Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

In this study, we propose a new mounting method to improve accelerometer sensing performance in the 50 Hz–10 kHz frequency band for knee sound measurement. The proposed method includes a thin double-sided adhesive tape for mounting and a 3D-printed custom-designed backing prototype. In our mechanical setup with an electrodynamic shaker, the measurements showed a 13 dB increase in the accelerometer's sensing performance in the 1–10 kHz frequency band when it is mounted with the craft tape under 2 N backing force applied through low-friction tape. As a proof-of-concept study, knee sounds of healthy subjects (n = 10) were recorded. When the backing force was applied, we observed statistically significant (p < 0.01) incremental changes in spectral centroid, spectral roll-off frequencies, and high-frequency (1–10 kHz) root-mean-square (RMS) acceleration, while low-frequency (50 Hz–1 kHz) RMS acceleration remained unchanged. The mean spectral centroid and spectral roll-off frequencies increased from 0.8 kHz and 4.15 kHz to 1.35 kHz and 5.9 kHz, respectively. The mean high-frequency acceleration increased from 0.45 mg_RMS to 0.9 mg_RMS with backing. We showed that the backing force improves the sensing performance of the accelerometer when mounted with the craft tape and the proposed backing prototype. This new method has the potential to be implemented in today's wearable systems to improve the sensing performance of accelerometers in knee sound measurements.

Objective: Rheumatoid arthritis (RA) is a chronic inflammatory syndrome that features painful and destructive joint disease. Aggressive disease-modifying treatment can result in reduced symptoms and protection from irreversible joint damage; however, assessment of treatment efficacy is currently based largely on subjective measures of patient and physician impressions. In this work, we address this compelling need to provide an accurate and quantitative capability for monitoring joint health in patients with RA. Methods: Joint acoustic emissions (JAEs), electrical bioimpedance (EBI), and kinematics were measured noninvasively from 11 patients with RA over the course of three weeks using a custom multimodal sensing brace, resulting in 49 visits with JAE recordings and 43 with EBI recordings. Features derived from all sensing modalities were fed into a linear discriminant analysis (LDA) model to predict disease activity according to the validated disease activity index (the DAS28-ESR). Erythrocyte sedimentation rate (ESR) was predicted using ridge regression and classified into a high or low class using LDA. Results: DAS28-ESR level was predicted with an area under the receiver operating characteristic curve (AUC) of 0.82. With JAEs alone, we were able to track intrasubject differences in the disease activity score as well as classify ESR level with an AUC of 0.93. The majority of patients reported both an interest and ability to use the brace at home for longitudinal monitoring. Conclusion: This work demonstrates the ability to detect RA disease activity using noninvasive sensing. Significance: This system has the potential to improve RA disease activity monitoring by giving treating clinicians objective data that can be acquired independent of a face-to-face clinic visit.

Respiratory rate (RR) is an important vital sign to monitor outside the clinic, particularly during physiological challenges such as exercise; unfortunately, ambulatory measurement devices for RR are typically obtrusive and inaccurate. The objective of this work is to allow for accurate and robust RR monitoring with a convenient and small chest-worn wearable patch during walking and exercise recovery periods. Methods: To estimate RR from the wearable patch, respiratory signals were first extracted from electrocardiogram (ECG), photoplethysmogram (PPG), and seismocardiogram (SCG) signals. The optimal channel in each signal was adaptively selected using respiratory quality index based on Fast Fourier Transform (RQI _FFT ). Next, we proposed modality attentive (MA) fusion–which merged spectral-temporal information from different modalities–to address motion artifacts during walking. The fused output was subsequently denoised using a U-Net-based deep learning model and used for final estimation. A dataset of N = 17 subjects was collected to validate RR estimated during three types of activities: stationary activities, walking (including six-minute-walk-test), and running. Major Results: Combining and denoising ECG and PPG data using MA fusion and the U-Net achieved the lowest mean absolute error (2.21 breaths per minute [brpm]) during walking. After rejecting a small portion of the data (coverage=84.43%) using RQI _FFT , this error was further reduced to 1.59 brpm, which was comparable to the state-of-the-art methods. Conclusion: Applying adaptive channel selection, MA fusion, and U-Net denoising achieved accurate RR estimation from a small chest-worn wearable patch. Significance: This work can enable cardiopulmonary monitoring applications in less controlled setting.

This study was undertaken to determine if knee acoustic emissions (KAEs) measured at the point of care with a wearable device can classify knees with pre-radiographic osteoarthritis (pre-OA) from healthy knees. We performed a single-center cross-sectional observational study comparing KAEs in healthy knees to knees with clinical symptoms compatible with knee OA that did not meet classification criteria for radiographic knee OA. KAEs were measured during scripted maneuvers performed in clinic exam rooms or similarly noisy medical center locations in healthy ( ${n}$ = 20), pre-OA ( ${n}$ = 11), and, for comparison, OA ( ${n}$ = 12) knees. Acoustic features were extracted from the KAEs and used to train models to classify pre-OA, OA, and control knees with logistic regression. Model performance was measured and optimized with leave-one-out cross-validation. Regressive sensitivity analysis was performed to combine acoustic information from individual maneuvers to further optimize performance. Test–retest reliability of KAEs was measured with intraclass correlation analysis. Classification models trained with KAEs were accurate for both pre-OA and OA (94% accurate, 0.96 and 0.99 area under a receiver operating characteristic curve (AUC), respectively). Acoustic features selected for use in the optimized models had high test–retest reliability by intrasession and intersession intraclass correlation analysis (mean intraclass correlation coefficient 0.971 ± 0.08 standard deviation). Analysis of KAEs measured in acoustically uncontrolled medical settings using an easily accessible wearable device accurately classified pre-OA knees from healthy control knees in our small cohort. Accessible methods of identifying pre-OA could enable regular joint health monitoring and improve OA treatment and rehabilitation outcomes.

Objective: To evaluate whether non-invasive knee sound measurements can provide information related to the underlying structural changes in the knee following meniscal tear. These changes are explained using an equivalent vibrational model of the knee-tibia structure. Methods: First, we formed an analytical model by modeling the tibia as a cantilever beam with the fixed end being the knee. The knee end was supported by three lumped components with features corresponding with tibial stiffnesses, and meniscal damping effect. Second, we recorded knee sounds from 46 healthy legs and 9 legs with acute meniscal tears (n = 34 subjects). We developed an acoustic event (“click”) detection algorithm to find patterns in the recordings, and used the instrumental variable continuous-time transfer function estimation algorithm to model them. Results: The knee sound measurements yielded consistently lower fundamental mode decay rate in legs with meniscal tears (16 ±13 s ^{- 1} ) compared to healthy legs (182 ±128 s ^{- 1} ), p <; 0.05. When we performed an intra-subject analysis of the injured versus contralateral legs for the 9 subjects with meniscus tears, we observed significantly lower natural frequency and damping ratio (first mode results for healthy: f _n1 = 115 ±44 Hz, ξ ₁ = 0.098 ±0.022, injured: f _n1 = 67 ±10 Hz, ξ ₁ = 0.036 ±0.029) for the first three vibration modes (p <; 0.05). These results agreed with the theoretical expectations gleaned from the vibrational model. Significance: This combined analytical and experimental method improves our understanding of how vibrations can describe the underlying structural changes in the knee following meniscal tear, and supports their use as a tool for future efforts in non-invasively diagnosing meniscal tear injuries.