Elnaggar, Omar, Arelhi, Roselina, Coenen, Frans 
ORCID: 0000-0003-1026-6649, Hopkinson, Andrew, Mason, Lyndon ORCID: 0000-0002-0371-3183 and Paoletti, Paolo ORCID: 0000-0001-6131-0377
(2023)
KIDS: kinematics-based (in)activity detection and segmentation in a
sleep case study.
[Preprint]
Abstract
Sleep behaviour and in-bed movements contain rich information on the neurophysiological health of people, and have a direct link to the general well-being and quality of life. Standard clinical practices rely on polysomnography for sleep assessment; however, it is intrusive, performed in unfamiliar environments and requires trained personnel. Progress has been made on less invasive sensor technologies, such as actigraphy, but clinical validation raises concerns over their reliability and precision. Additionally, the field lacks a widely acceptable algorithm, with proposed approaches ranging from raw signal or feature thresholding to data-hungry classification models, many of which are unfamiliar to medical staff. This paper proposes an online Bayesian probabilistic framework for objective (in)activity detection and segmentation based on clinically meaningful joint kinematics, measured by a custom-made wearable sensor. Intuitive three-dimensional visualisations of kinematic timeseries were accomplished through dimension reduction based preprocessing, offering out-of-the-box framework explainability potentially useful for clinical monitoring and diagnosis. The proposed framework attained up to 99.2\% $F_1$-score and 0.96 Pearson's correlation coefficient in, respectively, the posture change detection and inactivity segmentation tasks. The work paves the way for a reliable home-based analysis of movements during sleep which would serve patient-centred longitudinal care plans.
| Item Type: | Preprint |
|---|---|
| Additional Information: | 18 pages, 8 figures, supplementary info included |
| Uncontrolled Keywords: | eess.SP, eess.SP, cs.LG |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 18 Jan 2023 09:28 |
| Last Modified: | 14 Mar 2024 21:44 |
| DOI: | 10.48550/ARXIV.2301.03469 |
| Open Access URL: | http://arxiv.org/abs/2301.03469 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3167100 |
Dimensions
Dimensions
