Wearable Sensors

This pilot study aims to evaluate the feasibility and safety of continuous bowel sound monitoring in healthy term newborn infants using a novel wireless acoustic monitoring system. Participants will undergo a single 4-hour recording during routine postnatal care while bowel sounds are continuously recorded using wearable abdominal sensors. Feasibility will be assessed by participant recruitment and study completion, as well as the acquisition of high-quality, analyzable acoustic data. Bowel sounds will be characterized by assessing the temporal and spectral features of bowel sounds during the early neonatal period and exploring their relationship with routine caregiving activities and feeding.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease increases the risk of falling. Despite being a common symptom, it is still difficult to evaluate freezing of gait quickly and accurately. Currently, the gold-standard method to determine the severity of FOG is a manual analysis of video footage by an experienced assessor, collected during standardized FOG-provoking walking tests. Because this is a very time-intensive process, where different assessors sometimes obtain different results, our team at KU Leuven have developed an artificial-intelligent (AI) algorithm trained to identify FOG episodes based on wearable inertial measurement unit (IMU) sensor data. The AI algorithm has already undergone initial validation during laboratory testing, yielding promising results. The aim of this study is to investigate whether the AI algorithm can accurately detect FOG episodes in a less controlled environment, namely the home environment. In a second phase, the investigators will also use the collected data to improve the AI algorithm for automated FOG detection in the home. Finally, the investigators want to explore whether the AI algorithm can detect FOG in real-time.