Discussion

Main Findings

We collected a heterogeneous video dataset containing instances of single participants and multiple participants, sleeping in or simulating twelve unique sleeping positions along with a sitting position, differing in sleeping environments and pregnancy status (third trimester, non-pregnant), with varying usage of pillows (head, body, pregnancy, wedge) and bed sheets (none, thin, thick; and pattern-free, patterned, striped, textured), both in infrared scale and colour scale video. We trained, validated, and tested a model (SLeeP AIDePt-2) on this dataset to detect the body positions and pillow use of a pregnant person and their bed partner (if any) appearing in an overnight video recording of sleep. The model best detects (high AP@0.50) pillows and the sitting, left lateral, right lateral, and supine positions and has a relatively low false negative rate (high recall) for these detections. The model detects less frequently occurring positions (prone, left/right recovery, supine thorax with left/right pelvic tilt, supine pelvis with left/right thorax tilt) less accurately and is particularly challenged by left/right tilt on which its performance is poorest.

Strengths and Limitations

This study describes the transition of a vision-based sleeping position detection model in preparation for real-world use in pregnancy research. The SLeeP AIDePt-2 model has many strengths. Notably, its real-world deployment does not require specialised equipment and takes into account low-lighting, bedsheets, and entry/exit events. It has learned factors unique to pregnancy anatomy and physiology such as determination of the pelvis position (supine, tilt, lateral, recovery) and direction (left, right). It is trained to detect multiple participants and other objects such as pillows in bed simultaneously, enabling it to not only account for more natural occurrences and frequencies of sleeping positions but also more natural sleeping contexts, behaviours, objects, and environments. Prone sleeping in late pregnancy has never been reported in the literature as naturally occurring and, as such, we believe it is exceedingly rare. However, we observed in our real-world dataset that prone sleeping is common in non-pregnant adults. SLeeP AIDePt-2 is trained to detect this. Compared to our previous work,5SLeeP AIDePt-2 is built on an expanded dataset containing a total of 52 participants (39 pregnant, 13 bed partners), including fifteen additional sleeping environments with no restrictions on bedsheets (thickness or patterns) and pillows.
This study has some limitations. While the real-world dataset contains many possible naturally occurring sleeping positions, SLeeP AIDePt-2 does not account for naturally occurring sleeping positions other than the twelve that we predefined. The overall sample size on which SLeeP AIDePt-2 is trained, particularly the real-world dataset, is small and could benefit from further increases in the number of participants, bed partners, and sleeping environments. The performance of SLeeP AIDePt-2 is sensitive to camera placement. Finally, we did not train SLeeP AIDePt-2 to detect household pets, which could impact sleeping position.13,14See Appendix C for further details.

Interpretation

Despite the current model (SLeeP AIDePt-2) being tested on a more challenging test set than our previous model (SLeeP AIDePt-1) (seeAppendix C ), it significantly outperformed SLeeP AIDePt-1 for the left lateral, supine, and right lateral positions with AP@0.50’s of 0.89 (vs. 0.72), 0.82 (vs. 0.68), and 0.84 (vs. 0.64), respectively. In contrast, for almost all other positions (except sitting and supine pelvis with right thorax tilt), SLeeP AIDePt-2 performed slightly worse than SLeeP AIDePt-1. The explanation for this difference lies in the underlying datasets. The frequencies of occurrence of the sleeping positions in the controlled-setting dataset on which SLeeP AIDePt-1 was built were approximately equal (Figure 1 ); however, this was not so for the sleeping positions in the real-world dataset because people do not spend equal time sleeping in every possible position but, instead, shift between their two or three most comfortable positions. As such, despite our efforts to mitigate class imbalance during training, SLeeP AIDePt-2 is biassed to best detect the most frequent naturally occurring sleeping positions in pregnancy and in non-pregnant adults, which are left lateral, supine, and right lateral.
Our results are comparable to those from other vision-based sleeping position models in non-pregnant adults.15–20Both Li et al,19and Mohammadi et al,17used video recordings from a home-surveillance camera, leveraged controlled-setting data, accounted for bed sheets, and used a similar training methodology to us. Li et al, also combined real-world data with their controlled-setting data, as we did, to build their model.19Unfortunately, Li et al, did not present their model’s performance results in a format that can be compared to ours (see Appendix C ); however, comparing our model to Mohammadi et al,’s model, which was trained and validated on approximately 10,000 frames, we calculated their average recall (sensitivity) in the presence of bed sheets to be 0.64 (standard deviation 0.10), which is similar to ours (0.66, standard deviation 0.20). Beyond this, direct comparison of SLeeP AIDePt-2 to other vision-based models and position sensors is challenging because, as far as we are aware, no other measurement tool accounts for the positions of the pelvis and thorax simultaneously.