OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929414142427136 |
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| author | Cañellas, Manuel Lage Nguyen, Le Mukherjee, Anirban Casado, Constantino Álvarez Wu, Xiaoting Susarla, Praneeth Sharifipour, Sasan Jayagopi, Dinesh B. López, Miguel Bordallo |
| author_facet | Cañellas, Manuel Lage Nguyen, Le Mukherjee, Anirban Casado, Constantino Álvarez Wu, Xiaoting Susarla, Praneeth Sharifipour, Sasan Jayagopi, Dinesh B. López, Miguel Bordallo |
| contents | In the domain of non-contact biometrics and human activity recognition, the lack of a versatile, multimodal dataset poses a significant bottleneck. To address this, we introduce the Oulu Multi Sensing (OMuSense-23) dataset that includes biosignals obtained from a mmWave radar, and an RGB-D camera. The dataset features data from 50 individuals in three distinct poses -- standing, sitting, and lying down -- each featuring four specific breathing pattern activities: regular breathing, reading, guided breathing, and apnea, encompassing both typical situations (e.g., sitting with normal breathing) and critical conditions (e.g., lying down without breathing). In our work, we present a detailed overview of the OMuSense-23 dataset, detailing the data acquisition protocol, describing the process for each participant. In addition, we provide, a baseline evaluation of several data analysis tasks related to biometrics, breathing pattern recognition and pose identification. Our results achieve a pose identification accuracy of 87\% and breathing pattern activity recognition of 83\% using features extracted from biosignals. The OMuSense-23 dataset is publicly available as resource for other researchers and practitioners in the field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_06137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis Cañellas, Manuel Lage Nguyen, Le Mukherjee, Anirban Casado, Constantino Álvarez Wu, Xiaoting Susarla, Praneeth Sharifipour, Sasan Jayagopi, Dinesh B. López, Miguel Bordallo Computer Vision and Pattern Recognition In the domain of non-contact biometrics and human activity recognition, the lack of a versatile, multimodal dataset poses a significant bottleneck. To address this, we introduce the Oulu Multi Sensing (OMuSense-23) dataset that includes biosignals obtained from a mmWave radar, and an RGB-D camera. The dataset features data from 50 individuals in three distinct poses -- standing, sitting, and lying down -- each featuring four specific breathing pattern activities: regular breathing, reading, guided breathing, and apnea, encompassing both typical situations (e.g., sitting with normal breathing) and critical conditions (e.g., lying down without breathing). In our work, we present a detailed overview of the OMuSense-23 dataset, detailing the data acquisition protocol, describing the process for each participant. In addition, we provide, a baseline evaluation of several data analysis tasks related to biometrics, breathing pattern recognition and pose identification. Our results achieve a pose identification accuracy of 87\% and breathing pattern activity recognition of 83\% using features extracted from biosignals. The OMuSense-23 dataset is publicly available as resource for other researchers and practitioners in the field. |
| title | OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.06137 |