A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6319 |
| _version_ | 1797493239481106432 |
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| author | Anany Dwivedi Helen Groll Philipp Beckerle |
| author_facet | Anany Dwivedi Helen Groll Philipp Beckerle |
| author_sort | Anany Dwivedi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:17:10Z |
| format | Article |
| id | doaj.art-7b79d417ece3454ca4a871199810dd81 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:17:10Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7b79d417ece3454ca4a871199810dd812023-11-23T14:06:29ZengMDPI AGSensors1424-82202022-08-012217631910.3390/s22176319A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention DecodingAnany Dwivedi0Helen Groll1Philipp Beckerle2Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, GermanyChair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, GermanyChair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, GermanyHumans 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.https://www.mdpi.com/1424-8220/22/17/6319myographydata fusionhuman-intention decodingmuscle–machine interfaces |
| spellingShingle | Anany Dwivedi Helen Groll Philipp Beckerle A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding Sensors myography data fusion human-intention decoding muscle–machine interfaces |
| title | A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding |
| title_full | A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding |
| title_fullStr | A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding |
| title_full_unstemmed | A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding |
| title_short | A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding |
| title_sort | systematic review of sensor fusion methods using peripheral bio signals for human intention decoding |
| topic | myography data fusion human-intention decoding muscle–machine interfaces |
| url | https://www.mdpi.com/1424-8220/22/17/6319 |
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