Surface Electromyography as a Natural Human–Machine Interface: A Review

Zheng, Mingde; Crouch, Michael S.; Eggleston, Michael S.

doi:10.1109/jsen.2022.3165988

Cited by 38 publications

(17 citation statements)

References 152 publications

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“…During the last decades, most of the development has lead in the direction of introducing powered prostheses where movement control is decoded from surface electromyogram (sEMG) using myoelectric pattern recognition (Hudgins, Parker and Scott, 1993; Englehart and Hudgins, 2003; Zheng, Crouch and Eggleston, 2021). Further work has also been performed to improve control resolution and reliability through Targeted Muscle Reinnervation (TMR), where nerves in the remaining limb are innervated into existing musculature to increase the number of electromyogram channels and improve prosthesis controllability (Kuiken et al ., 2004).…”

Section: Introductionmentioning

confidence: 99%

Neurostimulation Artifact Removal for Implantable Sensors Improves Signal Clarity and Decoding of Motor Volition

Earley

Berneving

Zbinden

et al. 2022

Preprint

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As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction and ε-Normalized Least Mean Squares, and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress towards bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.

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Section: Introductionmentioning

confidence: 99%

Neurostimulation Artifact Removal for Implantable Sensors Improves Signal Clarity and Decoding of Motor Volition

Earley

Berneving

Zbinden

et al. 2022

Preprint

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show abstract

“…They have high accuracy in static gesture recognition and can provide rich data on position, but can be cumbersome and uncomfortable to wear over an extended period of time [ 2 ]. Electromyography (EMG) leaves the hands free but suffers from signal drift for a number of reasons including sweat, fatigue, variation of muscle force, and shifting electrodes [ 3 , 6 , 7 ]. While video based solutions leave the body unencumbered by wearables their challenges include changes in lighting, issues segmenting the hands from background clutter, and occluded lines of sight [ 3 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Typically, there is some period of time given to transition between gestures, which is flagged with a ‘Null’ label. This allows for three primary sources of error in labelling (i) users do not instantaneously transition between gestures and as a result many of the ‘Null’ samples will be members of actual gesture classes, (ii) user fatigue can lead to variation in production of gestures confounding a trained model [ 7 , 8 , 14 , 15 ], (iii) users may inadvertently make incorrect gestures [ 8 , 16 ]. The solutions presented in this paper focus on the first two issues.…”

Section: Introductionmentioning

confidence: 99%

“…It has been observed in a variety of studies that the accuracy of inter-set validation even on the same day tends to be far lower than in-set validation [ 8 , 10 , 11 , 12 ]. This in part can be attributed to signal drift, sensors shifting on users’ arms, fatigue, or even variations in user position leading to changes in sensor readings [ 3 , 4 , 7 , 9 , 10 , 12 ]. These changes violate a fundamental assumption in machine learning that data is stationary over time, i.e., the mean and standard deviation can be known a priori from a sample and will not change over time [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Many of the proposed solutions are dependent on deep learning. While deep learning can achieve high accuracy and recognize large numbers of gestures; it requires a significant quantity of data and high computational power [ 4 , 7 ]. This may represent a challenge both for edge computing devices and user patience.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users

Dankovich

Vaughn-Cooke

Bergbreiter

2022

Sensors

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Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject’s abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power.

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Highly Stable Ladder‐Type Conjugated Polymer Based Organic Electrochemical Transistors for Low Power and Signal Processing‐Free Surface Electromyogram Triggered Robotic Hand Control

Zhou,

Wu,

Tam

et al. 2023

Adv Funct Materials

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Organic electrochemical transistors (OECTs) based complementary inverters have been considered as promising candidates in electrophysiological amplification, owing to their low power consumption, and high gain. To create complementary inverters, it is important to use highly stable p‐type and n‐type polymers with well‐balanced current. In this study, the electrochemical stability of p‐type ladder‐conjugated polymer‐based OECT is improved through an annealing process that maintains its doped‐state drain current from 76% to 105% after 4,500 cycles in ambient environment. Next an OECT‐based complementary inverter made from p‐type and n‐type ladder‐conjugated polymers (PBBTL and BBL) that possess ultra‐low power consumption (≈170 nW), high gain (67 V/V), and high noise margin (92%) with full rail‐to‐rail swing, is presented. Furthermore, its potential for amplifying the envelope of surface electromyography (EMG) for robotic hand control is demonstrated. The high variation in the output (0.35 V) allows the amplified EMG signals to be directly captured by a commercial analog‐to‐digital converter, which in turn controls the robot hand to grasp different objects with low delay and low noise. These results demonstrate the capability of OECT inverter‐based amplifier in future signal processing‐free human‐machine interface, particularly useful for prosthetic control and gesture control applications.

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Surface Electromyography as a Natural Human–Machine Interface: A Review

Cited by 38 publications

References 152 publications

Neurostimulation Artifact Removal for Implantable Sensors Improves Signal Clarity and Decoding of Motor Volition

Neurostimulation Artifact Removal for Implantable Sensors Improves Signal Clarity and Decoding of Motor Volition

Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users

Highly Stable Ladder‐Type Conjugated Polymer Based Organic Electrochemical Transistors for Low Power and Signal Processing‐Free Surface Electromyogram Triggered Robotic Hand Control

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