| Literature DB >> 34960465 |
Mateusz Szumilas1, Michał Władziński1, Krzysztof Wildner1.
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.Entities:
Keywords: convolutional neural network; hand gesture recognition; human-machine interface; mechanomyography; piezoelectric sensor; prosthetic control; vibration sensor
Mesh:
Year: 2021 PMID: 34960465 PMCID: PMC8705252 DOI: 10.3390/s21248380
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576