A Wearable, Open-Source, Lightweight Forcemyography Armband: On Intuitive, Robust Muscle-Machine Interfaces

Chapman, Jayden; Dwivedi, Anany; Liarokapis, Minas

doi:10.1109/iros51168.2021.9636345

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…Gesture recognition devices based on FMG sensors offer a wide range of sensor options, such as capacitive, [ 34 ] piezoresistive, [ 35 ] pneumatic‐based sensors, [ 36 ] piezoelectric, [ 37 ] and force sensitive resistors (FSRs). [ 38 ]…”

Section: Sensing Modalitiesmentioning

confidence: 99%

“…In this system, 15 features are extracted from the signals to use support‐vector machine (SVM) and k‐nearest neighbor (KNN) in classifying various letters. Chapman et al [ 38 ] designed an armband composed of six FSRs for HMI to decode four different gestures (pinch, power, tripod, extension) using linear discriminant analysis (LDA), SVM, and random forest (RF) and concluded that FSR‐based interfaces performed better in recognizing the grasp motion (accuracy of 91.2%) as compared to the sEMG‐based interfaces (accuracy of 84.6%). Despite the high performance of these mechanical sensing methods in the recognition of individual gestures, they are still incapable of acquiring the movement information of deep muscles.…”

Section: Sensing Modalitiesmentioning

confidence: 99%

“…Gesture recognition devices based on FMG sensors offer a wide range of sensor options, such as capacitive, [34] piezoresistive, [35] pneumatic-based sensors, [36] piezoelectric, [37] and force sensitive resistors (FSRs). [38] As shown in Figure 5, 4 Â 3 capacitive sensor arrays are sewn into the denim fabric using conductive wires. [39] The system can recognize gestures of varying complexity with an average accuracy of 99% with minimal training using hidden Markov model and dynamic time warping.…”

Section: Mechanical Sensingmentioning

confidence: 99%

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A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

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Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

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Section: Sensing Modalitiesmentioning

confidence: 99%

Section: Sensing Modalitiesmentioning

confidence: 99%

Section: Mechanical Sensingmentioning

confidence: 99%

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A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

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“…Using an instrumented armband has shown to be a valid approach to detect relevant changes in FMG signals [19,20] and allows for simplified sensor placement. Due to the lack of suitable commercially available solutions, a custom-built sensor band similar to the one used used by Xiao and Menon [19] was designed (Fig.…”

Section: Fmg Bandmentioning

confidence: 99%

Feasibility of force myography for the direct control of an assistive robotic hand orthosis

Gantenbein

Ahmadizadeh

Heeb

et al. 2023

Preprint

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Background: Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. Methods: In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined achieved classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. Results: We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4% up to 100%. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. Conclusion: The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

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“…From the available literature, it is evident that the user intention can be decoded to execute various tasks using different sensing modalities. Researchers have employed MuMIs for decoding hand gestures to control robotic hands or interaction with computer application [40][41][42][43], decoding continuous arm-hand motions [44][45][46], rehabilitation after strokes [47,48], and for games and entertainment [49]. Such MuMIs have also been employed in decoding walking patterns for an effective control of lower limb prosthesis [50,51], quantifying user fatigue during various tasks [52], and for decoding user intentions during collaborative tasks with robots [53,54].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Dwivedi

Groll

Beckerle

2022

Sensors

Self Cite

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Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle–machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user’s intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

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A Wearable, Open-Source, Lightweight Forcemyography Armband: On Intuitive, Robust Muscle-Machine Interfaces

Cited by 16 publications

References 29 publications

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Feasibility of force myography for the direct control of an assistive robotic hand orthosis

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

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