems allow users to improve performance. This occurs across various control schemes including direct control [4], pattern recognition [5], [6], [4], [7] and regression [8], [3]. This apparent learning has been investigated in single sessions [8], [3], in multi-day studies [9], [4], [10], [6], [7] and in multi-week studies [5], [10].…”

Section: Introductionmentioning

confidence: 99%

A Network-Enabled Myoelectric Platform for Prototyping Research Outside of the Lab

Dyson

Olsen

Dupan

2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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We present a network-enabled myoelectric platform for performing research outside of the laboratory environment. A low-cost, flexible, modular design based on common Internet of Things connectivity technology allows home-based research to be piloted. An outline of the platform is presented followed by technical results obtained from ten days of home-based tests with three participants. Results show the system enabled collection of close to 12,000 trials during around 28 cumulative hours of use. Home-based testing of multiple participants in parallel offers efficiency gains and provides a intuitive route toward long-term testing of upper-limb prosthetic devices in more naturalistic settings. Clinical relevance-In-home myoelectric training reduces clinician time. Network-enabled systems with back-end dashboards allow clinicians to monitor patients myoelectric ability over time and will provide a new way of accessing information about how upper-limb prosthetics are commonly used.

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The Merits of Dynamic Data Acquisition for Realistic Myocontrol

Cited by 13 publications

References 42 publications

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Closed-Loop Acquisition of Training Data Improves Myocontrol of a Prosthetic Hand

A Network-Enabled Myoelectric Platform for Prototyping Research Outside of the Lab

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