Oral feeding competency is a primary requirement for preterm infant hospital release. Currently there is no widely accepted method to objectively measure oral feeding. Feeding consists primarily of the integration of three individual feeding events: sucking, breathing, and swallowing, and the objective of feeding coordination is to minimize aspiration. The purpose of this work was to quantify the infant feeding process from signals obtained during bottle feeding and ultimately develop a measure of feeding coordination. Sucking was measured using a pressure transducer embedded within a modified silicone bottle block. Breathing was measured using a thermistor embedded within nasal cannula, and swallowing was measured through the use of several different piezoelectric sensors. In addition to feeding signals, electrocardiogram (ECG) signals were obtained as an indicator of overall infant behavioral state during feeding. Event detection algorithms for the individual feeding signals were developed and validated, then used for the development of a measurement of feeding coordination. The final suck event detection algorithm was the result of an iterative process that depended on the validity of the signal model. As the model adapted to better represent the data, the accuracy and specificity of the algorithm improved. For the breath signal, however, the primary barrier to effective event detection was significant baseline drift. The frequency components of the baseline drift overlapped significantly with the breath event frequency components, so a time domain solution was developed. Several methods were tested, and it was found that the acceleration vector of the signal provided the most robust representation of the underlying breath signal while minimizing baseline drift. Swallow signal event detection was not possible due to a lack of available data resulting from problems with the consistency of the obtained signal. A robust method was developed for the batch processing of heart rate variability analysis. Finally a method of coordination analysis was developed based on the event detection algorithm outputs. Coordination was measured by determining the percentage of feeding time that consisted of overlapping suck and breath activity.

No description available.

Yes / The present study introduces four event detection algorithms for defining touch-down and foot-off during stair descent and stair ascent using segmental kinematics. For stair descent, vertical velocity minima of the whole body center-of-mass was used to define touch-down, and foot-off was defined as the instant of trail limb peak knee flexion. For stair ascent, vertical velocity local minima of the lead-limb toe was used to define touch-down, and foot-off was defined as the local maxima in vertical displacement between the toe and pelvis. The performance of these algorithms was determined as the agreement in timings of kinematically derived events to those defined kinetically (ground reaction forces). Data were recorded while 17 young and 15 older adults completed stair descent and ascent trials over a four-step instrumented staircase. Trials were repeated for three stair riser height conditions (85 mm, 170 mm, and 255 mm). Kinematically derived touch-down and foot-off events showed good agreement (small 95% limits of agreement) with kinetically derived events for both young and older adults, across all riser heights, and for both ascent and descent. In addition, agreement metrics were better than those returned using existing kinematically derived event detection algorithms developed for overground gait. These results indicate that touch-down and foot-off during stair ascent and descent of non-instrumented staircases can be determined with acceptable precision using segmental kinematic data.