Clinical Evaluation and Gait Characteristics before and after Total Knee Arthroplasty Based on a Portable Gait Analyzer

Evaluating Knee Recovery Beyond Patient Reports: A Comparative Study of Smart Implantable Device-Derived Gait Metrics Versus Patient-Reported Outcome Measures in Total Knee Arthroplasty

BACKGROUND

Total Knee Arthroplasty (TKA) is frequently performed for advanced osteoarthritis, with patient-reported outcome measures (PROMs) traditionally reporting on efficacy. These subjective evaluations, although useful, may inaccurately reflect post-TKA activity levels. With technological advancements, smart implantable devices (SIDs) offer objective, real-time gait metrics, potentially providing a more accurate postoperative recovery assessment. This study compares these objective metrics with PROMs to evaluate TKA success more effectively.

METHODS

We conducted a retrospective cohort study with 88 participants undergoing TKA using a SID. Eligible patients were aged 18 years or older and had advanced osteoarthritis. We excluded those who had bilateral TKAs, joint infections, or neuromuscular disease. The SID system collected daily gait metrics, including step count, distance traveled, walking speed, stride length, cadence, and functional knee range of motion. The PROMs, including Knee Injury and Osteoarthritis Outcome Score-Joint Replacement, Veterans Rand 12 Physical Component Summary, and Veterans Rand 12 Mental Component Summary, were analyzed against SID gait metrics. Among the 88 patients, 80 provided continuous data over 12 weeks.

RESULTS

All gait metrics, except stride length, significantly increased at the 12-week point (P < .05). The PROMs also significantly improved postoperatively (P < .05). Initial low positive correlations between 12-week PROMs and SID metrics decreased after adjusting for demographic variables, leaving only weak correlations between the Veterans Rand 12 Physical Component Summary and Knee Injury and Osteoarthritis Outcome Score-Joint Replacement with functional knee range of motion (r = 0.389, P = .002; r = 0.311, P = .014, respectively), and Veterans Rand 12 Mental Component Summary with step count (r = 0.406, P = .001) and distance traveled (r = 0.376, P = .003).

CONCLUSIONS

This study indicates that both PROMs and SID gait metrics show significant improvements post-TKA, though they correlate weakly with each other, suggesting a possible discrepancy between perceived recovery and actual functional improvement. The SID gait metrics might provide a valuable addition to traditional PROMs by offering an objective representation of physical capabilities unaffected by patient compliance or subjective perceptions of recovery. Further research is needed to validate these findings in larger populations and to explore whether integrating SID metrics can enhance long-term functional outcomes.

Postoperative Activity and Knee Function of Patients after Total Knee Arthroplasty: A Sensor-Based Monitoring Study

Inertial measurement units (IMUs) are increasingly being used to assess knee function. The aim of the study was to record patients' activity levels and to detect new parameters for knee function in the early postoperative phase after TKA. Twenty patients (n = 20) were prospectively enrolled. Two sensors were attached to the affected leg. The data were recorded from the first day after TKA until discharge. Algorithms were developed for detecting steps, range of motion, horizontal, sitting and standing postures, as well as physical therapy. The mean number of steps increased from day 1 to discharge from 117.4 (SD ± 110.5) to 858.7 (SD ± 320.1), respectively. Patients' percentage of immobilization during daytime (6 a.m. to 8 p.m.) was 91.2% on day one and still 69.9% on the last day. Patients received daily continuous passive motion therapy (CPM) for a mean of 36.4 min (SD ± 8.2). The mean angular velocity at day 1 was 12.2 degrees per second (SD ± 4.4) and increased to 28.7 (SD ± 16.4) at discharge. This study shows that IMUs monitor patients' activity postoperatively well, and a wide range of interindividual motion patterns was observed. These sensors may allow the adjustment of physical exercise programs according to the patient's individual needs.

Advances in the application of wearable sensors for gait analysis after total knee arthroplasty: a systematic review

BACKGROUND

Wearable sensors have become a complementary means for evaluation of body function and gait in lower limb osteoarthritis. This study aimed to review the applications of wearable sensors for gait analysis after total knee arthroplasty (TKA).

METHODS

Five databases, including Web of Science Core Collection, Embase, Cochrane, Medline, and PubMed, were searched for articles published between January 2010 and March 2023, using predetermined search terms that focused on wearable sensors, TKA, and gait analysis as broad areas of interest.

RESULTS

A total of 25 articles were identified, involving 823 TKA patients. Methodologies varied widely across the articles, with inconsistencies found in reported patient characteristics, sensor data and experimental protocols. Patient-reported outcome measures (PROMs) and gait variables showed various recovery times from 1 week postoperatively to 5 years postoperatively. Gait analysis using wearable sensors and PROMs showed differences in controlled environments, daily life, and when comparing different surgeries.

