Abstract
This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors.
Highlights
Smart-living technologies are changing the way modern living environments are conceived.Smart devices are increasingly populating the home scenario, fostering innovative services.Such services can be aimed at increasing safety and supporting independent life of older adults or people with disabilities, following the Active Assisted Living (AAL) paradigm
Heterogeneous technologies converge into the AAL framework, ranging from basic telemedicine services to non-intrusive, continuous monitoring based on simple environmental sensors [1] and behavioral analysis techniques [2,3]
Seismocardiogram and ballistocardiogram are oscillatory signals that originate from heart activity
Summary
Smart devices are increasingly populating the home scenario, fostering innovative services. Such services can be aimed at increasing safety and supporting independent life of older adults or people with disabilities, following the Active Assisted Living (AAL) paradigm. Heterogeneous technologies converge into the AAL framework, ranging from basic telemedicine services to non-intrusive, continuous monitoring based on simple environmental sensors [1] and behavioral analysis techniques [2,3]. Recent advances in wearable and ubiquitous sensors well fit in this scenario, making simple vital signs such as Heart Rate (HR) or physical activity very easy to collect in a continuous and prolonged fashion, for example, by means of a wrist-worn device. The gold standard for Computers 2020, 9, 41; doi:10.3390/computers9020041 www.mdpi.com/journal/computers
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