Analysis of Noisy Biosignals for Musical Performance

Proceeding volume: 7619Peer reviewe

Long‐term digital device‐enabled monitoring of functional status: Implications for management of persons with Alzheimer's disease

IntroductionInformal caregiving is an essential element of health-care delivery. Little data describes how caregivers structure care recipients' lives and impact their functional status.MethodsWe performed observational studies of community dwelling persons with dementia (PWD) to measure functional status by simultaneous assessment of physical activity (PA) and lifespace (LS). We present data from two caregiver/care-recipient dyads representing higher and average degrees of caregiver involvement.ResultsWe acquired >42,800 (subject 1); >41,300 (subject 2) PA data points and >154,500 (subject 1); >119,700 (subject 2) LS data points over 15 months of near continuous observation. PA and LS patterns provided insights into the caregiver's role in structuring the PWD's day-to-day function and change in function over time.DiscussionWe show that device-enabled functional monitoring (FM) can successfully gather and display data at resolutions required for dementia care studies. Objective quantification of individual caregiver/care-recipient dyads provides opportunities to implement patient-centered care

Smart Mat for Respiratory Activity Detection: Study in a Clinical Setting

We discuss in this paper a study of a smart and unobtrusive mattress in a clinical setting on a population with cardiorespiratory problems. Up to recently, the vast majority of studies with unobtrusive sensors are done with healthy populations. The unobtrusive monitoring of the Respiratory Rate (RR) is essential for proposing better diagnoses. Thus, new industrial and research activity on smart mattresses is targeting respiratory rate in an Internet-of-Things (IoT) context. In our work, we are interested in the performances of a microbend fiber optic sensor (FOS) mattress on 81 subjects admitted in the Cardiac Intensive Care Unit (CICU) by estimating the RR from their ballistocardiograms (BCG). Our study proposes a new RR estimator, based on harmonic plus noise models (HNM) and compares it with known estimators such as MODWT and CLIE. The goal is to examine, using a more representative and bigger dataset, the performances of these methods and of the smart mattress in general. Results of applying these three estimators on the BCG show that MODWT is more accurate with an average mean absolute error (MAE) of 1.97 ± 2.12 BPM. However, the HNM estimator has space for improvements with estimation errors of 2.91 ± 4.07 BPM. The smart mattress works well within a standard RR range of 10–20 breaths-per-minute (BPM) but gets less accurate with a bigger range of estimation. These results highlight the need to test these sensors in much more realistic contexts