Clinical evaluation of a mobile sensor-based gait analysis method for outcome measurement after knee arthroplasty

Mobile and Computer-Based Applications for Rehabilitation Monitoring and Self-Management After Knee Arthroplasty: Scoping Review

BACKGROUND

Successful post-knee replacement rehabilitation requires adequate access to health information, social support, and periodic monitoring by a health professional. Mobile health (mHealth) and computer-based technologies are used for rehabilitation and remote monitoring. The extent of technology use and its function in post-knee replacement rehabilitation care in low and middle-income settings are unknown.

OBJECTIVE

To inform future mHealth intervention development, we conducted a scoping review to map the features and functionality of existing technologies and determine users' perspectives on telerehabilitation and technology for self-management.

METHODS

We followed the Joanna Briggs Institute methodology for scoping reviews. We searched the Embase, Medline, PsycINFO via OVID, and Cochrane Central Register of Controlled Trials databases for manuscripts published from 2001 onward. We included original research articles reporting the use of mobile or computer-based technologies by patients, health care providers, researchers, or family members. Studies were divided into the following 3 categories based on the purpose: validation studies, clinical evaluation, and end user feedback. We extracted general information on study design, technology features, proposed function, and perspectives of health care providers and patients. The protocol for this review is accessible in the Open Science Framework.

RESULTS

Of the 5960 articles, 158 that reported from high-income settings contributed to the qualitative summary (64 studies on mHealth or telerehabilitation programs, 28 validation studies, 38 studies describing users' perceptions). The highest numbers of studies were from Europe or the United Kingdom and North America regarding the use of a mobile app with or without wearables and reported mainly in the last decade. No studies were from low and middle-income settings. The primary functions of technology for remote rehabilitation were education to aid recovery and enable regular, appropriate exercises; monitoring progress of pain (n=19), activity (n=20), and exercise adherence (n=30); 1 or 2-way communication with health care professionals to facilitate the continuum of care (n=51); and goal setting (n=23). Assessment of range of motion (n=16) and gait analysis (n=10) were the commonly validated technologies developed to incorporate into a future rehabilitation program. Few studies (n=14) reported end user involvement during the development stage. We summarized the reasons for satisfaction and dissatisfaction among users across various technologies.

CONCLUSIONS

Several existing mobile and computer-based technologies facilitate post-knee replacement rehabilitation care for patients and health care providers. However, they are limited to high-income settings and may not be extrapolated to low-income settings. A systematic needs assessment of patients undergoing knee replacement and health care providers involved in rehabilitation, involving end users at all stages of development and evaluation, with clear reporting of the development and clinical evaluation can make post-knee replacement rehabilitation care in resource-poor settings accessible and cost-effective.

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.

Comparison of spatiotemporal, kinematic, and kinetic gait characteristics in total and unicompartmental knee arthroplasty during level walking: A systematic review and meta-analysis

PURPOSE

This meta-analysis was performed to compare the spatiotemporal, kinematic, and kinetic gait characteristics during level walking between total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA).

METHODS

An electronic database literature search was performed to screen clinical trials which were included the studies evaluating not only spatiotemporal, kinematic, and kinetic gait parameters, but also knee range of motion and knee score (Knee Society Score and Oxford Knee Score, i.e., KSS and OKS). The data analysis was performed using statistical software Stata 14.0 and Review Manager 5.4.

RESULTS

Thirteen studies (369 knees) that met the criteria were eventually included in this meta-analysis. The results revealed significant differences between UKA and TKA with regard to walking speed (P = 0.04), stride length (P = 0.02), maximum knee flexion at loading (P = 0.001), the 1st peak of vert-GRF (P = 0.006), the 1st valley of vert-GRF (P = 0.007), knee internal rotational moment (P = 0.04), knee extension (P < 0.00001), and KSS Function score (P = 0.05). In contrast, there were no statistical differences in the remaining spatiotemporal, kinematic, and kinetic gait parameters.

CONCLUSION

Medial UKA design is superior to TKA design with regard to walking speed, stride length, maximum knee flexion at loading, the 1st peak and the 1st valley of vert-GRF, knee internal rotational moment, knee extension, and KSS Function score. And it could provide a stronger basis for physicians to make clinical decisions.

