ArticleSensors (Basel, Switzerland)2021
A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Fatigue analysis of the flexor digitorum superficialis muscle using mechanomyogram during moderate-intensity intermittent dynamic tasks.Scientific reports · 2025Article
- MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE.Sensors (Basel, Switzerland) · 2025Article
- AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A Neural Network Approach for Inertial Measurement Unit-Based Estimation of Three-Dimensional Spinal Curvature.Sensors (Basel, Switzerland) · 2023Article
- A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation.Sensors (Basel, Switzerland) · 2023Article
- Lower Limb Exoskeleton Sensors: State-of-the-Art.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human-machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor's functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.