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

Non-Invasive Assessment of Cartilage Damage of the Human Knee Using Acoustic Emission Monitoring: A Pilot Cadaver Study

OBJECTIVE

Knee osteoarthritis is currently one of the top causes of disability in older population, a rate that will only increase in the future due to an aging population and the prevalence of obesity. However, objective assessment of treatment outcomes and remote evaluation are still in need of further development. Acoustic emission (AE) monitoring in knee diagnostics has been successfully adopted in the past; however, a wide discrepancy among the adopted AE techniques and analyses exists. This pilot study determined the most suitable metrics to differentiate progressive cartilage damage and the optimal frequency range and placement of AE sensors.

METHODS

Knee AEs were recorded in the 100-450 kHz and 15-200kH frequency ranges from a cadaver specimen in knee flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated.

RESULTS

AE events in the lower frequency range and the following parameters provided better distinction between intact and damaged knee: hit amplitude, signal strength, and absolute energy. The medial condyle area of the knee was less prone to artefacts and unsystematic noise. Multiple reopenings of the knee compartment in the process of introducing the damage negatively affected the quality of the measurements.

CONCLUSION

Results may improve AE recording techniques in future cadaveric and clinical studies.

SIGNIFICANCE

This was the first study to evaluate progressive cartilage damage using AEs in a cadaver specimen. The findings of this study encourage further investigation of joint AE monitoring techniques.

Test-Retest Reliability of Acoustic Emission Sensing of the Knee during Physical Tasks

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises.

PySio: A New Python Toolbox for Physiological Signal Visualization and Feature Analysis

In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing and feature analysis, Python has recently dethroned MATLAB with the rise of data science, machine learning and artificial intelligence. Hence, there is a compelling need for a Python package for physiological feature analysis and extraction to achieve compatibility with downstream models often trained in Python. Thus, we present a novel visualization and feature analysis Python toolbox, PySio, to enable rapid, efficient and user-friendly analysis of physiological signals. First, the user should import the signal-of-interest with the corresponding sampling rate. After importing, the user can either analyze the signal as it is, or can choose a specific region for more detailed analysis. PySio enables the user to (i) visualize and analyze the physiological signals (or user-selected segments of the signals) in time domain, (ii) study the signals (or user-selected segments of the signals) in frequency domain through discrete Fourier transform and spectrogram representations, and (iii) investigate and extract the most common time (energy, entropy, zero crossing rate and peaks) and frequency (spectral entropy, rolloff, centroid, spread, peaks and bandpower) domain features, all with one click. Clinical relevance- As the physiological signals originate directly from the underlying physiological events, proper analysis of the signal patterns can provide valuable information in personalized treatment and wearable technology applications.

A Feasibility Study on Tribological Origins of Knee Acoustic Emissions

OBJECTIVE

Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs) while moving. The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that these JAEs are attributed to tribological origins.

METHODS

JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from 10 healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed with standard inverse dynamic analysis through musculoskeletal modeling, and a tribological parameter, lubrication coefficient, was calculated from these signals. Besides, JAEs were divided into short windows, and 64 time-frequency features were extracted. The lubrication coefficients and joint sound features of the two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features. Then, the classifier was used to predict the label of one-leg squat JAE windows. To evaluate these results, the predicted joint sound labels were directly compared against the associated lubrication coefficients.

RESULTS

The trained classifier achieves a high test-accuracy of 84% distinguishing lubrication modes of the one-leg squat JAE windows. The Pearson correlation coefficient between the estimated friction coefficient and the predicted JAE scores was 0.830.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, was calculated from joint sound labels, and it decreased by half from two-leg to one-leg squats. This result was consistent with the tribological changes in the knee load as it was inversely doubled in one-leg squats. These results verify that JAEs contain salient information on knee tribology.

SIGNIFICANCE

This study supports the potential use of JAEs as a quantitative digital biomarker to extract tribological information about joint lubrication modes and loading conditions. Since arthritis and many other conditions impact the roughness of cartilage and other surfaces within the knee, the use of JAEs in clinical applications could thereby have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.

