Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study.

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis. In this work, LS and Impedance Pneumography (IP) measurements were obtained from 10 healthy volunteers while performing normal and forced-expiratory (FE) breathing maneuvers using our wearable IP and respiratory sounds (WIRS) system. Simultaneous auscultation was performed with the Eko CORE stethoscope (EKO). The breathing-phase context was extracted from the IP signals and used to compute phase-by-phase (Inspiratory (I), expiratory (E), and their ratio (I:E)) and breath-by-breath acoustic features. Their individual and added value was then elucidated through machine learning analysis. We found that the phase-contextualized features effectively captured the underlying acoustic differences between deep and FE breaths, yielding a maximum F1 Score of 84.1 ±11.4% with the phase-by-phase features as the strongest contributors to this performance. Further, the individual phase-contextualized models outperformed the traditional breath-by-breath models in all cases. The validity of the results was demonstrated for the LS obtained with WIRS, EKO, and their combination. These results suggest that incorporating breathing-phase context may enhance computerized LS analysis. Hence, multimodal sensing systems that enable this, such as WIRS, have the potential to advance LS clinical utility beyond traditional manual auscultation and improve patient care.

Mid-Activity and At-Home Wearable Bioimpedance Elucidates an Interpretable Digital Biomarker of Muscle Fatigue.

OBJECTIVE: Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue. Unfortunately, the current state-of-the-art wearable systems for muscle fatigue tracking suffer from either low specificity or poor usability. METHODS: We propose using dual-frequency bioimpedance analysis (DFBIA) to non-invasively assess intramuscular fluid dynamics and thereby muscle fatigue. A wearable DFBIA system was developed to measure leg muscle fatigue of 11 individuals during a 13-day protocol consisting of exercise and unsupervised at-home portions. RESULTS: We derived a digital biomarker of muscle fatigue, fatigue score, from the DFBIA signals that was able to estimate the percent reduction in muscle force during exercise with repeated-measures Pearson's r = 0.90 and mean absolute error (MAE) of 3.6%. This fatigue score also estimated delayed onset muscle soreness with repeated-measures Pearson's r = 0.83 and MAE = 0.83. Using at-home data, DFBIA was strongly associated with absolute muscle force of participants (n = 198, p < 0.001). CONCLUSION: These results demonstrate the utility of wearable DFBIA for non-invasively estimating muscle force and pain through the changes in intramuscular fluid dynamics. SIGNIFICANCE: The presented approach may inform development of future wearable systems for quantifying muscle health and provide a novel framework for athletic performance optimization and injury prevention.

Validating Adhesive-Free Bioimpedance of the Leg in Mid-Activity and Uncontrolled Settings.

OBJECTIVE: Musculoskeletal health monitoring is limited in everyday settings where patient symptoms can substantially change - delaying treatment and worsening patient outcomes. Wearable technologies aim to quantify musculoskeletal health outside clinical settings but sensor constraints limit usability. Wearable localized multi-frequency bioimpedance assessment (MFBIA) shows promise for tracking musculoskeletal health but relies on gel electrodes, hindering extended at-home use. Here, we address this need for usable technologies for at-home musculoskeletal health assessment by designing a wearable adhesive-free MFBIA system using textile electrodes in extended uncontrolled mid-activity settings. METHODS: An adhesive-free multimodal wearable leg MFBIA system was developed in-lab under realistic conditions (5 participants, 45 measurements). Mid-activity textile and gel electrode MFBIA was compared across multiple compound movements (10 participants). Accuracy in tracking long-term changes in leg MFBIA was assessed by correlating gel and textile MFBIA simultaneously recorded in uncontrolled settings (10 participants, 80+ measurement hours). RESULTS: Mid-activity MFBIA measurements with textile electrodes agreed highly with (ground truth) gel electrode measurements (average [Formula: see text], featuring <1-Ohm differences (0.618 ± 0.340 Ω) across all movements. Longitudinal MFBIA changes were successfully measured in extended at-home settings (repeated measures r = 0.84). Participant responses found the system to be comfortable and intuitive (8.3/10), and all participants were able to don and operate the system independently. CONCLUSION: This work demonstrates wearable textile electrodes can be a viable substitute for gel electrodes when monitoring leg MFBIA in dynamic, uncontrolled settings. SIGNIFICANCE: Adhesive-free MFBIA can improve healthcare by enabling robust wearable musculoskeletal health monitoring in at-home and everyday settings.

Quantifying Rheumatoid Arthritis Disease Activity Using a Multimodal Sensing Knee Brace.

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.

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers.

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.

An Integrated Multimodal Knee Brace Enabling Mid-Activity Tracking for Joint Health Assessment.

