PMC8570382_01
Predicted position prevalence from classification (y-axis) plotted against actual position prevalence from human coding of body position (x-axis) for home case study Participant B. Each point represents the proportion of time the infant spent in each position during each of 17 7.5-min periods that were video recorded following the end of the training session (titles indicate the r-value for the correlation between actual and predicted within each position category). Note that several points are overlapping (e.g., the infant was supine and sitting 100% of the time for multiple periods and was held 0% of the time for multiple periods). The overall correlation between actual and predicted prevalence across positions/periods was r = 0.976.
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F6
Within-position scatterplots and correlations are shown in Figure 6 for Participant B, for whom sufficient data were available. The next 2 h (until 1:15 p.m. when the camera battery ran out) were recorded on video and used to calculate the correlations in Figure 6 and the validation statistics in Table 3.
Timeline from Participant B's entire data collection showing actual position codes (top row) compared with model predictions of body position (bottom row). The legend indicates the bar color for each body position and lists the model's prediction of how much time the infant spent in each position over the 4.5-h session. The sensors were placed on the infant after the first nap (at 10:30 a.m.). From 10:30 a.m. to 11:00 a.m., the infant and caregiver were guided through the scripted activities over the phone by the experimenter and completed the prescribed free play in front of the camera. Data from those 30 min were used to train the machine learning classifier. The remaining period (11 a.m. until the camera stopped at 1:15 p.m.) was used for validation. The video recording allowed us to verify that the 40-min period of sitting (from approximately 12 p.m. to 12:40 p.m.) corresponded to a meal with the infant sitting in a high chair and that the period of supine (from approximately 12:45 p.m. to 1:15 p.m.) corresponded to a period of TV viewing while the infant reclined in a seating device. The infant continued to wear the sensors until a nap at 3 p.m., which was the last recorded time before the experimenter picked up the equipment at 5 p.m.
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F7
Figure 7 shows a timeline of actual and predicted body positions during the entire recording session for Participant B, providing an example of the type of data afforded by this method. The sensors continued to record until the infant took a second nap at 3:00 p.m., and were picked up by the experimenter following the nap. The legend in Figure 7 shows the proportion of each body position predicted by the model across the entire sensor recording period.