An interpretable framework for sleep posture change detection and postural inactivity segmentation using wrist kinematics.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 10 2023
21 10 2023
Historique:
received:
31
01
2023
accepted:
10
10
2023
medline:
1
11
2023
pubmed:
22
10
2023
entrez:
21
10
2023
Statut:
epublish
Résumé
Sleep posture and movements offer insights into neurophysiological health and correlate with overall well-being and quality of life. Clinical practices utilise polysomnography for sleep assessment, which is intrusive, performed in unfamiliar environments, and requires trained personnel. While sensor technologies such as actigraphy are less invasive alternatives, concerns about their reliability and precision in clinical practice persist. Moreover, the field lacks a universally accepted algorithm, with methods ranging from raw signal thresholding to data-intensive classification models that may be unfamiliar to medical staff. This paper proposes a comprehensive framework for objectively detecting sleep posture changes and temporally segmenting postural inactivity using clinically relevant joint kinematics, measured by a custom-made wearable sensor. The framework was evaluated on wrist kinematic data from five healthy participants during simulated sleep. Intuitive three-dimensional visualisations of kinematic time series were achieved through dimension reduction-based preprocessing, providing an out-of-the-box framework explainability that may be useful for clinical monitoring and diagnosis. The proposed framework achieved up to 99.2% F1-score and 0.96 Pearson's correlation coefficient for posture detection and inactivity segmentation respectively. This work paves the way for reliable home-based sleep movement analysis, serving patient-centred longitudinal care.
Identifiants
pubmed: 37865640
doi: 10.1038/s41598-023-44567-9
pii: 10.1038/s41598-023-44567-9
pmc: PMC10590424
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
18027Informations de copyright
© 2023. Springer Nature Limited.
Références
Ibáñez, V., Silva, J. & Cauli, O. A survey on sleep questionnaires and diaries. Sleep Med. 42, 90–96. https://doi.org/10.1016/j.sleep.2017.08.026 (2018).
doi: 10.1016/j.sleep.2017.08.026
pubmed: 29458752
Beccuti, G. & Pannain, S. Sleep and obesity. Curr. Opin. Clin. Nutr. Metab. Care 14, 402–412. https://doi.org/10.1097/MCO.0b013e3283479109 (2011).
doi: 10.1097/MCO.0b013e3283479109
pubmed: 21659802
pmcid: 3632337
Calhoun, D. A. & Harding, S. M. Sleep and hypertension. Chest 138, 434–443. https://doi.org/10.1378/chest.09-2954 (2010).
doi: 10.1378/chest.09-2954
pubmed: 20682533
pmcid: 2913764
Spiegel, K., Knutson, K., Leproult, R., Tasali, E. & Cauter, E. V. Sleep loss: A novel risk factor for insulin resistance and type 2 diabetes. https://doi.org/10.1152/japplphysiol.00660.2005 (2005).
Wolk, R., Gami, A. S., Garcia-Touchard, A. & Somers, V. K. Sleep and cardiovascular disease. Curr. Probl. Cardiol. 30, 625–662. https://doi.org/10.1016/j.cpcardiol.2005.07.002 (2005).
doi: 10.1016/j.cpcardiol.2005.07.002
pubmed: 16301095
Paquay, L. et al. Adherence to pressure ulcer prevention guidelines in home care: A survey of current practice. J. Clin. Nurs. 17, 627–636. https://doi.org/10.1111/j.1365-2702.2007.02109.x (2008).
doi: 10.1111/j.1365-2702.2007.02109.x
pubmed: 18279295
Pinna, G. D. et al. Differential impact of body position on the severity of disordered breathing in heart failure patients with obstructive vs. central sleep apnoea. Eur. J. Heart Fail. 17, 1302–1309. https://doi.org/10.1002/ejhf.410 (2015).
