Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis

Gait compensations, pain, and functional performance during the six minute walk test in individuals with unilateral hip osteoarthritis

BACKGROUND

Individuals with unilateral hip osteoarthritis walk with kinematic and spatiotemporal compensations compared to healthy individuals. Our purpose was to determine associations between gait, pain, and functional performance during the six-minute walk test.

METHODS

Trunk and hip kinematics and spatiotemporal gait outcomes were recorded from individuals with unilateral hip osteoarthritis using inertial sensors (Xsens Technologies). Pain was collected prior to and at the end of the six-minute walk test. Paired t-tests were conducted to evaluate gait between limbs and between the first and final minutes of walking. Correlations were conducted between gait, pain, and six-minute walk test performance.

FINDINGS

Nineteen participants (8 females, age: 63 ± 5 yrs.

, BMI

29.0 ± 4.5 kg/m2) completed the study. Between-limb differences in hip flexion, hip extension, and trunk forward flexion peak angles were observed during the six-minute walk test (P < .05). Participants demonstrated an increase in trunk forward flexion of the osteoarthritis side (t = -2.34, P = .031) and a bilateral decrease in stride length (osteoarthritis limb: t = 2.98, P = .008, non- osteoarthritis limb: t = 3.17, P = .006) from the first to the final minute of walking. Greater pain was associated with greater osteoarthritis limb hip extension (first minute: r = -0.506, P = .027, final minute: r = -0.53, P = .020) and greater hip abduction (r = 0.46, P = .046) during the final minute of walking.

INTERPRETATIONS

Gait compensations increase throughout the six-minute walk test, and pain associates with hip kinematics during the six-minute walk test. Wearable technology may allow for more accurate clinical movement assessments.

Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study

BACKGROUND

Patients with knee osteoarthritis (KOA) often present lower extremity motor dysfunction. However, traditional radiography is a static assessment and cannot achieve long-term dynamic functional monitoring. Plantar pressure signals have demonstrated potential applications in the diagnosis and rehabilitation monitoring of KOA.

OBJECTIVE

Through wearable gait analysis technology, we aim to obtain abundant gait information based on machine learning techniques to develop a simple, rapid, effective, and patient-friendly functional assessment model for the KOA rehabilitation process to provide long-term remote monitoring, which is conducive to reducing the burden of social health care system.

METHODS

This cross-sectional study enrolled patients diagnosed with KOA who were able to walk independently for 2 minutes. Participants were given clinically recommended functional tests, including the 40-m fast-paced walk test (40mFPWT) and timed up-and-go test (TUGT). We used a smart shoe system to gather gait pressure data from patients with KOA. The multidimensional gait features extracted from the data and physical characteristics were used to establish the KOA functional feature database for the plantar pressure measurement system. 40mFPWT and TUGT regression prediction models were trained using a series of mature machine learning algorithms. Furthermore, model stacking and average ensemble learning methods were adopted to further improve the generalization performance of the model. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were used as regression performance metrics to evaluate the results of different models.

RESULTS

A total of 92 patients with KOA were included, exhibiting varying degrees of severity as evaluated by the Kellgren and Lawrence classification. A total of 380 gait features and 4 physical characteristics were extracted to form the feature database. Effective stepwise feature selection determined optimal feature subsets of 11 variables for the 40mFPWT and 10 variables for the TUGT. Among all models, the weighted average ensemble model using 4 tree-based models had the best generalization performance in the test set, with an MAE of 2.686 seconds, MAPE of 9.602%, and RMSE of 3.316 seconds for the prediction of the 40mFPWT and an MAE of 1.280 seconds, MAPE of 12.389%, and RMSE of 1.905 seconds for the prediction of the TUGT.

CONCLUSIONS

This wearable plantar pressure feature technique can objectively quantify indicators that reflect functional status and is promising as a new tool for long-term remote functional monitoring of patients with KOA. Future work is needed to further explore and investigate the relationship between gait characteristics and functional status with more functional tests and in larger sample cohorts.

Multivariable model for gait pattern differentiation in elderly patients with hip and knee osteoarthritis: A wearable sensor approach

Background

Hip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data.

Method

Participants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features.

Findings

We included 57 knee OA patients (30 females, median age 68 [range 49-89], median BMI 29.7 [range 21.0-45.9]) and 42 hip OA patients (19 females, median age 70 [range 47-89], median BMI 28.3 [range 20.4-37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively.

Interpretation

The study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.

Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study

Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases.

Detection of kinematic abnormalities in persons with knee osteoarthritis using markerless motion capture during functional movement screen and daily activities

Background: The functional movement screen (FMS) has been used to identify deficiencies in neuromuscular capabilities and balance among athletes. However, its effectiveness in detecting movement anomalies within the population afflicted by knee osteoarthritis (KOA), particularly through the application of a family-oriented objective assessment technique, remains unexplored. The objective of this study is to investigate the sensitivity of the FMS and daily activities in identifying kinematic abnormalities in KOA people employing a markerless motion capture system. Methods: A total of 45 persons, presenting various Kellgren-Lawrence grades of KOA, along with 15 healthy controls, completed five tasks of the FMS (deep squat, hurdle step, and in-line lunge) and daily activities (walking and sit-to-stand), which were recorded using the markerless motion capture system. The kinematic waveforms and discrete parameters were subjected to comparative analysis. Results: Notably, the FMS exhibited greater sensitivity compared to daily activities, with knee flexion, trunk sagittal, and trunk frontal angles during in-line lunge emerging as the most responsive indicators. Conclusion: The knee flexion, trunk sagittal, and trunk frontal angles during in-line lunge assessed via the markerless motion capture technique hold promise as potential indicators for the objective assessment of KOA.