CONCLUSION

Wearable sensors offered the potential to remotely monitor the gait function post-TKA in both controlled environments and patients' daily life, and covered more aspects than PROMs. More cohort longitudinal studies are warranted to further confirm the benefits of this remote technology in clinical practice.

Effect of pregnancy on female gait characteristics: a pilot study based on portable gait analyzer and induced acceleration analysis

Introduction: The changes in physical shape and center of mass during pregnancy may increase the risk of falls. However, there were few studies on the effects of maternal muscles on gait characteristics and no studies have attempted to investigate changes in induced acceleration during pregnancy. Further research in this area may help to reveal the causes of gait changes in women during pregnancy and provide ideas for the design of footwear and clothing for pregnant women. The purpose of this study is to compare gait characteristics and induced accelerations between non-pregnant and pregnant women using OpenSim musculoskeletal modeling techniques, and to analyze their impact on pregnancy gait. Methods: Forty healthy participants participated in this study, including 20 healthy non-pregnant and 20 pregnant women (32.25 ± 5.36 weeks). The portable gait analyzer was used to collect participants' conventional gait parameters. The adjusted OpenSim personalized musculoskeletal model analyzed the participants' kinematics, kinetics, and induced acceleration. Independent sample T-test and one-dimensional parameter statistical mapping analysis were used to compare the differences in gait characteristics between pregnant and non-pregnant women. Results: Compared to the control group, pregnancy had a 0.34 m reduction in mean walking speed (p < 0.01), a decrease in mean stride length of 0.19 m (p < 0.01), a decrease in mean stride frequency of 19.06 step/min (p < 0.01), a decrease in mean thigh acceleration of 0.14 m/s² (p < 0.01), a decrease in mean swing work of 0.23 g (p < 0.01), and a decrease in mean leg falling strength of 0.84 g (p < 0.01). Induced acceleration analysis showed that pregnancy muscle-induced acceleration decreased in late pregnancy (p < 0.01), and the contribution of the gastrocnemius muscle to the hip and joint increased (p < 0.01). Discussion: Compared with non-pregnant women, the gait characteristics, movement amplitude, and joint moment of pregnant women changed significantly. This study observed for the first time that the pregnant women relied more on gluteus than quadriceps to extend their knee joints during walking compared with the control group. This change may be due to an adaptive change in body shape and mass during pregnancy.

Evaluation of gait recovery after total knee arthroplasty using wearable inertial sensors: A systematic review

PURPOSE

This study aimed to conduct a systematic review of the recent research output to present more evidence of the current clinical applications of wearable sensors to determine the change and the recovery in gait function pre- and post-total knee arthroplasty (TKA).

METHODS

A systematic search of the PubMed, ScienceDirect, and Scopus databases was conducted in October 2022. Inclusion criteria consisted of applying acceleration wearable sensors for pre- and post-arthroplasty assessment of the gait cycle. Studies reporting gait analysis using wearable sensors in patients with knee osteoarthritis at any time after total or partial knee arthroplasty (KA) were also included. Each included study was assessed using the Joanna Briggs Institute Critical Appraisal Tool for Quasi-Experimental studies.

RESULTS

Twelve articles were finally considered. The extracted data included essential characteristics of participants, KA studies and their characteristics, sensor technology characteristics and the clinical protocols, gait parameter changes, and various clinical outcome scores at different follow up times after KA. Postoperative examinations were performed from 5 days to 1 year after KA. Clinical outcome scores and gait variables for all patient groups, with or without postoperative rehabilitation, showed various recovery profiles. A variety of wireless sensor devices for gait analysis were recorded. Also, different types of KA were found in the studies.

CONCLUSIONS

The study's findings showed that acceleration-based gait analysis has notable clinical use in monitoring patients after KA. This application provides objective information on the functional outcome beyond the use of clinical outcome scores. More extensive prospective studies are required to investigate gait function further with the help of wearable sensors in patients with knee osteoarthritis.

Objective gait assessment in individuals with knee osteoarthritis using inertial sensors: A systematic review and meta-analysis

BACKGROUND

Objective assessment of gait using inertial sensors has shown promising results for functional evaluations in individuals with knee osteoarthritis (OA). However, the large number of possible outcome measures calls for a systematic evaluation of most relevant parameters to be used for scientific and clinical purposes.

AIM

This systematic review and meta-analysis aimed to identify gait parameters derived from inertial sensors that reflect gait deviations in individuals with knee OA compared to healthy control subjects (HC).

