A soft-electronic sensor network tracks neuromotor development in infants

The brain coordinates the body’s movements through the central nervous system (CNS). Hence, movement behaviors in infants reveal valuable information regarding their developing CNS (1). In infants, spontaneous movements often referred to as general movements (GMs) are an indicator of later neurological deficits (2). GMs are automatic, are complex, occur frequently, and can be observed accurately from early fetal life to 6 mo of age (3). Early observation and assessment of atypical GMs open up the possibility of therapeutic intervention in infants and rely on the neuroplasticity of the brain to avert potential negative outcomes (4, 5). Qualitative and quantitative monitoring of GMs currently requires clinical tests, medical history, video monitoring, and medical experts (6, 7). All these are time and resource intensive; therefore, they are not available to the wider population. In PNAS, Jeong et al. (8) demonstrate an artificial intelligence-enabled soft-electronic sensor network that monitors movements in infants for predicting later neurological deficits (Fig. 1 A ). Fig. 1. Soft-electronic sensor network for early detection of later neurological deficits in infants. ( A ) The sensors are placed on the forehead, chest, and limbs of the infants. These sensors are fabricated using flexible printed circuit boards (PCBs). Electronic components and batteries are assembled and encapsulated inside a waterproof silicone elastomer. Accelerometer and gyroscope data from the left upper arm (LUA), left lower arm (LLA), right upper arm (RUA), and right lower arm (RLA) are then interpreted to acceleration, angular velocity, and normalized activity levels. With machine-learning techniques, these … [↵][1]1To whom correspondence may be addressed. Email: zbao{at}stanford.edu. [1]: #xref-corresp-1-1