Influence of Force Tremor on Mechanomyographic Signals Recorded with an Accelerometer and a Condenser Microphone during Measurement of Agonist and Antagonist Muscles in Voluntary Submaximal Isometric Contractions

Kim, Tae-Kwang; Shimomura, Yoshihiro; Iwanaga, Koichi; Katsuura, Tetsuo

doi:10.2114/jpa2.27.33

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…In other words, Flexion and Extension show a similar connectivity topology, and Pronation and Supination present the same phenomena. This could be a result of the fact that MMG captures not only the contraction of the agonist muscles but also the relaxation of the antagonist muscles, both simultaneously occurring during the task and found to be present at low frequencies [62]. Interestingly, similar topologies were found between Extension and Pronation across all frequency components for each amputee participant.…”

Section: B Muscle Connectivity Networkmentioning

confidence: 80%

Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Castillo

Vaidyanathan

Atashzar

2022

IEEE Trans. Biomed. Eng.

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Functional connectivity is a critical concept in describing synergistic muscle synchronization for the execution of complex motor tasks. Muscle synchronization is typically derived from decomposition of intermuscular coherence (IMC) at different frequency bands through electromyography (EMG) signal analysis, which potentially limits out-of-clinic applications. In this investigation, we introduce muscle network analysis to assess the coordination and functional connectivity of muscles based on mechanomyography (MMG). We focus on a targeted group of muscles vital for activities of daily living (ADLs) in the upper-limb. Functional muscle networks are evaluated for ten able-bodied participants and three upper-limb amputees. Muscle activity was acquired from a custom-made wearable armband of MMG sensors placed over four superficial muscles around the forearm (flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. Muscle connectivity analysis at multiple frequency bands shows significant topographical differences across gestures for low ( < 5Hz) and high (> 12 Hz) muscle activation frequencies as well as observable differences in coherence between amputee and non-amputee subjects. Results demonstrate MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic synchronization of upper-limb muscles in complex movement tasks. The new physiological modality provides key insights into neural circuitry of motor coordination. Findings further offer the concomitant outcomes of demonstrating feasibility of MMG to map muscle coherence from a neurophysiological perspective and providing a mechanistic basis for its translation in humanrobot interface.

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Section: B Muscle Connectivity Networkmentioning

confidence: 80%

Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Castillo

Vaidyanathan

Atashzar

2022

IEEE Trans. Biomed. Eng.

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“…2) Force and Torque Evaluation: Three studies [31], [34], [87] were associated with force in MMG signals (Table VI). Dillon et al [34], for example, investigated MMG RMS amplitudes and MPF versus isometric force relationships detected from the RF muscle using a laser displacement sensor in the perpendicular and transverse axes.…”

Section: Muscle Characterizations Using Mmg 1) Fatigue and Strengtmentioning

confidence: 99%

“…Further, Youn et al [31] demonstrated the feasibility of using MMG signals to estimate the isometric elbow flexion force for the BB and brachioradialis (BRD) muscles based on an artificial neural network. In another work, Kim et al [87] discussed the influence of force tremor on MMG signals recorded by an accelerometer and a microphone in the measurement of agonist and antagonist muscles during voluntary sub-maximal isometric contractions.…”

Section: Muscle Characterizations Using Mmg 1) Fatigue and Strengtmentioning

confidence: 99%

Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review

et al. 2013

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Mechanomyography (MMG) is extensively used in the research of sensor development, signal processing, characterization of muscle activity, development of prosthesis and/or switch control, diagnosis of neuromuscular disorders, and as a medical rehabilitation tool. Despite much existing MMG research, there has been no systematic review of these. This paper aims to determine the current status of MMG in sensor development, related signal processing, and applications. Six electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. From a total of 175 citations, 119 were selected for full-text evaluation and 86 potential studies were identified for further analysis. This systematic review initially reveals that the development of accelerometers for MMG is still in the initial stage. Another important finding of this paper is that sensor placement location on muscles may influence the MMG signal. In addition, we observe that the majority of research processes MMG signals using wavelet transform. Time/frequency domain analysis of MMG signals provides useful information to examine muscle. In addition, we find that MMG may be applied to diagnose muscle conditions, to control prosthesis and/or switch devices, to assess muscle activities during exercises, to study motor unit activity, and to identify the type of muscle fiber. Finally, we find that the majority of the studies use accelerometers as sensors for MMG measurements. We also observe that currently MMG-based rehabilitation is still in a nascent stage. In conclusion, we recommend further improvements of MMG in the areas of sensor development, particularly on accelerometers, and signal processing aspects, as well as increasing future applications of the technique in prosthesis and/or switch control, clinical practices, and rehabilitation.

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“…The cylindrical hole through the PCB acted as a hollow chamber between the microphone diaphragm and the skin surface. This setup is shown in Fig 2A and is similar to the one shown in Kim et al [19]. An IMU (Model: LSM9DS0, STMicroelectronics, Geneva, Switzerland) was placed over microphone unit #8, which was positioned over the middle of exterior wrist, or, in other words, at the top of the entire sensor arrangement over the wrist, as illustrated in Fig 1. Our prototype utilized only one IMU to record limb movement and it was not in skin contact with the wrist.…”

Section: Introductionmentioning

confidence: 99%

Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist

Siddiqui

Chan

2020

PLoS ONE

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To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion. OPEN ACCESSCitation: Siddiqui N, Chan RHM (2020) Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist. PLoS ONE 15(1): e0227039. https://doi.org/10.

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Influence of Force Tremor on Mechanomyographic Signals Recorded with an Accelerometer and a Condenser Microphone during Measurement of Agonist and Antagonist Muscles in Voluntary Submaximal Isometric Contractions

Cited by 6 publications

References 35 publications

Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review

Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist

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