Electrodermal activity (EDA) provides a direct indicator of sympathetic nervous system arousal through changes in skin conductance. However, wearable EDA sensing poses challenges such as inconsistent skin contact, electrode impedance variability, motion artifacts, and power constraints. To address these issues, this study presents mobile EDA (mEDA), a compact device driven by a stabilized direct-current source. A validation study was conducted on ten healthy adult participants in a time-synchronized protocol to collect data from BIOPAC and mEDA concurrently. mEDA recordings employed gel electrodes for P1-P5 and dry (textile) electrodes for P6-P10, while the BIOPAC MP160 system used gel electrodes for all participants. Participants underwent a 30-minute protocol of resting, deep breathing, and three cognitive tasks. The preprocessing pipeline consisted of low-pass filter and artifact (sharp peaks and flat line) removal. Cleaned signals were converted into frequency domain components for decomposition into low and high frequency components, skin conductance level (SCL), and skin conductance response (SCR) respectively. SCL and SCR were converted back to the time domain to analyze performance metrics between both devices. Pearson correlation, coherence, and Dynamic Time Warping (DTW) were computed on SCL, while zero-crossing peaks were counted for SCR analysis. With gel electrodes, the average Pearson correlation was 0.92 and the SCR peak count difference was 38. For textile electrodes, the correlation was 0.88 with a peak count difference of 119. Both configurations achieved coherence above 0.95 and DTW below 0.5 for most participants. These results demonstrate mEDA's reliable performance in capturing both tonic and phasic EDA across electrode configurations.

This manuscript investigates behavioral differences between children with ADHD and neurotypical children (6-11y) using smartwatch data from both hands collected during four classroom-like activities. Our study analyzes accelerometer and gyroscope data to identify distinct behavioral patterns, that can be used to enhance diagnostic methods for ADHD. We pre-processed the smartwatch data and calculated statistical features such as mean, median, standard deviation, kurtosis, and sum of consecutive differences. Wrist angles were used to differentiate on-task and off-task states. Hypothesis testing indicated that certain activities and features, such as the calculated angle of the y-axis in activity 1 (magnetic tiles) and the 'sum of consecutive differences' in activity 4 (drawing), were significant in distinguishing between the two groups. The left hand (non-dominant hand) generally provided more discriminative data than the right hand (dominant hand). Our findings suggest that specific patterns in smartwatch data can differentiate between ADHD and neurotypical children during classroom activities. These insights hold promise for developing more objective and data-driven diagnostic tools for ADHD.

The field of wearable sensors has witnessed remarkable progress in recent years, enabling real-time monitoring of musculoskeletal biomechanics. Musculoskeletal injuries require long-term management through medications and physical therapy to ensure a smooth recovery. Assessment of a patient’s range of motion (ROM) is an essential component of physical therapy. However, existing methods for measuring ROM are either analog or require expensive equipment. In this work, we present ElboSense, a smart textile-based sensing solution to monitor elbow motion precisely. ElboSense is embedded with a novel yarn-like capacitive strain sensor made with braided composite structures of multiple filament strands in a helical pattern. We seamlessly integrated the sensor into an elbow brace and developed an embedded system for data acquisition. The work conducted a study with eight healthy adult participants who performed a regulated elbow movement exercise (flexion and extension) 10 times. We validated the sensor measurements against a reliable optical motion-capture system. The average cross-correlation was found to be 0.91. Pearson’s correlation coefficient analysis showed 0.95 for peak-to-peak and 0.98 for valley-to-valley. All the preliminary results indicate a good agreement and reliability as a potential sensing solution to monitor the range of motion during elbow exercises.

Parkinson’s disease (PD) is a neurological disorder that nearly affects 1 million people in the US alone. PD causes involuntary motor fluctuations, resulting in bradykinesia (slowness of movement), tremors, stiffness, walking imbalance, and other complications. PD has no known cure; however, treatment exists through regular medication intake and therapy. Clinicians rate the progression of the symptoms based on standard clinical assessments during in-clinic visits that occur 2-3 times a year. In addition to self-reports from the patients, neurologists find it helpful if motor symptoms can be monitored objectively at in-home settings for better insights into disease progression. This work explores the impact of medication on motor symptoms, such as the spatial-temporal and frequency features of the finger-tapping motor exam. In our prior research, people with Parkinson’s Disease (n=4) were given to wear a pair of smart gloves that collected data from 156 sessions of the finger-tapping exam. To determine speed and amplitude in the finger tapping exam, we explored two different adaptive peak detection algorithms to detect peaks in the raw data. Human raters detected peaks to provide ground truth for analyzing the accuracy of the peak detectors. The average mean absolute error percentage for peak detection algorithms was 16%. We investigated the effect of medication wear off and our findings suggest a statistically significant difference in the dominant frequency and the peak-to-peak difference in time (p-value: 0.0256, 0.0366). A greater variability was also observed when medication effects were minimal. These findings are promising and encourage us to further investigate the smart gloves in a larger and longitudinal in-home study on Parkinson’s.

A rising number of preterm babies demands innovative solutions to monitor them in the Neonatal Intensive Care Unit (NICU) continuously. NICU monitors various kinds of vital signs. Among them, there is a strong demand for an accurate and sophisticated technology to monitor respiration rate (RR) and detect critical events such as apnea. Existing solutions for RR monitoring either rely on the indirect measurements from thoracic impedance or other invasive techniques posing discomfort and risk of infections to babies. Also, multiple wire loops lying around babies hinder the delivery of parental and clinical care. Motivated by this need, we have designed an Internet-of-Things (IoT) based smart textile chest belt called "Baby-Guard" to monitor RR and detect apnea. The Baby-Guard is a neonatal wearable system consisting of a sensor belt, a wearable embedded system, and an edge computing device. The sensor belt consists of textile-based pressure sensors and an Inertial Measurement Unit (IMU). The wearable system consists of a microcontroller equipped with wireless connectivity and power management. The edge computing device (ECD) connects with the wearable system through an MQTT networking architecture. ECD hosts signal processing and computing services to extract RR and detect apnea. We conducted simulation experiments using a high-fidelity, programmable NICU baby mannequin. We found an average error of 0.89 BrPM in breathing rate and ~97 percent accuracy in apnea detection. Computation and communication latencies were found to be ~66 and 22 ms, respectively. The Baby-Guard showed potential to be a wireless infant monitoring system in the NICU settings.

The purpose of this article is to inform readers about technical challenges that we encountered when assembling exome sequencing data from the 'Simplifying Complex Exomes' (SIMPLEXO) consortium-whose mandate is the discovery of novel genes predisposing to breast and ovarian cancers. Our motivation is to share these obstacles-and our solutions to them-as a means of communicating important technical details that should be discussed early in projects involving massively parallel sequencing.