Motion Artifact Resistant Mounting of Acoustic Emission Sensors for Knee Joint Monitoring

Among the many diverse methods of recording biological signals, sound and acoustic emission monitoring are becoming popular for data acquisition; however, these sensors tend to be very susceptible to motion artefacts and noise. In the case of joint monitoring, this issue is even more significant, considering that joint sounds are recorded during limb movements to establish joint health and performance. This paper investigates different sensor attachment methods for acoustic emission monitoring of the knee, which could lead to reduced motion and skin movement artefacts and improve the quality of sensory data sets. As a proof-of-concept study, several methods were tested over a range of exercises to evaluate noise resistance and signal quality. The signals least affected by motion artefacts were recorded when using high-density ethylene-vinyl acetate (EVA) foam holders, attached to the skin with double-sided biocompatible adhesive tape. Securing and isolating the connecting cable with foam is also recommended to avoid noise due to the cable movement.Clinical Relevance- The results of this study will be useful in joint AE monitoring, as well as in other methods of body sound recording that involve the mounting of relatively heavy sensors, such as phonocardiography and respiratory monitoring.

Acoustic emissions and kinematic instability of the osteoarthritic knee joint: comparison with radiographic findings

To evaluate the acoustic emissions (AE) and kinematic instability (KI) of the osteoarthritic (OA) knee joints, and to compare these signals to radiographic findings. Sixty-six female and 43 male participants aged 44–67 were recruited. On radiography, joint-space narrowing, osteophytes and Kellgren–Lawrence (KL) grade were evaluated. Based on radiography, 54 subjects (the study group) were diagnosed with radiographic OA (KL-grade ≥ 2) while the remaining 55 subjects (KL-grade < 2) formed the control group. AE and KI were recorded with a custom-made prototype and compared with radiographic findings using area-under-curve (AUC) and independent T-test. Predictive logistic regression models were constructed using leave-one-out cross validation. In females, the parameters reflecting consistency of the AE patterns during specific tasks, KI, BMI and age had a significant statistical difference between the OA and control groups (p = 0.001–0.036). The selected AE signals, KI, age and BMI were used to construct a predictive model for radiographic OA with AUC of 90.3% (95% CI 83.5–97.2%) which showed a statistical improvement of the reference model based on age and BMI, with AUC of 84.2% (95% CI 74.8–93.6%). In males, the predictive model failed to improve the reference model. AE and KI provide complementary information to detect radiographic knee OA in females.

Acoustic Emissions From Loaded and Unloaded Knees to Assess Joint Health in Patients With Juvenile Idiopathic Arthritis

OBJECTIVE

We studied and compared joint acoustical emissions (JAEs) in loaded and unloaded knees as digital biomarkers for evaluating knee health status during the course of treatment in patients with juvenile idiopathic arthritis (JIA).

METHODS

JAEs were recorded from 38 participants, performing 10 repetitions of unloaded flexion/extension (FE) and loaded squat exercises. A novel algorithm was developed to detect and exclude rubbing noise and loose microphone artifacts from the signals, and then 72 features were extracted. These features were down-selected based on different criteria to train three logistic regression classifiers. The classifiers were trained with healthy and pre-treatment data and were used to predict the knee health scores of post-treatment data for the same patients with JIA who had a follow-up recording. This knee health score represents the probability of having JIA in a subject (0 for healthy and 1 for arthritis).

RESULTS

Post-treatment knee health scores were lower than pre-treatment scores, agreeing with the clinical records of successful treatment. Regarding loaded versus unloaded knee scores, the squats achieved a higher score on average compared to FEs.

CONCLUSION

In healthy subjects with smooth cartilage, the knee scores of squats and FEs were similar indicating that vibrations from the friction of articulating surfaces do not significantly change by the joint load. However, in subjects with JIA, the scores of squats were higher than the scores of FEs, revealing that these two exercises contain different, possibly clinically relevant, information that could be used to further improve this novel assessment modality in JIA.