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.

OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data

Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.

Comparison of wearable sensor to traditional methods in functional outcome measures: A systematic review

Traditional methods of collecting functional outcome measures are widely used for lower extremity arthroplasty outcome assessment. Wearable sensors are emerging as viable tools for functional outcome measures in monitoring of postarthroplasty recovery. The objective of this review was to compare the efficacy of wearable sensors with traditional methods for monitoring postarthroplasty functional recovery. Articles were searched for inclusion in this review that used both traditional and wearable sensor functional outcome measures to assess lower extremity function before and after lower extremity arthroplasty. Two independent screeners reviewed all articles, and resolved differences through consensus and consultation with the senior author. Studies that met inclusion criteria were evaluated for methodologic quality using performed risk of bias assessments. Results from several traditional and wearable sensor functional outcome measures from baseline through follow-up were normalized across studies. Fourteen articles met the inclusion criteria. Six studies used statistical methods to directly compare functional outcome measures and eight studies used qualitative description of comparisons. This review found evidence that wearable sensors detected nuanced functional outcome information on the specific biomechanics and timing of recovery, which were unaccounted for using traditional methods. Wearable sensors have shown promising utility in providing additional recovery information from lower extremity arthroplasty compared with traditional functional outcome measures, but future research is needed to assess the clinical significance of this additional information. Wearable sensor technology is an emerging clinical tool providing advanced and determinative data with the potential for advancing the assessment of lower extremity arthroplasty outcomes.

Wearable Devices for Gait Analysis in Intelligent Healthcare

In this study, we review the role of wearable devices in tracking our daily locomotion. We discuss types of wearable devices that can be used, methods for gait analyses, and multiple healthcare-related applications aided by artificial intelligence. Impaired walking and locomotion are common resulting from injuries, degenerative pathologies, musculoskeletal disorders, and various neurological damages. Daily tracking and gait analysis are convenient and efficient approaches for monitoring human walking, where concreate and rich data can be obtained for examining our posture control mechanism during body movement and providing enhanced clinical pieces of evidence for diagnoses and treatments. Many sensors in wearable devices can help to record data of walking and running; spatiotemporal and kinematic variables can be further calculated in gait analysis. We report our previous works in gait analysis, discussing applications of wearable devices for detecting foot and ankle lesions, supporting surgeons in early diagnosis, and helping physicians with rehabilitation.

Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of the motor skill of walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the body motion's translation during walking. Gait measures from accelerometers can enrich measurements of walking and motor performance. This review article will categorize the aspects of the motor skill of walking and review how trunk-acceleration gait measures during walking can be mapped to motor skill aspects, satisfying a clinical need to understand how well accelerometer measures assess gait. We will clarify how to leverage more complicated acceleration measures to make accurate motor skill decline predictions, thus furthering fall research in older adults.

Unicompartmental knee arthroplasty results in a better gait pattern than total knee arthroplasty: Gait analysis with a smartphone application

OBJECTIVES

The aim of this study was to compare the smartphone- based gait analysis data of patients who underwent total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA).

PATIENTS AND METHODS

Between January 2016 and April 2019, a total of 51 patients (3 males, 48 females; mean age: 60.92 years; range, 51 to 70 years) who were operated with UKA or TKA in our clinic were retrospectively analyzed. The patients were divided into two groups according to the type of procedure as the UKA group (n=17) and unilateral TKA group (n=34). Gait analysis was made via a smartphone application (Gait Analyzer software version 0.9.95.0) with data acquired from the accelerometer of the smartphone. This analysis was performed using data collected from the Acceleration Sensor LSM6DSO into the Samsung Galaxy Note 10 Plus phone. Gait velocity, step time, step length, cadence, step time symmetry, step length symmetry, and vertical COM (vert-COM) parameters were measured.

RESULTS

There were no statistically significant differences between the groups in respect of age, sex, body mass index, operated side, and follow-up duration. Compared to the TKA group, the UKA patients showed a better gait pattern in gait velocity (p=0.03), step time symmetry (p=0.005), and step length symmetry (p=0.024). No significant difference was detected in step time (p=0.807), step length (p=0.302), cadence (p=0.727) and vert-COM parameters (p=0.608).

