Assessment System for Child Head Injury from Falls Based on Neural Network Learning
Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7896 |
| _version_ | 1827723529239396352 |
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| author | Ziqian Yang Baiyu Tsui Zhihui Wu |
| author_facet | Ziqian Yang Baiyu Tsui Zhihui Wu |
| author_sort | Ziqian Yang |
| collection | DOAJ |
| description | Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven. |
| first_indexed | 2024-03-10T22:02:01Z |
| format | Article |
| id | doaj.art-1a78b76c9bc14f388dd0e8d0ff2d7468 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:02:01Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1a78b76c9bc14f388dd0e8d0ff2d74682023-11-19T12:55:44ZengMDPI AGSensors1424-82202023-09-012318789610.3390/s23187896Assessment System for Child Head Injury from Falls Based on Neural Network LearningZiqian Yang0Baiyu Tsui1Zhihui Wu2College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, ChinaToddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven.https://www.mdpi.com/1424-8220/23/18/7896head injury from fallsdeep learningOpen PoseLSTM3D transform model |
| spellingShingle | Ziqian Yang Baiyu Tsui Zhihui Wu Assessment System for Child Head Injury from Falls Based on Neural Network Learning Sensors head injury from falls deep learning Open Pose LSTM 3D transform model |
| title | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
| title_full | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
| title_fullStr | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
| title_full_unstemmed | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
| title_short | Assessment System for Child Head Injury from Falls Based on Neural Network Learning |
| title_sort | assessment system for child head injury from falls based on neural network learning |
| topic | head injury from falls deep learning Open Pose LSTM 3D transform model |
| url | https://www.mdpi.com/1424-8220/23/18/7896 |
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