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PMC:7796058 / 52833-53668 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
387 71-76 Species denotes Human Tax:9606
388 230-238 Disease denotes coughing MESH:D003371
389 611-619 Disease denotes COVID-19 MESH:C000657245
390 717-725 Disease denotes coughing MESH:D003371
391 775-783 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T5 230-238 Phenotype denotes coughing http://purl.obolibrary.org/obo/HP_0012735
T6 717-725 Phenotype denotes coughing http://purl.obolibrary.org/obo/HP_0012735

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T378 0-70 Sentence denotes Camera stream processing is a popular and quick way to detect objects.
T379 71-184 Sentence denotes Human behaviors and actions can be detected as objects from the video frames using a trained deep learning model.
T380 185-435 Sentence denotes For the detection of risky behaviors such as coughing, hugging, handshaking, and doorknob touching, the You Only Look Once version3 (YOLOv3) which is suitable for real-time behavior detection for online video streams, was trained and applied [63,64].
T381 436-558 Sentence denotes This library classifies and localizes detected objects in one step with a speed of faster than 40 frames per second (FPS).
T382 559-640 Sentence denotes We considered two main types of risky behaviors for COVID-19 indoor transmission:
T383 641-727 Sentence denotes Group risky behaviors (e.g., hugging) and individual risky behaviors (e.g., coughing).
T384 728-835 Sentence denotes Figure 12 illustrates how to train a model for COVID-19 transmission risky behavior detection using YOLOv3.