Classifying Participant Standing and Sitting Postures Using Channel State Information
Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of C...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/21/4500 |
| _version_ | 1797632018152947712 |
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| author | Oliver Custance Saad Khan Simon Parkinson |
| author_facet | Oliver Custance Saad Khan Simon Parkinson |
| author_sort | Oliver Custance |
| collection | DOAJ |
| description | Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand. |
| first_indexed | 2024-03-11T11:31:57Z |
| format | Article |
| id | doaj.art-aa7a9347f99642ca9e1c910b2d41f2f2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T11:31:57Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-aa7a9347f99642ca9e1c910b2d41f2f22023-11-10T15:01:39ZengMDPI AGElectronics2079-92922023-11-011221450010.3390/electronics12214500Classifying Participant Standing and Sitting Postures Using Channel State InformationOliver Custance0Saad Khan1Simon Parkinson2Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UKDepartment of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UKDepartment of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UKRecently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand.https://www.mdpi.com/2079-9292/12/21/4500CSIbehaviour biometricsLSTM-1DCNNconfusion matrixisolation chamber |
| spellingShingle | Oliver Custance Saad Khan Simon Parkinson Classifying Participant Standing and Sitting Postures Using Channel State Information Electronics CSI behaviour biometrics LSTM-1DCNN confusion matrix isolation chamber |
| title | Classifying Participant Standing and Sitting Postures Using Channel State Information |
| title_full | Classifying Participant Standing and Sitting Postures Using Channel State Information |
| title_fullStr | Classifying Participant Standing and Sitting Postures Using Channel State Information |
| title_full_unstemmed | Classifying Participant Standing and Sitting Postures Using Channel State Information |
| title_short | Classifying Participant Standing and Sitting Postures Using Channel State Information |
| title_sort | classifying participant standing and sitting postures using channel state information |
| topic | CSI behaviour biometrics LSTM-1DCNN confusion matrix isolation chamber |
| url | https://www.mdpi.com/2079-9292/12/21/4500 |
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