Infant movement classification through pressure distribution analysis.
Infant movement classification through pressure distribution analysis.
复制标题
DOI：
10.1038/s43856-023-00342-5
复制
发表时间：
2023-08-16
期刊：
影响因子：
--
通讯作者：
Marschik, Peter B
中科院分区：
文献类型：
--
作者：
Kulvicius, Tomas;Zhang, Dajie;Nielsen-Saines, Karin;Bolte, Sven;Kraft, Marc;Einspieler, Christa;Poustka, Luise;Worgotter, Florentin;Marschik, Peter B
为了实现对脑瘫等神经运动疾病的客观检测，我们提出了一种创新的非侵入性方法，使用压力敏感性装置来对婴儿进行分类。
参与者（n = 45）是从典型的婴儿队列中进行的，包括来自1024个传感器的压力传感器的多模式传感器数据测试了人类评估者的相应同步视频数据的预先注销，以区分烦躁不安的架构，以区分烦躁不安的缺失类别，包括支持向量机器，馈电网络，卷积神经网络，长期短期记忆网络。
在这里，我们表明卷积神经网络达到了最高的平均分类精度（81.4％）。
我们包括，压力感应方法具有有效的大规模运动数据采集和共享的巨大潜力。
医疗保健专业人员使用的运动是否按预期发展，这项研究的目的是调查含有测量压力的垫子是否可以识别我们所获得的不同运动的结果。比当前使用的其他方法相比，检查婴儿的运动发展。
Kulvicius，Zhang等人提出了一种非侵入性方法，可以使用压力灵敏度设备分类。
Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we propose an innovative non-intrusive approach using a pressure sensing device to classify infant general movements. Here we differentiate typical general movement patterns of the “fidgety period” (fidgety movements) vs. the “pre-fidgety period” (writhing movements). Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4 to 16 weeks of post-term age. 1776 pressure data snippets, each 5 s 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 or absent. Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks. Here we show that the convolution neural network achieved the highest average classification accuracy (81.4%). By comparing the pros and cons of other methods aiming at automated general movement assessment to the pressure sensing approach, we infer that the proposed approach has a high potential for clinical applications. We conclude 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. The movement of a baby is used by health care professionals to determine whether they are developing as expected. The aim of this study was to investigate whether a pad containing sensors that measured pressure occurring as the babies moved could enable identification of different movements of the babies. The results we obtained were similar to those obtained from use of a computer to process videos of the moving babies or other methods using movement sensors. This method could be more readily used to check the movement development of babies than other methods that are currently used. Kulvicius, Zhang et al. propose a non-invasive approach to classify infant movements using a pressure sensing device. Applying neural network architectures to pressure sensing data enables large-scale motion data acquisition and analysis.
影响因子：
2.6
作者：
Fontana C;Ottaviani V;Veneroni C;Sforza SE;Pesenti N;Mosca F;Picciolini O;Fumagalli M;Dellacà RL
通讯作者：
Dellacà RL
DOI：
10.3390/s140100510
发表时间：
2013-12-31
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Donati M;Cecchi F;Bonaccorso F;Branciforte M;Dario P;Vitiello N
通讯作者：
Vitiello N