METHODS

A systematic search was conducted in five electronic databases (Medline, Embase, Web of Science, CINAHL, IEEE) to identify eligible articles. Risk of bias was assessed using a modified version of the Downs and Black scale. Data regarding study population, experimental procedures, and biomechanical outcomes were extracted. When a gait parameter was reported by a sufficient number of studies, a random-effects meta-analysis was conducted using the inverse variance method.

RESULTS

Twenty-three articles comparing gait between 411 individuals with knee OA and 507 HC were included. Individuals with knee OA had a lower gait speed than HC (standardized mean difference = -1.65), driven by smaller strides with a longer duration. Stride time variability was slightly higher in individuals with knee OA than in HC. Individuals with knee OA walked with a lower range of motion of the knee during the swing phase, less lumbar motion in the coronal plane, and a lower foot strike and toe-off angle compared to HC.

SIGNIFICANCE

This review shows that inertial sensors can detect gait impairments in individuals with knee OA. Large standardized mean differences found on spatiotemporal parameters support their applicability as sensitive endpoints for mobility in individuals with knee OA. More advanced measures, including kinematics of knee and trunk, may reveal gait adaptations that are more specific to knee OA, but compelling evidence was lacking.

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review

BACKGROUND

Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors.

RESULTS

A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls.

DISCUSSION

This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.

Gait Characteristics and Fatigue Profiles When Standing on Surfaces with Different Hardness: Gait Analysis and Machine Learning Algorithms

Simple Summary

The purpose of this study was to explore if an anti-fatigue soft mat could improve the gait performance after standing for long periods and to examine if a machine-learning algorithm could evaluate fatigue state objectively. Compared with standing directly on the hard ground, using an anti-fatigue mat could reduce the negative effect of standing for a long time (4 h). The machine-learning algorithm demonstrated moderate accuracy in measuring fatigue. The accuracy of gait parameters used to consider a non-fatigued state following the use of an anti-fatigue mat was higher than that of the fatigue state. The results may indicate that it is beneficial to use anti-fatigue mats when standing for long periods, and it is feasible to use gait parameters and machine-learning algorithms to detect fatigue.

Abstract

Background: Longtime standing may cause fatigue and discomfort in the lower extremities, leading to an increased risk of falls and related musculoskeletal diseases. Therefore, preventive interventions and fatigue detection are crucial. This study aims to explore whether anti-fatigue mats can improve gait parameters following long periods of standing and try to use machine learning algorithms to identify the fatigue states of standing workers objectively. Methods: Eighteen healthy young subjects were recruited to stand on anti-fatigue mats and hard ground to work 4 h, including 10 min rest. The portable gait analyzer collected walking speed, stride length, gait frequency, single support time/double support time, swing work, and leg fall intensity. A Paired sample t-test was used to compare the difference of gait parameters without standing intervention and standing on two different hardness planes for 4 h. An independent sample t-test was used to analyze the difference between males and females. The K-nearest neighbor (KNN) classification algorithm was performed, the subject’s gait characteristics were divided into non-fatigued and fatigue groups. The gait parameters selection and the error rate of fatigue detection were analyzed. Results: When gender differences were not considered, the intensity of leg falling after standing on the hard ground for 4 h was significantly lower than prior to the intervention (p < 0.05). When considering the gender, the stride length and leg falling strength of female subjects standing on the ground for 4 h were significantly lower than those before the intervention (p < 0.05), and the leg falling strength after standing on the mat for 4 h was significantly lower than that recorded before the standing intervention (p < 0.05). The leg falling strength of male subjects standing on the ground for 4 h was significantly lower than before the intervention (p < 0.05). After standing on the ground for 4 h, female subjects’ walking speed and stride length were significantly lower than those of male subjects (p < 0.05). In addition, the accuracy of testing gait parameters to predict fatigue was medium (75%). After standing on the mat was divided into fatigue, the correct rate was 38.9%, and when it was divided into the non-intervention state, the correct rate was 44.4%. Conclusion: The results show that the discomfort and fatigue caused by standing for 4 h could lead to the gait parameters variation, especially in females. The use of anti-fatigue mats may improve the negative influence caused by standing for a long period. The results of the KNN classification algorithm showed that gait parameters could be identified after fatigue, and the use of an anti-fatigue mat could improve the negative effect of standing for a long time. The accuracy of the prediction results in this study was moderate. For future studies, researchers need to optimize the algorithm and include more factors to improve the prediction accuracy.

