Mental health disorders are often comorbid, highlighting the need for predictive models that can address multiple outcomes simultaneously. Multi-task learning (MTL) provides a principled approach to jointly model related conditions, enabling shared representations that improve robustness and reduce reliance on large disorder-specific datasets. In this work, we present a tri-modal speech-based framework that integrates text transcriptions, acoustic landmarks, and vocal biomarkers within a large language model (LLM)-driven architecture. Beyond static assessments, we introduce a longitudinal modeling strategy that captures temporal dynamics across repeated clinical interactions, offering deeper insights into symptom progression and relapse risk. Our MTL design simultaneously predicts depression relapse, suicidal ideation, and sleep disturbances, reflecting the comorbid nature of adolescent mental health. Evaluated on the Depression Early Warning (DEW) dataset, the proposed longitudinal trimodal MTL model achieves a balanced accuracy of 70.8%, outperforming unimodal, single-task, and non-longitudinal baselines. These results demonstrate the promise of combining MTL with longitudinal monitoring for scalable, noninvasive prediction of adolescent mental health outcomes.
Prone positioning improves oxygenation in patients with acute respiratory distress syndrome (ARDS), but traditional methods are labor-intensive and complicates continuous monitoring capabilities. Building on a previously developed mechanically assisted proning vest (V/Q vest), this work focuses on integrating and validating a multi-modal sensor network for non-invasive respiratory monitoring. The system incorporates thoracic bioimpedance electrodes and microphones to derive respiratory parameters, including tidal volume (TV), respiratory rate (RR), phase timing and lung sounds. Data from ten healthy adults were collected during controlled breathing tasks in seated and supine postures. Both seated and supine positions showed strong agreement between IP-derived and spirometer-derived TV, with $\mathbf{R}^{\mathbf{2}} \boldsymbol{=} \mathbf{0. 9 0}$, MAE $\boldsymbol{=} \mathbf{0. 1 6} \boldsymbol{\pm} \mathbf{0. 0 7} \mathbf{L}$ in the seated posture and $\mathbf{R}^{\mathbf{2}} =0.94$, $\text{MAE}=0.12 \pm 0.05 \mathrm{L}$ in the supine posture. A flow-based correction method was applied to decouple lung sound intensity from airflow. In the seated posture, mean repeated-measures correlation between uncorrected sound intensity and flow was $r= 0.72$, dropping to $\mathbf{r}=0.01$ after correction. In the supine posture, $\mathbf{r}=0.71$ before correction and $\mathbf{r}=-0.07$ after correction, demonstrating successful flow-decoupling. These results support the feasibility of integrating real-time impedance and acoustic sensing into a therapeutic proning vest, laying the foundation for localized lung monitoring and data-driven respiratory management in critical care.
Incorporating mobile health (mHealth) into cancer care holds growing promise for improving quality and access, especially in rural and disadvantaged populations, who continue to face disparities in distress prevalence and healthcare engagement. Regardless, sustained engagement with mHealth solutions remains challenging due to motivational, technical, and usability barriers. We developed and piloted a HIPAA-compliant mHealth app, Assuage, in a 45-day longitudinal study with seven cancer patients, the majority from rural backgrounds. Participants alternated between daily and every-other-day symptom reporting. Our findings reflect adherence trends across reporting frequencies, stable and favorable system usability measures before and after the study, and qualitative insights about participants' willingness to use mHealth for symptom self-reporting.
Continuous glucose monitoring (CGM) devices provide critical real-time data but remain minimally invasive and require frequent replacement. This study presents a novel, personalized machine learning approach for non-invasive glucose monitoring using radiofrequency (RF) spectroscopy to address these limitations. To simulate real-world usage and ensure clinical relevance, we developed a model for a single individual using data collected during standardized meals. The model was trained and tested on data collected on separate days, ensuring that the training and test sets are drawn from distinct, non-overlapping time periods. A comprehensive machine learning pipeline was validated using 3,101 spectral features (400-3500 MHz) combined with contextual data to predict glucose levels. Our best-performing model, multi-layer perception regressor (MLP), achieved a Mean Absolute Relative Difference (MARD) of 11.6%. These findings demonstrate that a personalized machine learning model holds potential to predict glucose non-invasively. This highlights a promising path toward a more user-friendly and sustainable solution for continuous glucose management
This paper presents the first reported measurements of impedance plethysmography (IPG) signals directly from the human fingertip for the purpose of evaluating peripheral hemodynamics. The human fingertip serves as a vital non-invasive access point for cardiovascular monitoring, with photoplethysmography (PPG) widely adopted for its assessment of superficial blood flow. However, the optical sensing depth of PPG limits its capability to fully characterize deeper peripheral hemodynamics. Here, we developed a novel four-electrode fingertip IPG prototype and measurement protocol, enabling the consistent capture of pulsatile impedance waveforms reflecting blood volume changes within the digit. Preliminary data from two participants illustrates clear signal reproducibility and enables the derivation of key pulse wave metrics, including pulse arrival time (PAT). Given IPG's electrical sensing principle, distinct from PPG's optical approach, our findings suggest that fingertip IPG offers a complementary “new window” into peripheral circulation. This work establishes fingertip IPG as a significant advancement for non-invasive physiological monitoring, holding promise for both clinical applications and wearable health sensing.