Akeson, W. H., Amiel, D., Abel, M. F., Garfin, S. R. & Woo, S. L. Effects of immobilization on joints. Clin. Orthop. Relat. Res. 219, 28–37. https://doi.org/10.1097/00003086-198706000-00006 (1987).
doi: 10.1097/00003086-198706000-00006
Parisi, L. et al. Muscular cramps: Proposals for a new classification. Acta Neurol. Scand. 107, 176–186. https://doi.org/10.1034/j.1600-0404.2003.01289.x (2003).
doi: 10.1034/j.1600-0404.2003.01289.x
pubmed: 12614310
Elnaggar, O., Coenen, F. & Paoletti, P. In-bed human pose classification using sparse inertial signals, Vol. 12498 LNAI, 331–344. https://doi.org/10.1007/978-3-030-63799-6_25 (2020).
Elnaggar, O., Coenen, F., Hopkinson, A., Mason, L. & Paoletti, P. Sleep posture one-shot learning framework based on extremity joint kinematics: In-silico and in-vivo case studies. Inf. Fusion 95, 215–236. https://doi.org/10.1016/j.inffus.2023.02.003 (2023).
doi: 10.1016/j.inffus.2023.02.003
Alaziz, M., Jia, Z., Howard, R., Lin, X. & Zhang, Y. In-bed body motion detection and classification system. ACM Trans. Sens. Netw. 16, 13:1–13–26. https://doi.org/10.1145/3372023 (2020).
Jeon, S., Park, T., Paul, A., Lee, Y. S. & Son, S. H. A wearable sleep position tracking system based on dynamic state transition framework. IEEE Access 7, 135742–135756. https://doi.org/10.1109/ACCESS.2019.2942608 (2019).
doi: 10.1109/ACCESS.2019.2942608
Perslev, M. et al. U-sleep: Resilient high-frequency sleep staging. NPJ Digit. Med. 4, https://doi.org/10.1038/s41746-021-00440-5 (2021).
Yang, Z., Pathak, P. H., Zeng, Y., Liran, X. & Mohapatra, P. Vital sign and sleep monitoring using millimeter wave. ACM Trans. Sens. Netw.. https://doi.org/10.1145/3051124 (2017).
Ben-Dov, I. Z. et al. Predictors of all-cause mortality in clinical ambulatory monitoring: Unique aspects of blood pressure during sleep. Hypertension 49, https://doi.org/10.1161/HYPERTENSIONAHA.107.087262 (2007).
Chang, L. et al. Sleepguard: Capturing rich sleep information using smartwatch sensing data. Proc. ACM Interact. Mobile Wearable Ubiquit. Technol. 2, 1–34. https://doi.org/10.1145/3264908 (2018).
doi: 10.1145/3264908
Min, J. K. et al. Toss ’n’ turn: Smartphone as sleep and sleep quality detector. https://doi.org/10.1145/2556288.2557220 (2014).
Alaziz, M. et al. Motion scale: A body motion monitoring system using bed-mounted wireless load cells, 183–192. https://doi.org/10.1109/CHASE.2016.13 (2016).
Gu, W. et al. Intelligent sleep stage mining service with smartphones, 649–660. https://doi.org/10.1145/2632048.2632084 (2014).
Gu, W., Shangguan, L., Yang, Z. & Liu, Y. Sleep hunter: Towards fine grained sleep stage tracking with smartphones. IEEE Trans. Mobile Comput. 15, 1514–1527. https://doi.org/10.1109/TMC.2015.2462812 (2016).
doi: 10.1109/TMC.2015.2462812
Borazio, M., Berlin, E., Kucukyildiz, N., Scholl, P. & Laerhoven, K. V. Towards benchmarked sleep detection with wrist-worn sensing units, 125–134. https://doi.org/10.1109/ICHI.2014.24 (2014).
Kwasnicki, R. M. et al. A lightweight sensing platform for monitoring sleep quality and posture: A simulated validation study. Eur. J. Med. Res. 23, 1–9. https://doi.org/10.1186/s40001-018-0326-9 (2018).
doi: 10.1186/s40001-018-0326-9
Nakazaki, K. et al. Validity of an algorithm for determining sleep/wake states using a new actigraph. J. Physiol. Anthropol.. https://doi.org/10.1186/1880-6805-33-31 (2014).