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for "Code Reuse" to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

SARS-CoV-2-Pandemie und ihre Auswirkungen auf Orthopädie und Unfallchirurgie: „Booster“ für die Telemedizin

Mit dem Ausbruch der COVID-19-Pandemie ist das Interesse an telemedizinischen Versorgungsmöglichkeiten gewachsen. Diese betreffen einerseits den Bereich der Diagnostik, aber auch die Überwachung von Therapieverläufen und Rehabilitationsmaßnahmen.

Aufgrund der derzeitigen Ressourcenbeschränkungen sahen sich viele Orthopäden und Unfallchirurgen gezwungen, Videosprechstunden einzurichten, obwohl Standards für orthopädische Konsultationen bisher nur unzureichend entwickelt sind. Um die Effizienz der virtuellen Orthopädie zu maximieren, sollten die Patienten auf den virtuellen Besuch vorbereitet werden (Checkliste mit spezifischen Anweisungen zur Kamerapositionierung, Körperpositionierung, Einstellung und Kleidung, Prüfung der audiovisuellen Fähigkeiten). Klassische diagnostische Maßnahmen wie Anamnese, Inspektion und Beurteilung radiologischer Befunde sind in der Videosprechstunde möglich. Es entfällt jedoch die Möglichkeit der funktionellen Untersuchung (Stabilität des Kniegelenkes, Schultertests). Auch wenn erste wissenschaftliche Studien gezeigt haben, dass die telemedizinische Diagnostik der konventionellen Diagnostik nicht unterlegen ist, so fehlen doch validierte Untersuchungsprotokolle und Methoden. Die postoperative Überwachung von Rehabilitationsmaßnahmen kann z. B. durch den Einsatz von Sensoren erleichtert werden. Mit moderner Sensorik ist mittlerweile eine kostengünstige Erfassung der Gelenkbeweglichkeit und Gelenkstellung möglich und wird bereits im Bereich der Rehabilitation nach Rekonstruktion des vorderen Kreuzbandes eingesetzt. Auch hier ist sicher weitere Forschung notwendig, um diese Methoden zu validieren.

Wir glauben, dass die derzeitige Pandemie Chancen bietet, die Möglichkeiten der Telemedizin für die Orthopädie und Unfallchirurgie auszubauen, um sie auch in der Zukunft weiter zu nutzen (z. B. bei der Versorgung von Patienten aus dem Ausland oder in dünn besiedelten Gebieten sowie der Betreuung von Hochleistungs- und Profisportlern).

Use of the pedometer in the evaluation of the effects of rehabilitation treatment on deambulatory autonomy in patients with lower limb arthroplasty during hospital rehabilitation: long-term postoperative outcomes

[Purpose] To provide data on the applicability of pedometers in the evaluation of the results of rehabilitative treatment on total daily walking activity after total knee arthroplasty (TKA) and total hip arthroplasty (THA). [Participants and Methods] One hundred fifty six hospitalized patients (age 63.9 ± 12.2 years) involved. On the day of hospitalization and at the end of the rehabilitation treatment the following were performed: clinical examination, X-ray examination and weight. On the same day the pedometer was applied and removed after 48 hours. Only on 30 participants, the same evaluation was carried out 5 days before the hospitalization to measure reliability and responsiveness. Compliance was measured by a face-to-face interview. Visual analogic scale (VAS), Barthel Index (BI) and Ambulation Index (AI) were used to better describe the analyzed sample. [Results] VAS, BI and AI improved by 29.8%, 19.4 and 60.6% respectively. The data obtained on testing-retesting showed a good reliability and a mean Total Error of 7.3% for steps and 15.8% for distance. A good response in the test-retest was detected. The deambulatory autonomy of patients passed from 2,070 ± 740 m to 3,100 ± 810 m. Average improvement in the number of daily steps is 25%. [Conclusion] The data showed a good applicability of pedometer. The results on responsiveness can be used to better interpret the results of rehabilitative treatment on total daily walking activity after THA and TKA.

Current clinical utilisation of wearable motion sensors for the assessment of outcome following knee arthroplasty: a scoping review

OBJECTIVES

Wearable motion sensors are used with increasing frequency in the evaluation of gait, function and physical activity within orthopaedics and sports medicine. The integration of wearable technology into the clinical pathway offers the ability to improve post-operative patient assessment beyond the scope of current, questionnaire-based patient-reported outcome measures. This scoping review assesses the current methodology and clinical application of accelerometers and inertial measurement units for the evaluation of patient activity and functional recovery following knee arthroplasty.

DESIGN

This is a systematically conducted scoping review following Joanna Briggs Institute methodology for scoping reviews and reported consulting the Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews. A protocol for this review is registered with the Open Science Framework (https://osf.io/rzg9q).

