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.

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.

Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status.

OBJECTIVE: To study knee acoustical emission patterns in subjects with acute knee injury immediately following injury and several months after surgery and rehabilitation. METHODS: We employed an unsupervised graph mining algorithm to visualize heterogeneity of the high-dimensional acoustical emission data, and then to derive a quantitative metric capturing this heterogeneity-the graph community factor (GCF). A total of 42 subjects participated in the studies. Measurements were taken once each from 33 healthy subjects with no known previous knee injury, and twice each from 9 subjects with unilateral knee injury: first, within seven days of the injury, and second, 4-6 months after surgery when the subjects were determined to start functional activities. Acoustical signals were processed to extract time and frequency domain features from multiple time windows of the recordings from both knees, and k-nearest neighbor graphs were then constructed based on these features. RESULTS: The GCF calculated from these graphs was found to be 18.5 ± 3.5 for healthy subjects, 24.8 ± 4.4 (p = 0.01) for recently injured, and 16.5 ± 4.7 (p = 0.01) at 4-6 months recovery from surgery. CONCLUSION: The objective GCF scores changes were consistent with a medical professional's subjective evaluations and subjective functional scores of knee recovery. SIGNIFICANCE: Unsupervised graph mining to extract GCF from knee acoustical emissions provides a novel, objective, and quantitative biomarker of knee injury and recovery that can be incorporated with a wearable joint health system for use outside of clinical settings, and austere/under resourced conditions, to aid treatment/therapy.

Wearable knee health system employing novel physiological biomarkers.

Knee injuries and chronic disorders, such as arthritis, affect millions of Americans, leading to missed workdays and reduced quality of life. Currently, after an initial diagnosis, there are few quantitative technologies available to provide sensitive subclinical feedback to patients regarding improvements or setbacks to their knee health status; instead, most assessments are qualitative, relying on patient-reported symptoms, performance during functional tests, and physical examinations. Recent advances have been made with wearable technologies for assessing the health status of the knee (and potentially other joints) with the goal of facilitating personalized rehabilitation of injuries and care for chronic conditions. This review describes our progress in developing wearable sensing technologies that enable quantitative physiological measurements and interpretation of knee health status. Our sensing system enables longitudinal quantitative measurements of knee sounds, swelling, and activity context during clinical and field situations. Importantly, we leverage machine-learning algorithms to fuse the low-level signal and feature data of the measured time series waveforms into higher level metrics of joint health. This paper summarizes the engineering validation, baseline physiological experiments, and human subject studies-both cross-sectional and longitudinal-that demonstrate the efficacy of using such systems for robust knee joint health assessment. We envision our sensor system complementing and advancing present-day practices to reduce joint reinjury risk, to optimize rehabilitation recovery time for a quicker return to activity, and to reduce health care costs.

Wearable Vector Electrical Bioimpedance System to Assess Knee Joint Health.

OBJECTIVE: We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health. METHODS: Five separate experiments were performed to demonstrate the: 1) ability of the EBI system to assess knee injury and recovery; 2) interday variability of knee EBI measurements; 3) sensitivity of the system to small changes in interstitial fluid volume; 4) reducing the error of EBI measurements using acceleration signals; and 5) use of the system with dry electrodes integrated to a wearable knee wrap. RESULTS: 1) The absolute difference in resistance ( R) and reactance (X) from the left to the right knee was able to distinguish injured and healthy knees (p < 0.05); the absolute difference in R decreased significantly (p < 0.05) in injured subjects following rehabilitation. 2) The average interday variability (standard deviation) of the absolute difference in knee R was 2.5 Ω and for X was 1.2 Ω. 3) Local heating/cooling resulted in a significant decrease/increase in knee R (p < 0.01). 4) The proposed subject position detection algorithm achieved 97.4% leave-one subject out cross-validated accuracy and 98.2% precision in detecting when the subject is in the correct position to take measurements. 5) Linear regression between the knee R and X measured using the wet electrodes and the designed wearable knee wrap were highly correlated ( R2 = 0.8 and 0.9, respectively). CONCLUSION: This study demonstrates the use of wearable EBI measurements in monitoring knee joint health. SIGNIFICANCE: The proposed wearable system has the potential for assessing knee joint health outside the clinic/lab and help guide rehabilitation.

Quantifying the Consistency of Wearable Knee Acoustical Emission Measurements During Complex Motions.

