General movements (GMs) have been widely used for the early clinical evaluation of infant brain development. Recently, an international team proposed a sparse sensor network with soft wireless IMU (inertial motion unit) devices (SWDs) for automatic early evaluation of GMs in infants, which will facilitate immediate evaluation of potential development disorders and timely rehabilitation.

Infants’ general movements can be captured and digitized using a video camera, but the lack of quantitative assessment and well-trained clinical pediatricians presents an obstacle for wider deployment, especially in low-resource settings.

Wearable sensors for movement analysis is therefore of high potential due to outstanding privacy, low cost, and easy-to-use feature.

The group led by YANG Hongbo from Suzhou Institute of Biomedical Engineering and Technology (SIBET), and CHENG huanyu from The Pennsylvania State University (PSU) jointly developed the intelligent sparse sensor network with only five sensor nodes.

The nodes, with robust mechanical properties and excellent biocompatibility, make it possible to capture full-body motion data continuously and stably.

“To significantly reduce the risk of iatrogenic skin injuries due to the fragility associated with infants’ immature skin, it is highly desirable to explore soft wearable wireless devices with ‘skin-like’ mechanical properties to interface with the neonatal skin,” said YANG Hongbo.

Their study showed that the soft wireless devices have excellent mechanical, biocompatible and electrical performance towards neonatal skin. Moreover, soft sparse sensor nodes wirelessly connected by the Bluetooth low energy one-to-five technology could be pliably attached to the delicate neonatal skin for monitoring the general movement of the infants.

The integrated device combined with the custom graphical user interface and a tiny machine-learning model provides continuous and robust monitoring and processing of the general movements to achieve a prediction accuracy of up to 99.9% in clinical settings.

Furthermore, the model with the feature dimension reduced by half still achieves almost unchanged accuracy, showing the high potential for future deployment in low-resource settings.

The design strategies from this work could promise AI-powered intelligent medical services in the future, for other disease applications such as lung function and stroke balance assessments as well as assisting rehabilitation in remote settings, according to YANG. 

The research results entitled “Intelligence Sparse Sensor Network for Automatic Early Evaluation of General Movements in Infants” were published in Advanced Science.

Fig1. Inside Cover picture of the journal about this paper. This cover is inspired by a traditional Chinese mythology, where the fairy of Seven Star Lady is guarding the health and safety of the infant. In this article, the intelligence sparse sensor network with five sensing nodes on the infant continuously capture and analyze general movements for the early clinical evaluation of infant brain development. (Image by the journal)

Fig2. The schematic illustrating the system design and application of the intelligent sparse sensor network with soft wireless IMU devices (SSN-SWDs). (Image by SIBET)

Fig3. Photograph showing the experimental setup with the integrated device placed on the right wrist, left wrist, right ankle, left ankle, and the head of an infant (12 wk.) lying in a crib. (Image by SIBET)

Contact

XIAO Xintong

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences (http://www.sibet.cas.cn/)

Phone: 86-512-69588013

E-mail: xiaoxt@sibet.ac.cn