DATA SOURCES

CINAHL, EMBASE, MEDLINE and Web of Science databases were searched for manuscripts published between 2008 and 2019.

ELIGIBILITY CRITERIA

We included clinical studies reporting the use of any combination of accelerometers, pedometers or inertial measurement units for patient assessment at any time point following knee arthroplasty.

DATA EXTRACTION AND SYNTHESIS

Data extracted from manuscripts included patient demographics, sensor technology, testing protocol and sensor-based outcome variables.

RESULTS

45 studies were identified, including 2076 knee arthroplasty patients, 620 patients with end-stage osteoarthritis and 449 healthy controls. Primary aims of the identified studies included functional assessment, physical activity monitoring and evaluation of knee instability. Methodology varied widely between studies, with inconsistency in reported sensor configuration, testing protocol and output variables.

CONCLUSIONS

The use of wearable sensors in evaluation of knee arthroplasty procedures is becoming increasingly common and offers the potential to improve clinical understanding of recovery and rehabilitation. While current studies lack consistency, significant opportunity exists for the development of standardised measures and protocols for function and physical activity evaluation.

Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty

BACKGROUND

Despite the general success of total knee arthroplasty (TKA) regarding patient-reported outcome measures, studies investigating gait function have shown diverse functional outcomes. Mobile sensor-based systems have recently been employed for accurate clinical gait assessments, as they allow a better integration of gait analysis into clinical routines as compared to laboratory based systems.

RESEARCH QUESTION

In this study, we sought to examine whether an accurate assessment of gait function of knee osteoarthritis patients with respect to surgery outcome evaluation after TKA using a mobile sensor-based gait analysis system is possible.

METHODS

A foot-worn sensor-based system was used to assess spatio-temporal gait parameters of 24 knee osteoarthritis patients one day before and one year after TKA, and in comparison to matched control participants. Patients were clustered into positive and negative responder groups using a heuristic approach regarding improvements in gait function. Machine learning was used to predict surgery outcome based on pre-operative gait parameters.

RESULTS

Gait function differed significantly between controls and patients. Patient-reported outcome measures improved significantly after surgery, but no significant global gait parameter difference was observed between pre- and post-operative status. However, the responder groups could be correctly predicted with an accuracy of up to 89% using pre-operative gait parameters. Patients exhibiting high pre-operative gait function were more likely to experience a functional decrease after surgery. Important gait parameters for the discrimination were stride time and stride length.

SIGNIFICANCE

The early identification of post-surgical functional outcomes of patients is of great importance to better inform patients pre-operatively regarding surgery success and to improve post-surgical management.

Gait analysis of patients with continuous proximal sciatic nerve blockade in flexion contractures after primary total knee arthroplasty

BACKGROUND

The main objective of total knee arthroplasty is to relieve pain, restore normal knee function, and improve gait stability. Significant flexion contractures can severely impair function after surgery. The purpose of this study is to evaluate the efficacy of implementing a continuous proximal sciatic nerve block in conjunction with aggressive physical therapy to treat patients with persistent flexion contractures that were recalcitrant to rehabilitation efforts following primary total knee arthroplasty (TKA).

METHODS

From December 2012 to January 2016, the following subjects were enrolled in this study: 20 patients (15 females and 5 males aged between 62 and 78 years old; median age = 65.7 y) with flexion contractures ranging from 15° to 25° (19.2°±5.6°) that persisted for at least 1.5 months following total knee arthroplasty and showed no significant improvement in response to conventional therapeutic methods. Demographic data, the passive range of motion, flexion contracture, pain score during stretching, and the Hospital for Special Surgery knee score were recorded. A portable motion analyzer was used to obtain the corresponding gait parameters from the flexion contractures group and control group. Repeated measurement ANOVA was used to compare the clinical results.

RESULTS

In combination with 2 to 4 (2.5 ± 1.3) months of aggressive knee stretching exercises, 16 out of 18 knees achieved full extension, and 2 out of 18 improved to within 5° of the full extension. An average of the 12 to 48 (26.6 ± 10.7) month follow-up showed that this improved range of motion was maintained for all the corresponding patients, and that there were no reoccurrences of deformity. The mean Hospital for Special Surgery knee scores improved from 61.2 to 93.2 points (p < 0.001). After six months of continuous proximal sciatic nerve blockage, all gait parameters for the flexion contractures group exhibited significant improvement.

CONCLUSION

A continuous proximal sciatic nerve block could be a useful adjunct to a physical therapy regimen for patients with knee flexion contractures, especially for patients with difficult-to-treat cases of knee flexion contracture that are recalcitrant to conservative therapy.

Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.