Table 2.

Studies for neonatal motion quantification using direct sensing technology. The order is sorted by the aim of motion quantification, and year.

Author (Refer)	Year	Subject size (P, I)	Sensing technology	Product model	Placement	Aim of motion quantification	Signal processing method of motion quantification
Chung et al. [10]	2020	P = 35 I = 15	Accelerometer & gyroscope	BMI160	Chest, using an epidermal sensor [15]	Motion/position monitoring: Recognize body positions during different activities.	Low-pass filtering, Derived rotation angles
Jeong et al. [13]	2022	I = 2	Accelerometer & gyroscope	BMI160	Multiple limb and head/torso positions, using silicone elastomer with medical silicone adhesives	Neuromotor pathology: Estimate pose and orientation for 3D motion reconstruction	Unified robot description format
Lan et al. [12]	2018	P = 65	Accelerometer	wGT3x-BT	Left and right legs, using a bandage	Sleep: Transform motion counts into sleep stage estimates	Activity is processed based on the method from Sadeh et al. [16]
Schoch et al. [4]	2019	I = 50	Accelerometer	GENEactiv	Left ankle, using a sock or paper strap to position	Sleep: Transform motion counts into sleep estimates	Squared sum of three axes
Raj et al. [11]	2018	P = 10	Accelerometer	LIS2HH12	Sternum or abdomen; using skin adhesive tape	Vitals: Reject unreliable period with motion artifact for respiration estimation	Squared sum of three axes, adaptive filter

P – Preterm infants; I – Infants.