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.

Time-Frequency Analysis and Parameterisation of Knee Sounds for Non-Invasive Detection of Osteoarthritis

OBJECTIVE

In this work the potential of non-invasive detection of knee osteoarthritis is investigated using the sounds generated by the knee joint during walking.

METHODS

The information contained in the time-frequency domain of these signals and its compressed representations is exploited and their discriminant properties are studied. Their efficacy for the task of normal vs abnormal signal classification is evaluated using a comprehensive experimental framework. Based on this, the impact of the feature extraction parameters on the classification performance is investigated using Classification and Regression Trees, Linear Discriminant Analysis and Support Vector Machine classifiers.

RESULTS

It is shown that classification is successful with an area under the Receiver Operating Characteristic curve of 0.92.

CONCLUSION

The analysis indicates improvements in classification performance when using non-uniform frequency scaling and identifies specific frequency bands that contain discriminative features.

SIGNIFICANCE

Contrary to other studies that focus on sit-to-stand movements and knee flexion/extension, this study used knee sounds obtained during walking. The analysis of such signals leads to non-invasive detection of knee osteoarthritis with high accuracy and could potentially extend the range of available tools for the assessment of the disease as a more practical and cost effective method without requiring clinical setups.

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their use as a novel biomarker of the disease. JIA is the most common rheumatic disease of childhood; it has a highly variable presentation, and few reliable biomarkers which makes diagnosis and personalization of care difficult. The knee is the most commonly affected joint with hallmark synovitis and inflammation that can extend to damage the underlying cartilage and bone. During movement of the knee, internal friction creates JAEs that can be non-invasively measured. We hypothesize that these JAEs contain clinically relevant information that could be used for the diagnosis and personalization of treatment of JIA. In this study, we record and compare the JAEs from 25 patients with JIA-10 of whom were recorded a second time 3-6 months later-and 18 healthy age- and sex-matched controls. We compute signal features from each of those record cycles of flexion/extension and train a logistic regression classification model. The model classified each cycle as having JIA or being healthy with 84.4% accuracy using leave-one-subject-out cross validation (LOSO-CV). When assessing the full JAE recording of a subject (which contained at least 8 cycles of flexion/extension), a majority vote of the cycle labels accurately classified the subjects as having JIA or being healthy 100% of the time. Using the output probabilities of a JIA class as a basis for a joint health score and test it on the follow-up patient recordings. In all 10 of our 6-week follow-up recordings, the score accurately tracked with successful treatment of the condition. Our proposed JAE-based classification model of JIA presents a compelling case for incorporating this novel joint health assessment technique into the clinical work-up and monitoring of JIA.

Estimating Knee Joint Load Using Acoustic Emissions During Ambulation

Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures-electromyography, ground reaction forces, and motion capture trajectories-were collected. The gait measures were synthesized using a neuromuscular model to estimate internal joint contact force which was the target variable for subject-specific machine learning models (XGBoost) trained based on spectral, temporal, cepstral, and amplitude-based features of the joint acoustic emissions. The model using joint acoustic emissions significantly outperformed (p < 0.05) the best estimate without the sounds, the subject-specific average load (MAE = 0.31 ± 0.12 BW), for both seen (MAE = 0.08 ± 0.01 BW) and unseen (MAE = 0.21 ± 0.05 BW) conditions. This demonstrates that joint acoustic emissions contain information that correlates to internal joint contact force and that information is consistent such that unique cases can be estimated.

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist-another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz-20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen-Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems.

Improved Acquisition of Vibroarthrographic Signals of the Knee Joint

This paper presents an improved solution for vibroarthrographic measurements. Four different setups for sensor attachment to the knee were assessed with a focus on the stability and reproducibility of the measured signals. By means of power spectral density estimates, the main signal components were compared and afterwards evaluated by conducting a cross-correlation analysis.