CONCLUSION

The gait of UKA patients is closer to the physiological pattern with a better gait velocity, step time symmetry, and step length symmetry than TKA patients. The surgical treatment option of UKA for knee medial compartment osteoarthritis leads to a better gait pattern than TKA.

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.

Self-Reported and Performance-Based Outcome Measures Estimation Using Wearables After Unilateral Total Knee Arthroplasty

Total knee arthroplasty is a common surgical treatment to improve ambulatory function for individuals with end-stage osteoarthritis of the knee. Functional and self-reported measures are widely used to assess functional ability and impairment before and after total knee arthroplasty. However, clinical assessments have limitations and often provide subjective and limited information. Seamless gait characteristic monitoring in the real-world condition is a viable alternative to address these limitations, but the effectiveness of using wearable sensors for knee treatment is unclear. The purpose of this study was to determine if inertial gait variables from wearable sensors effectively estimate the questionnaire, performance (6-min walk test, timed up and go, and 30-s chair stand test), and isometric measure outcomes in individuals after unilateral total knee arthroplasty. Eighteen subjects at least 6 months post-surgery participated in the experiment. In one session, three tasks, including self-reported surveys, functional testing, and isometric tests were conducted. In another session, the participants' gait patterns were measured during a 1-min walking test at their self-selected gait speed with two accelerometers worn above the lateral malleoli. Session order was inconsistent between subjects. Significant inertial gait variables were selected using stepwise regressions, and the contributions of different categories of inertial gait variables were examined using hierarchical regressions. Our results indicate inertial gait variables were significantly correlated with performance test and questionnaire outcomes but did not correlate well with isometric strength measures. The findings demonstrate that wearable sensor-based gait analysis may be able to help predict clinical measures in individuals after unilateral knee treatment.

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).

Sensor Positioning Influences the Accuracy of Knee Rom Data of an E-Rehabilitation System: A Preliminary Study with Healthy Subjects

E-rehabilitation is the term used to define medical rehabilitation programs that are implemented at home with the use of information and communication technologies. The aim was to test whether sensor position and the sitting position of the patient influence the accuracy of knee range of movement (ROM) data displayed by the BPMpathway e-rehabilitation system. A preliminary study was conducted in a laboratory setting with healthy adults. Knee ROM data was measured with the BPMpathway e-rehabilitation system and simultaneously with a BIOPAC twin-axis digital goniometer. The main outcome was the root mean squared error (RMSE). A 20% increase or reduction in sitting height led to a RMSE increase. A ventral shift of the BPMpathway sensor by 45° and 90° caused significant measurement errors. A vertical shift was associated with a diminution of the measurement errors. The lowest RMSE (2.4°) was achieved when the sensor was placed below the knee. The knee ROM data measured by the BPMpathway system is comparable to the data of the concurrent system, provided the instructions of the manufacturer are respected concerning the sitting position of the subject for knee exercises, and disregarding the same instructions for sensor positioning, by placing the sensor directly below the knee.

What does digitalization hold for the creation of real-world evidence?

Health-related information is increasingly being collected and stored digitally. These data, either structured or unstructured, are becoming the ubiquitous assets that might enable us to comprehensively map out a patient's health journey from an asymptomatic state of wellness to disease onset and its trajectory. These new data could provide rich real-world evidence for better clinical care and research, if they can be accessed, linked and analyzed-all of which are possible. In this review, these opportunities will be explored through a case vignette of a patient with OA, followed by discussion on how this digitalized real-world evidence could best be utilized, as well as the challenges of data access, quality and maintaining public trust.

