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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.13152 |
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| _version_ | 1866910066803736576 |
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| author | Gabriel, Paolo Rehani, Peter Troy, Tyler Wyatt, Tiffany Choma, Michael Singh, Narinder |
| author_facet | Gabriel, Paolo Rehani, Peter Troy, Tyler Wyatt, Tiffany Choma, Michael Singh, Narinder |
| contents | This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in the cloud for retrospective evaluation. The dataset, compiled in collaboration with 11 hospital partners, encompasses over 300 high-risk fall patients and over 1,000 days of inference, enabling applications such as fall detection and safety monitoring for vulnerable patient populations. To foster innovation and reproducibility, an anonymized subset of this dataset is publicly available. The AI system detects key components in hospital rooms, including individual presence and role, furniture location, motion magnitude, and boundary crossings. Performance evaluation demonstrates strong accuracy in object detection (macro F1-score = 0.92) and patient-role classification (F1-score = 0.98), as well as reliable trend analysis for the "patient alone" metric (mean logistic regression accuracy = 0.82 \pm 0.15). These capabilities enable automated detection of patient isolation, wandering, or unsupervised movement-key indicators for fall risk and other adverse events. This work establishes benchmarks for validating AI-driven patient monitoring systems, highlighting the platform's potential to enhance patient safety and care by providing continuous, data-driven insights into patient behavior and interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13152 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings Gabriel, Paolo Rehani, Peter Troy, Tyler Wyatt, Tiffany Choma, Michael Singh, Narinder Computer Vision and Pattern Recognition Artificial Intelligence This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in the cloud for retrospective evaluation. The dataset, compiled in collaboration with 11 hospital partners, encompasses over 300 high-risk fall patients and over 1,000 days of inference, enabling applications such as fall detection and safety monitoring for vulnerable patient populations. To foster innovation and reproducibility, an anonymized subset of this dataset is publicly available. The AI system detects key components in hospital rooms, including individual presence and role, furniture location, motion magnitude, and boundary crossings. Performance evaluation demonstrates strong accuracy in object detection (macro F1-score = 0.92) and patient-role classification (F1-score = 0.98), as well as reliable trend analysis for the "patient alone" metric (mean logistic regression accuracy = 0.82 \pm 0.15). These capabilities enable automated detection of patient isolation, wandering, or unsupervised movement-key indicators for fall risk and other adverse events. This work establishes benchmarks for validating AI-driven patient monitoring systems, highlighting the platform's potential to enhance patient safety and care by providing continuous, data-driven insights into patient behavior and interactions. |
| title | Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.13152 |