Use of acoustic emission to identify novel candidate biomarkers for knee osteoarthritis (OA)

Our objective was to determine the efficacy and feasibility of a new approach for identifying candidate biomarkers for knee osteoarthritis (OA), based on selecting promising candidates from a range of high-frequency acoustic emission (AE) measurements generated during weight-bearing knee movement. Candidate AE biomarkers identified by this approach could then be validated in larger studies for use in future clinical trials and stratified medicine applications for this common health condition. A population cohort of participants with knee pain and a Kellgren-Lawrence (KL) score between 1-4 were recruited from local NHS primary and secondary care sites. Focusing on participants' self-identified worse knee, and using our established movement protocol, sources of variation in AE measurement and associations of AE markers with other markers were explored. Using this approach we identified 4 initial candidate AE biomarkers, of which "number of hits" showed the best reproducibility, in terms of within-session, day to day, week to week, between-practitioner, and between-machine variation, at 2 different machine upper frequency settings. "Number of hits" was higher in knees with KL scores of 2 than in KL1, and also showed significant associations with pain in the contralateral knee, and with body weight. "Hits" occurred predominantly in 2 of 4 defined movement quadrants. The protocol was feasible and acceptable to all participants and professionals involved. This study demonstrates how AE measurement during simple sit-stand-sit movements can be used to generate novel candidate knee OA biomarkers. AE measurements probably reflect a composite of structural changes and joint loading factors. Refinement of the method and increasing understanding of factors contributing to AE will enable this approach to be used to generate further candidate biomarkers for validation and potential use in clinical trials.

Acoustic Emissions as a Non-invasive Biomarker of the Structural Health of the Knee

The longitudinal assessment of joint health is a long-standing issue in the management of musculoskeletal injuries. The acoustic emissions (AEs) produced by joint articulation could serve as a biomarker for joint health assessment, but their use has been limited by a lack of mechanistic understanding of their creation. In this paper, we investigate that mechanism using an injury model in human lower-limb cadavers, and relate AEs to joint kinematics. Using our custom joint sound recording system, we recorded the AEs from nine cadaver legs in four stages: at baseline, after a sham surgery, after a meniscus tear, and post-meniscectomy. We compare the resulting AEs using their b-values. We then compare joint anatomy/kinematics to the AEs using the X-ray reconstruction of moving morphology (XROMM) technique. After the meniscus tear the number and amplitude of the AE peaks greatly increased from baseline and sham (b-value = 1.33 ± 0.15; p < 0.05). The XROMM analysis showed a close correlation between the minimal inter-joint distances (0.251 ± 0.082 cm during extension, 0.265 ± .003 during flexion, at 145°) and a large increase in the AEs. This work provides key insight into the nature of joint AEs, and details a novel technique and analysis for recording and interpreting these biosignals.

A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation

Sounds produced by the articulation of joints have been shown to contain information characteristic of underlying joint health, morphology, and loading. In this work, we explore the use of a novel form factor for non-invasively acquiring acoustic/vibrational signals from the knee joint: an instrumented glove with a fingertip-mounted accelerometer. We validated the glove-based approach by comparing it to conventional mounting techniques (tape and foam microphone pads) in an experimental framework previously shown to reliably alter healthy knee joint sounds (vertical leg press). Measurements from healthy subjects (N = 11) in this proof-of-concept study demonstrated a highly consistent, monotonic, and significant (p < 0.01) increase in low-frequency signal root-mean-squared (RMS) amplitude-a straightforward metric relating to joint grinding loudness-with increasing vertical load across all three techniques. This finding suggests that a glove-based approach is a suitable alternative for collecting joint sounds that eliminates the need for consumables like tape and the interface noise associated with them.

Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study

Background

In horses, osteoarthritis (OA) mostly affects metacarpophalangeal and metatarsophalangeal (fetlock) joints. The current modalities used for diagnosis of equine limb disorders lack ability to detect early OA. Here, we propose a new alternative approach to assess experimental cartilage damage in fetlock joint using Acoustic Emissions (AE).