Measuring markers of aging and knee osteoarthritis gait using inertial measurement units

Differences in gait with age or knee osteoarthritis have been demonstrated in laboratory studies using optical motion capture (MoCap). While MoCap is accurate and reliable, it is impractical for assessment outside the laboratory. Inertial measurement units (IMUs) may be useful in these situations. Before IMUs are used as a surrogate for MoCap, methods that are reliable, repeatable, and that calculate metrics at similar accuracy to MoCap must be demonstrated. The purpose of this study was to compare spatiotemporal gait parameters and knee range of motion calculated via MoCap to IMU-derived variables and to compare the ability of these tools to discriminate between groups. MoCap and IMU data were collected from young, older, and adults with knee osteoarthritis during overground walking at three self-selected speeds. Walking velocity, stride length, cadence, percent of gait cycle in stance, and sagittal knee range of motion were calculated and compared between tools (MoCap and IMU), between participant groups, and across speed. There were no significant differences between MoCap and IMU outcomes, and root mean square error between tools was ≤0.05 m/s for walking velocity, ≤0.07 m for stride length, ≤0.5 strides/min for cadence, ≤5% for percent of gait cycle in stance, and ≤1.5° for knee range of motion. No interactions were present, suggesting that MoCap and IMU calculated metrics similarly across groups and speeds. These results demonstrate IMUs can accurately calculate spatiotemporal variables and knee range of motion during gait in young and older, asymptomatic and knee osteoarthritis cohorts.

Validation of a Novel Device for the Knee Monitoring of Orthopaedic Patients

Fast-track surgery is becoming increasingly popular, whereas the monitoring of postoperative rehabilitation remains a matter of considerable debate. The aim of this study was to validate a newly developed wearable system intended to monitor knee function and mobility. A sensor system with a nine-degree-of-freedom (DOF) inertial measurement unit (IMU) was developed. Thirteen healthy volunteers performed five 10-meter walking trials with simultaneous sensor and motion capture data collection. The obtained kinematic waveforms were analysed using root mean square error (RMSE) and correlation coefficient (CC) calculations. The Bland–Altman method was used for the agreement of discrete parameters consisting of peak knee angles between systems. To test the reliability, 10 other subjects with sensors walked a track of 10 metres on two consecutive days. The Pearson CC was excellent for the walking data set between both systems (r = 0.96) and very good (r = 0.95) within the sensor system. The RMSE during walking was 5.17° between systems and 6.82° within sensor measurements. No significant differences were detected between the mean values observed, except for the extension angle during the stance phase (E1). Similar results were obtained for the repeatability test. Intra-class correlation coefficients (ICCs) between systems were excellent for the flexion angle during the swing phase (F1); good for the flexion angle during the stance phase (F2) and the re-extension angle, which was calculated by subtracting the extension angle at swing phase (E2) from F2; and moderate for the extension angle during the stance phase (E1), E2 and the range of motion (ROM). ICCs within the sensor measurements were good for the ROM, F2 and re-extension, and moderate for F1, E1 and E2. The study shows that the novel sensor system can record sagittal knee kinematics during walking in healthy subjects comparable to those of a motion capture system.

Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial

BACKGROUND

Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PGHD in TJA to predict patient-reported outcome measures (PROMs).

METHODS

Twenty-two TJA patients were recruited for this pilot study. Three activity trackers collected 35 features from 4 weeks before to 6 weeks following surgery. PROMs were collected at both endpoints (Hip and Knee Disability and Osteoarthritis Outcome Score, Knee Osteoarthritis Outcome Score, and Veterans RAND 12-Item Health Survey Physical Component Score). We used ML to identify features with the highest correlation with PROMs. The algorithm trained on a subset of patients and used 3 feature sets (A, B, and C) to group the rest into one of the 3 PROM clusters.

RESULTS

Fifteen patients completed the study and collected 3 million data points. Three sets of features with the highest R² values relative to PROMs were selected (A, B and C). Data collected through the 11th day had the highest predictive value. The ML algorithm grouped patients into 3 clusters predictive of 6-week PROM results, yielding total sum of squares values ranging from 3.86 (A) to 1.86 (C).

CONCLUSION

This small but critical proof-of-concept study demonstrates that ML can be used in combination with PGHD to predict 6-week PROM data as early as 11 days following TJA surgery. Further study is needed to confirm these findings and their clinical value.

