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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2208.00884 |
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| _version_ | 1866929721234685952 |
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| author | Kulvicius, Tomas Zhang, Dajie Nielsen-Saines, Karin Bölte, Sven Kraft, Marc Einspieler, Christa Poustka, Luise Wörgötter, Florentin Marschik, Peter B |
| author_facet | Kulvicius, Tomas Zhang, Dajie Nielsen-Saines, Karin Bölte, Sven Kraft, Marc Einspieler, Christa Poustka, Luise Wörgötter, Florentin Marschik, Peter B |
| contents | Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we proposed an innovative non-intrusive approach using a pressure sensing device to classify infant general movements (GMs). Here, we tested the feasibility of using pressure data to differentiate typical GM patterns of the ''fidgety period'' (i.e., fidgety movements) vs. the ''pre-fidgety period'' (i.e., writhing movements). Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a 32x32-grid pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4-16 weeks of post-term age. For proof-of-concept, 1776 pressure data snippets, each 5s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present (FM+) or absent (FM-). Multiple neural network architectures were tested to distinguish the FM+ vs. FM- classes, including support vector machines (SVM), feed-forward networks (FFNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. The CNN achieved the highest average classification accuracy (81.4%) for classes FM+ vs. FM-. Comparing the pros and cons of other methods aiming at automated GMA to the pressure sensing approach, we concluded that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_00884 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Infant movement classification through pressure distribution analysis Kulvicius, Tomas Zhang, Dajie Nielsen-Saines, Karin Bölte, Sven Kraft, Marc Einspieler, Christa Poustka, Luise Wörgötter, Florentin Marschik, Peter B Signal Processing Artificial Intelligence Machine Learning Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we proposed an innovative non-intrusive approach using a pressure sensing device to classify infant general movements (GMs). Here, we tested the feasibility of using pressure data to differentiate typical GM patterns of the ''fidgety period'' (i.e., fidgety movements) vs. the ''pre-fidgety period'' (i.e., writhing movements). Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a 32x32-grid pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4-16 weeks of post-term age. For proof-of-concept, 1776 pressure data snippets, each 5s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present (FM+) or absent (FM-). Multiple neural network architectures were tested to distinguish the FM+ vs. FM- classes, including support vector machines (SVM), feed-forward networks (FFNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. The CNN achieved the highest average classification accuracy (81.4%) for classes FM+ vs. FM-. Comparing the pros and cons of other methods aiming at automated GMA to the pressure sensing approach, we concluded that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions. |
| title | Infant movement classification through pressure distribution analysis |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2208.00884 |