Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring.

Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for ambulatory diagnosis and monitoring applications of populations at risk of cardiovascular disease, generally due to a limited sample size. This paper introduces an algorithm for BP estimation solely reliant on photoplethysmography (PPG) signals and demographic features. It automatically obtains signal features and employs the Markov Blanket (MB) feature selection to discern informative and transmissible features, achieving a robust space adaptable to the population shift. This approach was validated with the Aurora-BP database, compromising ambulatory wearable cuffless BP measurements for over 500 individuals. After evaluating several machine-learning regression methods, Gradient Boosting emerged as the most effective. According to the MB feature selection, temporal, frequency, and demographic features ranked highest in importance, while statistical ones were deemed non-significant. A comparative assessment of a generic model (trained on unclassified BP data) and specialized models (tailored to each distinct BP population), demonstrated a consistent superiority of our proposed MB feature space with a mean absolute error of 10.2 mmHg (0.28) for systolic BP and 6.7 mmHg (0.18) for diastolic BP on the whole dataset. Moreover, we present a first comparison of in-clinic vs. ambulatory models, with performance significantly lower for the latter with a drop of 2.85 mmHg in systolic ( ) and 2.82 mmHg for diastolic ( ) estimation errors. This work contributes to the resilient understanding of BP estimation algorithms from PPG signals, providing causal features in the signal and quantifying the disparities between ambulatory and in-clinic measurements.

Learning Activities of Daily Living from Unobtrusive Multimodal Wearables: Towards Monitoring Outpatient Rehabilitation.

Monitoring activities of daily living (ADLs) for wheelchair users, particularly spinal cord injury individuals is important for understanding the rehabilitation progress, customizing treatment plans, and observing the onset of secondary health conditions. This work proposes an innovative sensory system for measuring and classifying ADLs relevant to secondary health conditions. We systematically evaluated multiple wearable sensors such as pressure distribution mats on the wheelchair seat, accelerometer data from the ear and wrists, and IMU data from the wheelchair wheels to achieve the best unobtrusive combination of sensors that successfully distinguished ADLs. Our work resulted in an XGBoost classifier with a 20-second window size and extracted features in statistical, time, frequency, and wavelet domains, with an average class-wise F1 score of 82% (with only 3 out of 12 classes being mislabeled). Our study results demonstrate that the newly investigated modality of the bottom pressure mat emerges as the most relevant information source for recognizing ADLs, while heart and respiratory rates did not provide added value for the selected set of ADLs. The proposed sensory system and methodology proved high quality in most classes and easily extendable for long-term monitoring in outpatient rehabilitation, with the need for an extended database of activities.

A new scheme for the development of IMU-based activity recognition systems for telerehabilitation.

Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and multistage RBF SVM) were investigated. Features were signal-based in the time, frequency, and time-frequency domains. Double-stage feature extraction by PCA and fisher technique was used. The optimal design reached a recall of 95% on healthy subjects using only two sensors on the left thigh and forearm. Implementing the adaptation procedure on two PD subjects, the performance was maintained above 80%. Post analysis on the performance of the adapted HAR showed a slight drop in precision (above 87% to above 81%) for activities that was performed in sitting condition.

Time-domain ultrasound as prior information for frequency-domain compressive ultrasound for intravascular cell detection: A 2-cell numerical model.

This study proposes a new method for the detection of a weak scatterer among strong scatterers using prior-information ultrasound (US) imaging. A perfect application of this approach is in vivo cell detection in the bloodstream, where red blood cells (RBCs) serve as identifiable strong scatterers. In vivo cell detection can help diagnose cancer at its earliest stages, increasing the chances of survival for patients. This work combines time-domain US with frequency-domain compressive US imaging to detect a 20-µ MCF-7 circulating tumor cell (CTC) among a number of RBCs within a simulated venule inside the mouth. The 2D image reconstructed from the time-domain US is employed to simulate the reflected and scattered pressure field from the RBCs, which is then measured at the location of the receivers. The RBCs are tagged one time by a human operator and another time, automatically, by template-based computer vision. Next, the resulting signal from the RBCs is subtracted from the measured total signal in frequency domain to generate the scattered-field data, coming from the CTC alone. Feeding that signal and the background pressure field into a norm-one-based compressive sensing code enables detecting the CTC at various locations. As errors could arise in determining the location of the RBCs and their acoustic properties in the real world, small errors (up to 10% in the former and 5% in the latter) are purposefully introduced to the model, to which the proposed method is shown to be resilient. Localization errors are smaller than 12 µ when a human tags the RBCs and smaller than 25 µ when computer vision is applied. Despite its limitations, this study, for the first time, reports the results of combining two US modalities aimed at cell detection and introduces a unique and useful application for ultrahigh-frequency US imaging. It should be noted that this method can be used in detecting weak scatterers with ultrasound waves in other applications as well.