Computation of spatio-temporal parameters in level walking using a single inertial system in lean and obese adolescents

In recent years, the availability of low-cost equipment capable of recording kinematic data during walking has facilitated the outdoor assessment of gait parameters, thus overcoming the limitations of three-dimensional instrumented gait analysis (3D-GA). The aim of this study is twofold: firstly, to investigate whether a single sensor on the lower trunk could provide valid spatio-temporal parameters in level walking in normal-weight and obese adolescents compared to instrumented gait analysis (GA); secondly, to investigate whether the inertial sensor is capable of capturing the spatio-temporal features of obese adolescent gait. These were assessed in 10 obese and 8 non-obese adolescents using both a single inertial sensor on the lower trunk and an optoelectronic system. The parameters obtained were not statistically different in either normal-weight or obese participants between the two methods. Obese adolescents walked with longer stance and double support phase compared to normal-weight participants. The results showed that the inertial system is a valid means of evaluating spatio-temporal parameters in obese individuals.

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 comparison of unicompartmental knee arthroplasty and total knee arthroplasty during level walking

This meta-analysis compared the gait patterns of unicompartmental knee arthroplasty (UKA) patients and total knee arthroplasty (TKA) patients during level walking by evaluating the kinetics, kinematics, and spatiotemporal parameters. Studies were included in the meta-analysis if they assessed the vertical ground reaction force (GRF), joint moment at stance, flexion at initial contact, flexion at swing, overall range of motion (ROM), coronal knee angle at stance, walking speed, cadence, and stride length in UKA patients or TKA patients. Seven non-randomized studies met the criteria for inclusion in this meta-analysis. UKA patients and TKA patients were similar in terms of vertical GRF (95% CI: -0.36 to 0.20; P = 0.60), joint moment (95% CI: -0.55 to 0.63; P = 0.90), kinematic outcomes (95% CI: -0.72 to 1.02; P = 0.74), walking speed (95% CI: -0.27 to 0.81; P = 0.32), and cadence (95% CI: -0.14 to 0.68; P = 0.20). In contrast, the stride length (95% CI: 0.01 to 0.80; P = 0.04) differed significantly between groups. Subgroup analyses revealed that the pooled data were similar between the groups: 1st maximum (heel strike), -0.18 BW (P = 0.53); 1st minimum (mid-stance), -0.43 BW (P = 0.08); and 2nd maximum (toe off), -0.03 BW (P = 0.87). On gait analysis, there were no significant differences in vertical GRF, joint moment at stance, overall kinematics, walking speed, or cadence between UKA patients and TKA patients during level walking. However, the TKA group had significantly shorter stride length than UKA patients. Although the comparison was inconclusive in determining which types of knee arthroplasty offered the closest approximation to normal gait, we consider it important to provide better rehabilitation programs to reduce the abnormal stride length in TKA patients compared to UKA patients.

Mobile assessment of the lower limb kinematics in healthy persons and in persons with degenerative knee disorders: A systematic review

Inertial sensor systems are increasingly used in the assessment of persons with knee osteoarthritis (KOA) and total knee replacement (TKR). This systematic review aims to (1) investigate the application of inertial sensor systems and kinematics derived from these systems, and (2) assess if current assessment protocols consist of tasks which are, according to the International Classification of Functioning, Disability and Health (ICF) for KOA, relevant for persons with KOA and TKR. A search was conducted in six electronic databases (ACM, CINAHL, EMBASE, IEEE, PubMed, Web of Science) to include papers assessing the knee and one or more adjacent joints by means of inertial sensors in healthy persons or persons with KOA or TKR. Two reviewers checked the methodological quality. Twenty-three papers were included: 18 in healthy persons and five in persons with KOA or TKR. In healthy persons, 11 tasks were related to metrics of the ICF-function and ICF-activity level. In persons with KOA, only walking was assessed. Apart from walking, four additional tasks were related to the ICF-function and ICF-activity level in persons with TKR. In healthy persons, joints located proximally and distally to the knee were assessed, while in persons with KOA and TKR, only the knee and ankle were assessed. This is a shortcoming since hip and trunk motion potentially contain clinically relevant information, in terms of identifying (mal)adaptive compensatory movement strategies. Additionally, physically more demanding tasks should be evaluated as these might be superior in detecting compensatory movement strategies. Former considerations warrant attention in future research.

