Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.

INTRODUCTION: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. METHODS: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. RESULTS: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho = 0.529, p < 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho = -0.441, p < 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1 ± 1.2% and 76.3 ± 1.3%, respectively. DISCUSSION: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.

Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer.

This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies.

Taking a Load Off: User Perceptions of Smart Offloading Walkers for Diabetic Foot Ulcers Using the Technology Acceptance Model.

People with diabetic foot ulcers (DFUs) are commonly prescribed offloading walkers, but inadequate adherence to prescribed use can be a barrier to ulcer healing. This study examined user perspectives of offloading walkers to provide insight on ways to help promote adherence. Participants were randomized to wear: (1) irremovable, (2) removable, or (3) smart removable walkers (smart boot) that provided feedback on adherence and daily walking. Participants completed a 15-item questionnaire based on the Technology Acceptance Model (TAM). Spearman correlations assessed associations between TAM ratings with participant characteristics. Chi-squared tests compared TAM ratings between ethnicities, as well as 12-month retrospective fall status. A total of 21 adults with DFU (age 61.5 ± 11.8 years) participated. Smart boot users reported that learning how to use the boot was easy (ρ =-0.82, p≤ 0.001). Regardless of group, people who identified as Hispanic or Latino, compared to those who did not, reported they liked using the smart boot (p = 0.05) and would use it in the future (p = 0.04). Non-fallers, compared to fallers, reported the design of the smart boot made them want to wear it longer (p = 0.04) and it was easy to take on and off (p = 0.04). Our findings can help inform considerations for patient education and design of offloading walkers for DFUs.

An E-Textile Respiration Sensing System for NICU Monitoring: Design and Validation.

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing skin injuries, discomfort, and irritation. In addition, respiration rate (RR) monitoring in the NICU faces challenges of accuracy and clinical quality because RR is extracted from electrocardiogram (ECG). This research paper presents a design and validation of a smart textile pressure sensor system that addresses the existing challenges of medical monitoring in NICU. We designed two e-textile, piezoresistive pressure sensors made of Velostat for noninvasive RR monitoring; one was hand-stitched on a mattress topper material, and the other was embroidered on a denim fabric using an industrial embroidery machine. We developed a data acquisition system for validation experiments conducted on a high-fidelity, programmable NICU baby mannequin. We designed a signal processing pipeline to convert raw time-series signals into parameters including RR, rise and fall time, and comparison metrics. The results of the experiments showed that the relative accuracies of hand-stitched sensors were 98.68 (top sensor) and 98.07 (bottom sensor), while the accuracies of embroidered sensors were 99.37 (left sensor) and 99.39 (right sensor) for the 60 BrPM test case. The presented prototype system shows promising results and demands more research on textile design, human factors, and human experimentation.

Textile Knitted Stretch Sensors for Wearable Health Monitoring: Design and Performance Evaluation.

The advancement of smart textiles has led to significant interest in developing wearable textile sensors (WTS) and offering new modalities to sense vital signs and activity monitoring in daily life settings. For this, textile fabrication methods such as knitting, weaving, embroidery, and braiding offer promising pathways toward unobtrusive and seamless sensing for WTS applications. Specifically, the knitted sensor has a unique intermeshing loop structure which is currently used to monitor repetitive body movements such as breathing (microscale motion) and walking (macroscale motion). However, the practical sensing application of knit structure demands a comprehensive study of knit structures as a sensor. In this work, we present a detailed performance evaluation of six knitted sensors and sensing variation caused by design, sensor size, stretching percentages % (10, 15, 20, 25), cyclic stretching (1000), and external factors such as sweat (salt-fog test). We also present regulated respiration (inhale-exhale) testing data from 15 healthy human participants; the testing protocol includes three respiration rates; slow (10 breaths/min), normal (15 breaths/min), and fast (30 breaths/min). The test carried out with statistical analysis includes the breathing time and breathing rate variability. These testing results offer an empirically derived guideline for future WTS research, present aggregated information to understand the sensor behavior when it experiences a different range of motion, and highlight the constraints of the silver-based conductive yarn when exposed to the real environment.

Designing and testing a wearable, wireless fNIRS patch.

Optical brain monitoring using near infrared (NIR) light has got a lot of attention in order to study the complexity of the brain due to several advantages as oppose to other methods such as EEG, fMRI and PET. There are a few commercially available functional NIR spectroscopy (fNIRS) brain monitoring systems, but they are still non-wearable and pose difficulties in scanning the brain while the participants are in motion. In this work, we present our endeavors to design and test a low-cost, wireless fNIRS patch using NIR light sources at wavelengths of 770 and 830nm, photodetectors and a microcontroller to trigger the light sources, read photodetector's output and transfer data wirelessly (via Bluetooth) to a smart-phone. The patch is essentially a 3-D printed wearable system, recording and displaying the brain hemodynamic responses on smartphone, also eliminates the need for complicated wiring of the electrodes. We have performed rigorous lab experiments on the presented system for its functionality. In a proof of concept experiment, the patch detected the NIR absorption on the arm. Another experiment revealed that the patch's battery could last up to several hours with continuous fNIRS recording with and without wireless data transfer.