Sun, X., Qiu, L., Wu, Y., Tang, Y. & Cao, G. Sleepmonitor: Monitoring respiratory rate and body position during sleep using smartwatch. Proc. ACM Interact. Mobile Wearable Ubiquit. Technol. 1, 1–22. https://doi.org/10.1145/3130969 (2017).
doi: 10.1145/3130969
Banfi, T. et al. Efficient embedded sleep wake classification for open-source actigraphy. Sci. Rep. https://doi.org/10.1038/s41598-020-79294-y (2021).
Domingues, A., Paiva, T. & Sanches, J. M. Sleep and wakefulness state detection in nocturnal actigraphy based on movement information. IEEE Trans. Biomed. Eng. 61, 426–434. https://doi.org/10.1109/TBME.2013.2280538 (2014).
doi: 10.1109/TBME.2013.2280538
pubmed: 24013826
Palotti, J. et al. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. NPJ Digit. Med.. https://doi.org/10.1038/s41746-019-0126-9 (2019).
Webster, J. B., Kripke, D. F., Messin, S., Mullaney, D. J. & Wyborney, G. An activity-based sleep monitor system for ambulatory use. Sleep 5, 389–399. https://doi.org/10.1093/sleep/5.4.389 (1982).
doi: 10.1093/sleep/5.4.389
pubmed: 7163726
Olivares, A., Ramírez, J., Górriz, J. M., Olivares, G. & Damas, M. Detection of (in)activity periods in human body motion using inertial sensors: A comparative study. Sensors (Switzerland) 12, 5791–5814. https://doi.org/10.3390/s120505791 (2012).
doi: 10.3390/s120505791
McInnes, L., Healy, J. & Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction, 1–63. https://doi.org/10.48550/arXiv.1802.03426 (2018).
Woodman, O. J. An introduction to inertial navigation (report no. ucam-cl-tr-696) (2007).
Kok, M., Hol, J. D. & Schön, T. B. Using inertial sensors for position and orientation estimation. Found. Trends Signal Process. https://doi.org/10.1561/2000000094 (2017).
Madgwick, S. O., Harrison, A. J. & Vaidyanathan, R. Estimation of imu and marg orientation using a gradient descent algorithm, 179–185. https://doi.org/10.1109/ICORR.2011.5975346 (2011).
Elnaggar, O. & Arelhi, R. A new unsupervised short-utterance based speaker identification approach with parametric t-sne dimensionality reduction. 1–10. https://doi.org/10.1109/ICAIIC.2019.8669051 (2019).
Elnaggar, O. & Arelhi, R. Quantification of knowledge exchange within classrooms: An AI-based approach. 1–11. https://doi.org/10.22492/issn.2188-1162.2021.17 (2021).
Airaksinen, M. et al. Automatic posture and movement tracking of infants with wearable movement sensors. Sci. Rep. 10, 1–12. https://doi.org/10.1038/s41598-019-56862-5 (2020).
doi: 10.1038/s41598-019-56862-5
Zebin, T. Wearable inertial multi-sensor system for physical activity analysis and classification with machine learning algorithms (2018).
Mäkelä, S. M. et al. Introducing vtt-coniot: A realistic dataset for activity recognition of construction workers using imu devices. Sustainability (Switzerland) 14, 1–20. https://doi.org/10.3390/su14010220 (2022).
doi: 10.3390/su14010220
Hamad, R., Jarpe, E. & Lundstrom, J. Stability analysis of the t-sne algorithm for human activity pattern data, 1839–1845. https://doi.org/10.1109/SMC.2018.00318 (2019).
Adams, R. P. & MacKay, D. J. C. Bayesian online changepoint detection. https://doi.org/10.48550/arxiv.0710.3742 (arXiv preprint) (2007).
Dekking, F. M., Kraaikamp, C., Lopuhaä, H. P. & Meester, L. E. A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer, 2005).
Elnaggar, O. et al. Kids: Kinematics-based (in)activity detection and segmentation in a sleep case study. https://doi.org/10.17638/datacat.liverpool.ac.uk/2127 (2023).