Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors
Electromyography (EMG) sensors produce a stream of data at rates that can easily saturate a low-energy wireless link such as Bluetooth Low Energy (BLE), especially if more than a few EMG channels are being transmitted simultaneously. Compressing data can thus be seen as a nice feature that could all...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/15/5160 |
| _version_ | 1797411328679215104 |
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| author | Giorgio Biagetti Paolo Crippa Laura Falaschetti Ali Mansour Claudio Turchetti |
| author_facet | Giorgio Biagetti Paolo Crippa Laura Falaschetti Ali Mansour Claudio Turchetti |
| author_sort | Giorgio Biagetti |
| collection | DOAJ |
| description | Electromyography (EMG) sensors produce a stream of data at rates that can easily saturate a low-energy wireless link such as Bluetooth Low Energy (BLE), especially if more than a few EMG channels are being transmitted simultaneously. Compressing data can thus be seen as a nice feature that could allow both longer battery life and more simultaneous channels at the same time. A lot of research has been done in lossy compression algorithms for EMG data, but being lossy, artifacts are inevitably introduced in the signal. Some artifacts can usually be tolerable for current applications. Nevertheless, for some research purposes and to enable future research on the collected data, that might need to exploit various and currently unforseen features that had been discarded by lossy algorithms, lossless compression of data may be very important, as it guarantees no extra artifacts are introduced on the digitized signal. The present paper aims at demonstrating the effectiveness of such approaches, investigating the performance of several algorithms and their implementation on a real EMG BLE wireless sensor node. It is demonstrated that the required bandwidth can be more than halved, even reduced to 1/4 on an average case, and if the complexity of the compressor is kept low, it also ensures significant power savings. |
| first_indexed | 2024-03-09T04:43:31Z |
| format | Article |
| id | doaj.art-f8a61f8ecbdd4d94a8c21bf7a9097e6c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:43:31Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f8a61f8ecbdd4d94a8c21bf7a9097e6c2023-12-03T13:18:32ZengMDPI AGSensors1424-82202021-07-012115516010.3390/s21155160Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG SensorsGiorgio Biagetti0Paolo Crippa1Laura Falaschetti2Ali Mansour3Claudio Turchetti4DII—Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, ItalyDII—Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, ItalyDII—Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, ItalyLab-STICC—Laboratoire des Sciences et Techniques de l’information de la Communication et de la Connaissance, UMR 6285 CNRS, ENSTA Bretagne, 2 Rue F. Verny, 29806 Brest, FranceDII—Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, ItalyElectromyography (EMG) sensors produce a stream of data at rates that can easily saturate a low-energy wireless link such as Bluetooth Low Energy (BLE), especially if more than a few EMG channels are being transmitted simultaneously. Compressing data can thus be seen as a nice feature that could allow both longer battery life and more simultaneous channels at the same time. A lot of research has been done in lossy compression algorithms for EMG data, but being lossy, artifacts are inevitably introduced in the signal. Some artifacts can usually be tolerable for current applications. Nevertheless, for some research purposes and to enable future research on the collected data, that might need to exploit various and currently unforseen features that had been discarded by lossy algorithms, lossless compression of data may be very important, as it guarantees no extra artifacts are introduced on the digitized signal. The present paper aims at demonstrating the effectiveness of such approaches, investigating the performance of several algorithms and their implementation on a real EMG BLE wireless sensor node. It is demonstrated that the required bandwidth can be more than halved, even reduced to 1/4 on an average case, and if the complexity of the compressor is kept low, it also ensures significant power savings.https://www.mdpi.com/1424-8220/21/15/5160EMGlossless compressionBluetooth Low EnergyFLACentropy codingwireless sensors |
| spellingShingle | Giorgio Biagetti Paolo Crippa Laura Falaschetti Ali Mansour Claudio Turchetti Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors Sensors EMG lossless compression Bluetooth Low Energy FLAC entropy coding wireless sensors |
| title | Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors |
| title_full | Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors |
| title_fullStr | Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors |
| title_full_unstemmed | Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors |
| title_short | Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors |
| title_sort | energy and performance analysis of lossless compression algorithms for wireless emg sensors |
| topic | EMG lossless compression Bluetooth Low Energy FLAC entropy coding wireless sensors |
| url | https://www.mdpi.com/1424-8220/21/15/5160 |
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