Developments in wearable technologies created opportunities for non-invasive joint health assessment while subjects perform daily activities during rehabilitation and recovery. However, existing state-of-art solutions still require a health professional or a researcher to set up the device, and most of them are not convenient for at-home use. In this paper, we demonstrate the latest version of the multimodal knee brace that our lab previously developed. This knee brace utilizes four sensing modalities: joint acoustic emissions (JAEs), electrical bioimpedance (EBI), activity and temperature. We designed custom printed-circuit boards and developed firmware to acquire high quality data. For the brace material, we used a commercial knee brace and modified it for the comfort of patients as well as to secure all electrical connections. We updated the electronics to enable rapid EBI measurements for mid-activity tracking. The performance of the multimodal knee brace was evaluated through a proof-of-concept human subjects study (n=9) with 2 days of measurement and 3 sessions per day. We obtained consistent EBI data with less than 1 Ω variance in measured impedance within six full frequency sweeps (each sweep is from 5 kHz to 100 kHz with 256 frequency steps) from each subject. Then, we asked subjects to perform 10 unloaded knee flexion/extensions, while we measured continuous 5 kHz and 100 kHz EBI at every 100 ms. The ratio of the range of reactance (ΔX5kHz/ΔX100kHz) was found to be less than 1 for all subjects for all cycles, which indicates lack of swelling and thereby a healthy joint. We also conducted intra and inter session reliability analysis for JAE recordings through intraclass correlation analysis (ICC), and obtained excellent ICC values (>0.75), suggesting reliable performance on JAE measurements. The presented knee brace could readily be used at home in future work for knee health monitoring of patients undergoing rehabilitation or recovery.

Quantifying Signal Quality for Joint Acoustic Emissions Using Graph-Based Spectral Embedding.

We present a new method for quantifying signal quality of joint acoustic emissions (JAEs) from the knee during unloaded flexion/extension (F/E) exercises. For ten F/E cycles, JAEs were recorded, in a clinical setting, from 34 healthy knees and 13 with a meniscus tear (n=24 subjects). The recordings were first segmented by F/E cycle and described using time and frequency domain features. Using these features, a symmetric k-nearest neighbor graph was created and described using a spectral embedding. We show how the underlying community structure of JAEs was comparable across joint health levels and was highly affected by artifacts. Each F/E cycle was scored by its distance from a diverse set of manually annotated, clean templates and removed if above the artifact threshold. We validate this methodology by showing an improvement in the distinction between the JAEs of healthy and injured knees. Graph community factor (GCF) was used to detect the number of communities in each recording and describe the heterogeneity of JAEs from each knee. Before artifact removal, there was no significant difference between the healthy and injured groups due to the impact of artifacts on the community construction. Following implementation of artifact removal, we observed improvement in knee health classification. The GCF value for the meniscus tear group was significantly higher than the healthy group (p<0.01). With more JAE recordings being taken in the clinic and at home, this paper addresses the need for a robust artifact removal method which is necessary for an accurate description of joint health.

A Novel Accelerometer Mounting Method for Sensing Performance Improvement in Acoustic Measurements From the Knee.

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 mgRMS to 0.9 mgRMS 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.

An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions.

The characteristics of joint acoustic emissions (JAEs) measured from the knee have been shown to contain information regarding underlying joint health. Researchers have developed methods to process JAE measurements and combined them with machine learning algorithms for knee injury diagnosis. While these methods are based on JAEs measured in controlled settings, we anticipate that JAE measurements could enable accessible and affordable diagnosis of acute knee injuries also in field-deployable settings. However, in such settings, the noise and interference would be greater than in sterile, laboratory environments, which could decrease the performance of existing knee health classification methods using JAEs. To address the need for an objective noise and interference detection method for JAE measurements as a step towards field-deployable settings, we propose a novel experimental data augmentation method to locate and then, remove the corrupted parts of JAEs measured in clinical settings. In the clinic, we recruited 30 participants, and collected data from both knees, totaling 60 knees (36 healthy and 24 injured knees) to be used subsequently for knee health classification. We also recruited 10 healthy participants to collect artifact and joint sounds (JS) click templates, which are audible, short duration and high amplitude JAEs from the knee. Spectral and temporal features were extracted, and clinical data was augmented in five-dimensional subspace by fusing the existing clinical dataset into experimentally collected templates. Then knee scores were calculated by training and testing a linear soft classifier utilizing leave-one-subject-out cross-validation (LOSO-CV). The area under the curve (AUC) was 0.76 for baseline performance without any window removal with a logistic regression classifier (sensitivity = 0.75, specificity = 0.78). We obtained an AUC of 0.86 with the proposed algorithm (sensitivity = 0.80, specificity = 0.89), and on average, 95% of all clinical data was used to achieve this performance. The proposed algorithm improved knee health classification performance by the added information through identification and collection of common artifact sources in JAE measurements. This method when combined with wearable systems could provide clinically relevant supplementary information for both underserved populations and individuals requiring point-of-injury diagnosis in field-deployable settings.

Detection of Meniscal Tear Effects on Tibial Vibration Using Passive Knee Sound Measurements.

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: [Formula: see text]injured: [Formula: see text]) 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.