Results
From July 2022 through April 2023, 51 people expressed interest in
participating in the real-world study. Of these, 34 (67%) were not
assessed for eligibility (four decided against participating after
learning more about the study, 25 did not respond after their initial
expression of interest, and five gave birth prior to screening) – we
did not collect any data from these. Of the seventeen (33%)
participants that were screened, all met the eligibility criteria and
gave written informed consent. All seventeen had bed partners, but only
fifteen bed partners gave informed consent and participated. Two
participants (and their bed partners) installed the camera incorrectly,
so their data was excluded from the model building. As such, fifteen
participants (and thirteen bed partners) successfully completed the
study.
Demographic
Characteristics
Demographic characteristics of the pregnant participants and their bed
partners (if applicable) are shown in Table 1 . Ethnic
backgrounds from the real-world dataset included representation from
Northern European, Latino, Greek, South Asian, East Asian,
Armenian/Turkish, Italian, and Hungarian ancestries. We did not collect
ethnicity, gravida, or parity data from the participants in the
controlled-setting dataset.
Dataset
In the real-world study, we collected 29,253 minutes (487.6 hours) of
infrared video from which we extracted and annotated 6,960 unique
frames. Of these, 6,514 were multi-participant frames, and 446 were
single-participant frames. See Appendix B for a qualitative
description of the real-world dataset. These data from our real-world
dataset were combined with our controlled-setting dataset (5,970 frames)
for a total of 12,930 annotated frames, which contained 47,001
annotations, and comprised our dataset for building SLeeP AIDePt-2. SeeTable 2 for class-wise information about the datasets.
A bar chart of the frequency of occurrences of each position class in
the real-world dataset and controlled-setting dataset are shown inFigure 1 . The sleeping positions have been rearranged on the
x-axis of the bar chart to demonstrate the progression of the positions
starting from left recovery and proceeding leftward, rolling across the
back (supine), until right recovery and, finally, prone and sitting.
Models
In Figure 2 , class-wise results (averaged across all five
loops) are shown using a bar chart and heat map. The bar chart inFigure 2A shows the four performance metrics from the testing
phase averaged across the five models’ (one model per loop of the
cross-validation) testing sets and across all classes. The error bars on
the bar chart represent one standard deviation of the respective value
across all measures, reflecting the variability across models (n=5) and
classes (n=14). The heatmap in Figure 2B shows the four
performance parameters (columns) from the testing phase averaged across
the five models’ test sets for each of the predicted classes (rows).
On a per-class basis and averaged across the five models, the sitting
class had the highest AP@0.50 (0.92). The left lateral, right lateral,
and supine classes also had high values of AP@0.50 (0.82 to 0.89),
whereas recovery, prone, and twisted/hybrid positions generally had
intermediate values of AP@0.50 (0.62 to 0.72). The non-hybrid tilted
positions (left tilt and right tilt) had the lowest values of AP@0.50
(<0.50). As for the pillow class, the AP@0.50 was
intermediate-to-high (0.80).
See Table B.1 and Table B.2 in Appendix B for
a loop-wise summary of the training, validation, and performance testing
of the cross-validation of SLeeP AIDePt-2.
A running example using one of our trained SLeeP AIDePt-2 models to
localise and classify the sleeping position of a study participant and
their bed partner in eight different extracted frames is displayed inFigure 3 .
Harms
There were no known or identified harms related to this study.