Effects of hip osteoarthritis on lower body joint kinematics during locomotion tasks: a systematic review and meta-analysis

Introduction

Motion analysis can be used to gain information needed for disease diagnosis as well as for the design and evaluation of intervention strategies in patients with hip osteoarthritis (HOA). Thereby, joint kinematics might be of great interest due to their discriminative capacity and accessibility, especially with regard to the growing usage of wearable sensors for motion analysis. So far, no comprehensive literature review on lower limb joint kinematics of patients with HOA exists. Thus, the aim of this systematic review and meta-analysis was to synthesise existing literature on lower body joint kinematics of persons with HOA compared to those of healthy controls during locomotion tasks.

Methods

Three databases were searched for studies on pelvis, hip, knee and ankle kinematics in subjects with HOA compared to healthy controls during locomotion tasks. Standardised mean differences were calculated and pooled using a random-effects model. Where possible, subgroup analyses were conducted. Risk of bias was assessed with the Downs and Black checklist.

Results and Discussion

A total of 47 reports from 35 individual studies were included in this review. Most studies analysed walking and only a few studies analysed stair walking or turning while walking. Most group differences were found in ipsi- and contralateral three-dimensional hip and sagittal knee angles with reduced ranges of motion in HOA subjects. Differences between subjects with mild to moderate and severe HOA were found, with larger effects in severe HOA subjects. Additionally, stair walking and turning while walking might be promising extensions in clinical gait analysis due to their elevated requirements for joint mobility. Large between-study heterogeneity was observed, and future studies have to clarify the effects of OA severity, laterality, age, gender, study design and movement execution on lower limb joint kinematics.

Systematic Review Registration

PROSPERO (CRD42021238237).

Using a Markerless Motion Capture System to Identify Preinjury Differences in Functional Assessments

Functional assessments identify biomechanical issues which may indicate risk for injury and can be used to monitor functional recovery after an injury or surgery. Although the gold standard to assess functional movements is marker-based motion capture systems, these are cost prohibitive and have high participant burden. As such, this study was conducted to determine if a markerless motion capture system could detect preinjury differences in functional movements between those who did and did not experience a noncontact lower extremity injury (NCLEI). A three-dimensional markerless motion capture system comprised an area of 3 m × 5 m × 2.75 m was used. Participants were Division I collegiate athletes wearing plain black long-sleeve shirts, pants, and running shoes of their choice. Functional assessments were the bilateral squat, right and left squat, double leg drop vertical jump, static vertical jump, right and left vertical jump, and right and left 5 hop. Measures were recorded once and the first NCLEI was recorded during the first year after measurement. Two-factor analysis of variance models were used for each measure with factors sex and injury status. Preinjury functional measures averaged 8.4 ± 3.4 minutes capture time. Out of the 333 participants recruited, 209 were male and 124 were female. Of those, 127 males (61%) and 92 females (74%) experienced later NCLEI. The most common initial NCLEI was nonanterior cruciate ligament knee injury in 38 females (41.3%) and 80 males (62.0%). Females had decreased flexion and lower valgus/varus displacement during the bilateral squat (p < 0.006). In addition, knee loading flexion for those who were not injured were more than that seen in the injured group, and was more pronounced for injured females (p < 0.03). The markerless motion capture system can efficiently provide data that can identify preinjury functional differences for lower extremity noncontact injuries. This method holds promise for effectively screening patients or other populations at risk of injury, as well as for monitoring pre-/postsurgery function, without the large costs or participant burden.

Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis

Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context.

Do Patient Reported Outcomes Reflect Objective Measures of Function? Implications for Total Knee Arthroplasty

BACKGROUND

Patient Reported Outcomes (PROs) are used in research, clinical practice, and by federal reimbursement models to assess outcomes for patients who have knee osteoarthritis (OA) and total knee arthroplasty (TKA). We examined a large cohort of patients to determine if commonly used PROs reflect observed evaluation as measured by standardized functional tests (SFTs).

METHODS

We used data from the Osteoarthritis Initiative, a ten-year observational study of knee osteoarthritis patients. Two cohorts were examined: 1) participants who received TKA (n=281) and 2) participants who have native OA (n=4,687). The PROs included Western Ontario and McMaster Osteoarthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS), 12-Item Short Form Health Survey (SF-12), and Intermittent and Constant Pain Score (ICOAP). The SFTs included 20 and 400 meter (M) walks and chair stand pace. Repeated measures correlation coefficients were used to determine the relationship between PROs and SFTs.

