| DOI | Resolve DOI: https://doi.org/10.1109/SAS54819.2022.9881344 |
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| Author | Search for: Habashy, Karim1; Search for: Valdes, Julio1; Search for: Cohen-McFarlane, Madison; Search for: Xi, Pengcheng1; Search for: Wallace, Bruce; Search for: Goubran, Rafik; Search for: Knoefel, Frank |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Funder | Search for: National Research Council |
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| Format | Text, Article |
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| Conference | 2022 IEEE Sensors Applications Symposium (SAS), August 1-3, 2022, Sundsvall, Sweden |
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| Physical description | 6 p. |
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| Subject | aging in place; independent living; smart sensing; audio data analysis; vision transformer; deep learning; computational modeling; transfer learning; smart homes; aging; transformers; data models; recording |
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| Abstract | A variety of technologies can support aging in place, including smart home sensing that can enable independent living through real-time data analysis. In this work, we study cough sound analysis as the cough is a key symptom of many respiratory illnesses and conditions. Based on a data set of cough recordings, we propose a two-pronged approach: the first leverages unsupervised learning to compute intrinsic dimensions of the data and maps raw data for visualizations, and the second uses the insight to train machine learning models through transfer learning on Vision Transformer models. Data augmentation approaches are implemented to improve the performance of the models and our top-performing model achieves an F1-score of 0.804. This study suggests the feasibility of using smart sensing and deep learning for gaining insights into the health of older adults. |
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| Publication date | 2022-08-01 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | a58e380a-c1da-41df-a0da-5d383eaaf213 |
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| Record created | 2022-09-26 |
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| Record modified | 2022-09-28 |
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