Objectives

To evaluate the potential of AE technique in diagnosing OA and see how AE signals changes with increasing severity of OA.

Study design

An in vitro experimental study.

Methods

A total of 16 distal limbs (8 forelimbs and 8 hindlimbs) from six Finn horses were collected from an abattoir and fitted in a custom‐made frame allowing fetlock joint bending. Eight fetlock joints were opened, and cartilage surface was progressively damaged mechanically three times using sandpaper to mimic mild, moderate and severe OA. The remaining eight fetlock joints were opened and closed without any mechanical procedure, serving as controls. Before cartilage alteration, synovial fluid was aspirated, mixed with phosphate‐buffered saline solution, and then reinjected before suturing for constant joint lubrication. For each simulated condition of OA severity, a force was applied to the frame and then released to mimic joint flexion and extension. AE signals were acquired using air microphones.

Results

A strong association was found between the joint condition and the power of AE signals analysed in 1.5–6 kHz range. The signal from both forelimb and hindlimb joints followed a similar pattern for increased cartilage damage. There were statistically significant differences between each joint condition progressively (generalised linear mixed model, P<0.001) in limbs with in vitro cartilage damage of varying severity while the control limbs did not show any changes.

Main limitations

Small sample size using in vitro, mechanically induced cartilage damage.

Conclusion

The AE technique presented here could differentiate the severity of fetlock joint cartilage damage. The consistent results for each simulated condition suggest there is potential for this method in the diagnosis of OA.

b-Value: A Potential Biomarker for Assessing Knee-Joint Health Using Acoustical Emission Sensing

This paper explores the novel application of an automated b-value extraction algorithm for the interpretation of sounds produced by the knee joint during movement. Acoustical emissions were recorded from a total of eight subjects with acute knee injuries a first time, within one week of the injury, then a second time, four to six months following corrective surgery and rehabilitation. The data were collected from each subject using miniature electret microphones placed on the medial and lateral side of the patella during knee flexion and extension exercises. From the acoustical signals measured from each subject, we computed the b-value using the modified Gutenberg-Ritcher equation which is widely used in seismology. The b-value increased for each subject's injured knee from immediately following the injury to several months post recovery. (mean b-value: 1.46 ± 0.35 [injured] and 1.92 ± 0.21 [post-surgery and recovery], p < 0.01). In addition, we compared this analysis technique against an unsupervised machine learning algorithm from our previous work and found that the b-value metric can be as effective to differentiate changes in the joint sounds as our prior approach while requiring less computational time and complexity - both of which are preferable for future integration of this technology into a wearable system.

Using Knee Acoustical Emissions for Sensing Joint Health in Patients with Juvenile Idiopathic Arthritis: A Pilot Study

In this paper, we present a pilot study evaluating novel methods for assessing joint health in patients with Juvenile Idiopathic Arthritis (JIA) using wearable acoustical emission measurements from the knees. Measurements were taken from four control subjects with no known knee injuries, and from four subjects with JIA, before and after treatment. Time and frequency domain features were extracted from the acoustical emission signals and used to compute a knee audio score. The score was used to separate out the two groups of subjects based solely on the sounds their joints produce. It was created using a soft classifier based on gradient boosting trees. The knee audio scores ranged from 0-1 with 0 being a healthy knee and 1 being an involved joint with arthritis. Leave-one-subject-out cross-validation (LOSO-CV) was used to validate the algorithm. The average of the right and left knee audio scores was 0.085±0.099 and 0.89±0.012 for the control group and group with JIA, respectively (p<0.05). The average knee audio score for the subjects with JIA decreased from 0.89±0.012 to 0.25±0.20 following successful treatment (p<0.05). The knee audio score metric successfully distinguished between the control subjects and subjects with JIA. The scores calculated before and after treatment accurately reflected the observed clinical course of the subjects with JIA. After successful treatment, the subjects with JIA were classified as healthy by the algorithm. Knee acoustical emissions provide a novel and cost-effective method for monitoring JIA, and can be used as an objective guide for assessing treatment efficacy.

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.