RESULTS

The PROs and SFTs were not strongly correlated in either cohort. The magnitude of the repeated measures correlation (r_rm) between KOOS, WOMAC, SF-12, and ICOAP scores and SFT measurements in native knee OA patients ranged as follows: 400 M walk pace (0.08 to 0.20), chair stand pace (0.05 to 0.12), and 20 M pace (0.02 to 0.21), all with P<0.05. In the TKA cohort, values ranged as follows: 400 M walk pace (0.00 to 0.29), chair stand time (0.02 to 0.23), and 20 M pace (0.03 to 0.30). Due to the smaller cohort size, the majority, but not all had P values <0.05.

CONCLUSION

There is not a strong association between PROs and SFTs among patients who have knee OA or among patients who received a TKA. Therefore, PROs should not be used as a simple proxy for observed evaluation of physical function. Rather, PROs and SFTs are complementary and should be used in combination for a more nuanced and complete characterization of outcome.

Functional assessment of total hip arthroplasty using inertial measurement units: Improvement in gait kinematics and association with patient-reported outcome measures

Inertial measurement units (IMUs) are commonly used for gait assessment, yet their potential for quantifying improvements in gait function and patterns after total hip arthroplasty (THA) has not been fully explored. The primary aim of this study was to compare spatiotemporal parameters and sagittal plane kinematic patterns of patients with hip osteoarthritis (OA) before and after THA, and to asymptomatic controls. The secondary aim was to assess the association between dynamic hip range of motion (ROM) during walking and the Hip Osteoarthritis Outcome Scores (HOOS). Twenty-four patients with hip OA and 24 matched asymptomatic controls completed gait analyses using the RehaGait® sensor system. Patients were evaluated pre- and 1 year postoperatively, controls in a single visit. Differences in kinematic data were analyzed using statistical parametric mapping, and correlations between dynamic hip ROM and HOOS were calculated. Walking speed and stride length significantly increased (+0.08 m/s, p = 0.019; +0.06 m, p = 0.048) after THA but did not reach the level of asymptomatic controls (-0.11 m/s, p = 0.028; -0.14 m, p = 0.001). Preoperative hip and knee kinematics differed significantly from controls. After THA, they improved significantly and did not differ from controls. Dynamic hip flexion-extension ROM correlated positively with all HOOS subscores (r > 0.417; p ≤ 0.001). The change in HOOS symptoms in patients was explained by the combination of baseline HOOS symptoms and change in dynamic hip ROM (r²  = 0.748) suggesting that the additional information gained with IMU gait analysis helps to complement and objectify patient-reported outcome measures pre- and postoperatively and monitor treatment-related improvements.

Comparison of Patient-Reported Outcomes and Functional Assessment Using a Marker-Less Image Capture System in End-Stage Knee Arthritis

BACKGROUND

Patient self-assessment of knee function in end-stage osteoarthritis (OA) and following total knee arthroplasty (TKA) using patient-reported outcome measures (PROMs) has become standard for defining disability. The relationship of PROMs to functional performance requires a continued investigation. The purpose of this study was to determine correlations between patient demographics, PROMs, and functional performances using a marker-less image capture system (MICS).

METHODS

Patients indicated for elective TKA completed the Knee Injury and Osteoarthritis Score for Joint Replacement (KOOS-JR) and an office-based functional assessment using a MICS. Patient age, body mass index (BMI), and gender were collected. A total of 112 patients were enrolled. Their mean age was 65.0 (±9.7) years, mean BMI was 32.5 (±6.6) kg/m², and mean KOOS-JR was 14.5 (±5.7). The relationships between patient characteristics, KOOS-JR, MICS Alignment (coronal), MICS Mobility (flexion), and composite Total Joint scores were described using Spearman's correlation coefficients.

RESULTS

BMI was weakly correlated with KOOS-JR (ρ = -0.22, P = .024), whereas age was not. Age and BMI were not correlated with performance scores. There were weak to no correlations between KOOS-JR and MICS Alignment (ρ = -0.01, P = .951), Mobility (ρ = 0.33, P < .001), and Total Joint scores (ρ = 0.06, P = .504).

CONCLUSION

This study found no strong correlation between KOOS-JR and functional performance using a validated MICS for patients with end-stage knee OA. Further study is warranted in determining the relationship between PROMs and performance to optimize outcomes of patients undergoing nonoperative or surgical interventions for knee OA. The use of high-fidelity functional assessment tools that can be integrated into clinical workflow, such as the MICS used in this study, should permit PROM/functional performance comparisons in large populations.

How does hip osteoarthritis differ from knee osteoarthritis?

Hip and knee osteoarthritis (OA) are leading causes of global disability. Most research to date has focused on the knee, with results often extrapolated to the hip, and this extends to treatment recommendations in clinical guidelines. Extrapolating results from research on knee OA may limit our understanding of disease characteristics specific to hip OA, thereby constraining development and implementation of effective treatments. This review highlights differences between hip and knee OA with respect to prevalence, prognosis, epigenetics, pathophysiology, anatomical and biomechanical factors, clinical presentation, pain and non-surgical treatment recommendations and management.