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.

Risk constellation of hospital acquired pressure injuries in patients with a spinal cord injury/ disorder - focus on time since spinal cord injury/ disorder and patients' age.

STUDY DESIGN: Prospective cohort study. OBJECTIVES: The aim of this study was to evaluate how time since spinal cord injury/disorder (SCI/D) and patients' age influence risk constellation for hospital acquired pressure injuries (HAPI) in patients with a SCI/D. SETTING: Acute care and rehabilitation clinic specialized in SCI/D. METHODS: We collected patients' characteristics and 85 risk factors for HAPI development in adults with SCI/D with at least one HAPI during their inpatient stay between August 2018 and December 2019. We analyzed patients' characteristics and HAPI risk factors using descriptive statistics according to time since SCI/D ( < 1 year, 1-15 years, > 15 years) and patients' age (18-35 years, 35-65 years, > 65 years). RESULTS: We identified 182 HAPI in 96 patients. Comparing patients with SCI/D < 1 year with the other groups, autonomic dysreflexia (p < 0.001), abnormal body temperature (p = 0.001), hypertensive episode (p = 0.005), and pneumonia (p < 0.001) occurred more frequently; mean hemoglobin (p < 0.001), albumin (p = 0.002) and vitamin D levels (p = 0.013) were significantly lower, and patients with time since SCI/D < 1 year scored fewer points (10-12) on the Braden Scale (p < 0.001). Comparing groups per patients' age, only the SCIPUS score was higher in patients > 65 years compared to the other two groups (p = 0.002). CONCLUSIONS: Different risk factor constellation seem to be underlying HAPI development with more differences in patients time since SCI/D than patients' age. Awareness of these differences in risk factor constellation depending on time since SCI/D in these patients might lead to different HAPI prevention strategies. SPONSORSHIP: The study team didn't receive any additional sponsorship.

Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots.

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who are inherently smaller and fragile. This study compared the risks faced by different pedestrian categories and determined risks through crash testing involving a service robot hitting an adult and a child dummy. Results of collisions at 3.1 m/s (11.1 km/h/6.9 mph) showed risks of serious head (14%), neck (20%), and chest (50%) injuries in children, and tibia fracture (33%) in adults. Furthermore, secondary impact analysis resulted in both populations at risk of severe head injuries, namely, from falling to the ground. Our data and simulations show mitigation strategies for reducing impact injury risks below 5% by either lowering the differential speed at impact below 1.5 m/s (5.4 km/h/3.3 mph) or through the usage of absorbent materials. The results presented herein may influence the design of controllers, sensing awareness, and assessment methods for robots and small vehicles standardization, as well as, policymaking and regulations for the speed, design, and usage of these devices in populated areas.