Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study
Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentat...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7426 |
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| author | Diletta Balta HsinHung Kuo Jing Wang Ilaria Giuseppina Porco Olga Morozova Manon Maitland Schladen Andrea Cereatti Peter Stanley Lum Ugo Della Croce |
| author_facet | Diletta Balta HsinHung Kuo Jing Wang Ilaria Giuseppina Porco Olga Morozova Manon Maitland Schladen Andrea Cereatti Peter Stanley Lum Ugo Della Croce |
| author_sort | Diletta Balta |
| collection | DOAJ |
| description | Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentation in the home may be used to estimate quantitative metrics predictive of movement disorders. This study explored if infants’ GM may be successfully evaluated in a familiar environment by processing the 3D trajectories of points of interest (PoI) obtained from recordings of a single commercial RGB-D sensor. The RGB videos were processed using an open-source markerless motion tracking method which allowed the estimation of the 2D trajectories of the selected PoI and a purposely developed method which allowed the reconstruction of their 3D trajectories making use of the data recorded with the depth sensor. Eight infants’ GM were recorded in the home at 3, 4, and 5 months of age. Eight GM metrics proposed in the literature in addition to a novel metric were estimated from the PoI trajectories at each timepoint. A pediatric neurologist and physiatrist provided an overall clinical evaluation from infants’ video. Subsequently, a comparison between metrics and clinical evaluation was performed. The results demonstrated that GM metrics may be meaningfully estimated and potentially used for early identification of movement disorders. |
| first_indexed | 2024-03-09T21:10:38Z |
| format | Article |
| id | doaj.art-f62f6457d9e243e585ac5769d686044d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:38Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f62f6457d9e243e585ac5769d686044d2023-11-23T21:49:00ZengMDPI AGSensors1424-82202022-09-012219742610.3390/s22197426Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory StudyDiletta Balta0HsinHung Kuo1Jing Wang2Ilaria Giuseppina Porco3Olga Morozova4Manon Maitland Schladen5Andrea Cereatti6Peter Stanley Lum7Ugo Della Croce8Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, ItalyDepartment of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USADepartment of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USADepartment of Biomedical Sciences, University of Sassari, 07100 Sassari, ItalyChildren’s National Hospital, Washington, DC 20010, USADepartment of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USADepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, ItalyDepartment of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USADepartment of Biomedical Sciences, University of Sassari, 07100 Sassari, ItalyCerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentation in the home may be used to estimate quantitative metrics predictive of movement disorders. This study explored if infants’ GM may be successfully evaluated in a familiar environment by processing the 3D trajectories of points of interest (PoI) obtained from recordings of a single commercial RGB-D sensor. The RGB videos were processed using an open-source markerless motion tracking method which allowed the estimation of the 2D trajectories of the selected PoI and a purposely developed method which allowed the reconstruction of their 3D trajectories making use of the data recorded with the depth sensor. Eight infants’ GM were recorded in the home at 3, 4, and 5 months of age. Eight GM metrics proposed in the literature in addition to a novel metric were estimated from the PoI trajectories at each timepoint. A pediatric neurologist and physiatrist provided an overall clinical evaluation from infants’ video. Subsequently, a comparison between metrics and clinical evaluation was performed. The results demonstrated that GM metrics may be meaningfully estimated and potentially used for early identification of movement disorders.https://www.mdpi.com/1424-8220/22/19/7426markerlessRGB-Dgeneral movementsinfant movement analysismovement disorders |
| spellingShingle | Diletta Balta HsinHung Kuo Jing Wang Ilaria Giuseppina Porco Olga Morozova Manon Maitland Schladen Andrea Cereatti Peter Stanley Lum Ugo Della Croce Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study Sensors markerless RGB-D general movements infant movement analysis movement disorders |
| title | Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study |
| title_full | Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study |
| title_fullStr | Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study |
| title_full_unstemmed | Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study |
| title_short | Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study |
| title_sort | characterization of infants general movements using a commercial rgb depth sensor and a deep neural network tracking processing tool an exploratory study |
| topic | markerless RGB-D general movements infant movement analysis movement disorders |
| url | https://www.mdpi.com/1424-8220/22/19/7426 |
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