Knee-joint sounds could potentially be used to noninvasively probe the physical and/or physiological changes in the knee associated with rehabilitation following acute injury. In this paper, a system and methods for investigating the consistency of knee-joint sounds during complex motions in silent and loud background settings are presented. The wearable hardware component of the system consists of a microelectromechanical systems microphone and inertial rate sensors interfaced with a field programmable gate array-based real-time processor to capture knee-joint sound and angle information during three types of motion: flexion-extension (FE), sit-to-stand (SS), and walking (W) tasks. The data were post-processed to extract high-frequency and short-duration joint sounds (clicks) with particular waveform signatures. Such clicks were extracted in the presence of three different sources of interference: background, stepping, and rubbing noise. A histogram-vector Vn(→) was generated from the clicks in a motion-cycle n, where the bin range was 10°. The Euclidean distance between a vector and the arithmetic mean Vav(→) of all vectors in a recording normalized by the Vav(→) is used as a consistency metric dn. Measurements from eight healthy subjects performing FE, SS, and W show that the mean (of mean) consistency metric for all subjects during SS (µ [ µ (dn)] = 0.72 in silent, 0.85 in loud) is smaller compared with the FE (µ [ µ (dn)] = 1.02 in silent, 0.95 in loud) and W ( µ [ µ (dn)] = 0.94 in silent, 0.97 in loud) exercises, thereby implying more consistent click-generation during SS compared with the FE and W. Knee-joint sounds from one subject performing FE during five consecutive work-days (µ [ µ (dn) = 0.72) and five different times of a day (µ [ µ (dn) = 0.73) suggests high consistency of the clicks on different days and throughout a day. This work represents the first time, to the best of our knowledge, that joint sound consistency has been quantified in ambulatory subjects performing every-day activities (e.g., SS, walking). Moreover, it is demonstrated that noise inherent with joint-sound recordings during complex motions in uncontrolled settings does not prevent joint-sound-features from being detected successfully.

Novel Methods for Sensing Acoustical Emissions From the Knee for Wearable Joint Health Assessment.

OBJECTIVE: We present the framework for wearable joint rehabilitation assessment following musculoskeletal injury. We propose a multimodal sensing (i.e., contact based and airborne measurement of joint acoustic emission) system for at-home monitoring. METHODS: We used three types of microphones-electret, MEMS, and piezoelectric film microphones-to obtain joint sounds in healthy collegiate athletes during unloaded flexion/extension, and we evaluated the robustness of each microphone's measurements via: 1) signal quality and 2) within-day consistency. RESULTS: First, air microphones acquired higher quality signals than contact microphones (signal-to-noise-and-interference ratio of 11.7 and 12.4 dB for electret and MEMS, respectively, versus 8.4 dB for piezoelectric). Furthermore, air microphones measured similar acoustic signatures on the skin and 5 cm off the skin (∼4.5× smaller amplitude). Second, the main acoustic event during repetitive motions occurred at consistent joint angles (intra-class correlation coefficient ICC(1, 1) = 0.94 and ICC(1, k) = 0.99). Additionally, we found that this angular location was similar between right and left legs, with asymmetry observed in only a few individuals. CONCLUSION: We recommend using air microphones for wearable joint sound sensing; for practical implementation of contact microphones in a wearable device, interface noise must be reduced. Importantly, we show that airborne signals can be measured consistently and that healthy left and right knees often produce a similar pattern in acoustic emissions. SIGNIFICANCE: These proposed methods have the potential for enabling knee joint acoustics measurement outside the clinic/lab and permitting long-term monitoring of knee health for patients rehabilitating an acute knee joint injury.

Real-time activity classification in a wearable system prototype for knee health assessment via joint sounds.

An algorithm for performing activity classification for a joint health assessment system using acoustical emissions from the knee is presented. The algorithm was refined based on linear acceleration data from the shank and the thigh sampled at 100 Hz/ch and collected from eight healthy subjects performing unloaded flexion-extension and sit-to-stand motions. The algorithm was implemented on a field-programmable gate array (FPGA)-based processor and has been validated in realtime on a subject performing two minutes of activities consisting of flexion-extension, sit-to-stand, and other motions while standing. When an activity is detected, the algorithm generates an enable signal for high throughput data acquisition of knee joint sounds using two airborne microphones (100 kHz/ch) and two single-axis gyroscope and accelerometer pairs (1 kHz/ch). This approach can facilitate energy-efficient recording of joint sound signatures in the context of flexion-extension and sit-to-stand activities from freely-moving subjects throughout the day, potentially providing a means of evaluating rehabilitation status, for example, following acute knee injury.