What Is the Proportion of Studies Reporting Patient and Practitioner Satisfaction with Software Support Tools Used in the Management of Knee Pain and Is This Related to Sample Size, Effect Size, and Journal Impact Factor?

INTRODUCTION

E-health software tools have been deployed in managing knee conditions. Reporting of patient and practitioner satisfaction in studies regarding e-health usage is not widely explored. The objective of this review was to identify studies describing patient and practitioner satisfaction with software use concerning knee pain.

MATERIALS AND METHODS

A computerized search was undertaken: four electronic databases were searched from January 2007 until January 2017. Keywords were decision dashboard, clinical decision, Web-based resource, evidence support, and knee. Full texts were scanned for effect of size reporting and satisfaction scales from participants and practitioners. Binary regression was run; impact factor and sample size were predictors with indicators for satisfaction and effect size reporting as dependent variables.

RESULTS

Seventy-seven articles were retrieved; 37 studies were included in final analysis. Ten studies reported patient satisfaction ratings (27.8%): a single study reported both patient and practitioner satisfaction (2.8%). Randomized control trials were the most common design (35%) and knee osteoarthritis the most prevalent condition (38%). Electronic patient-reported outcome measures and Web-based training were the most common interventions. No significant dependency was found within the regression models (p > 0.05).

DISCUSSION AND CONCLUSIONS

The proportion of reporting of patient satisfaction was low; practitioner satisfaction was poorly represented. There may be implications for the suitability of administering e-health, a medium for capturing further meta-evidence needs to be established and used as best practice for implicated studies in future. This is the first review of its kind to address patient and practitioner satisfaction with knee e-health.

Effects of narrow-base walking and dual tasking on gait spatiotemporal characteristics in anterior cruciate ligament-injured adults compared to healthy adults

PURPOSE

The present experiment was conducted to examine the hypothesis that challenging control through narrow-base walking and/or dual tasking affects ACL-injured adults more than healthy control adults.

METHODS

Twenty male ACL-injured adults and twenty healthy male adults walked on a treadmill at a comfortable speed under two base-of-support conditions, normal-base versus narrow-base, with and without a cognitive task. Gait patterns were assessed using mean and variability of step length and mean and variability of step velocity. Cognitive performance was assessed using the number of correct counts in a backward counting task.

RESULTS

Narrow-base walking resulted in a larger decrease in step length and a more pronounced increase in variability of step length and of step velocity in ACL-injured adults than in healthy adults. For most of the gait parameters and for backward counting performance, the dual-tasking effect was similar between the two groups.

CONCLUSIONS

ACL-injured adults adopt a more conservative and more unstable gait pattern during narrow-base walking. This can be largely explained by deficits of postural control in ACL-injured adults, which impairs gait under more balance-demanding conditions. The observation that the dual-tasking effect did not differ between the groups may be explained by the fact that walking is an automatic process that involves minimal use of attentional resources, even after ACL injury. Clinicians should consider the need to include aspects of terrain complexity, such as walking on a narrow walkway, in gait assessment and training of patients with ACL injury.

LEVEL OF EVIDENCE

III.

Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters

The purpose of this study was to assess the concurrent validity and test–retest reliability of a sensor-based gait analysis system. Eleven healthy subjects and four Parkinson’s disease (PD) patients were asked to complete gait tasks whilst wearing two inertial measurement units at their feet. The extracted spatio-temporal parameters of 1166 strides were compared to those extracted from a reference camera-based motion capture system concerning concurrent validity. Test–retest reliability was assessed for five healthy subjects at three different days in a two week period. The two systems were highly correlated for all gait parameters (r>0.93). The bias for stride time was 0±16 ms and for stride length was 1.4±6.7 cm. No systematic range dependent errors were observed and no significant changes existed between healthy subjects and PD patients. Test-retest reliability was excellent for all parameters (intraclass correlation (ICC) > 0.81) except for gait velocity (ICC > 0.55). The sensor-based system was able to accurately capture spatio-temporal gait parameters as compared to the reference camera-based system for normal and impaired gait. The system’s high retest reliability renders the use in recurrent clinical measurements and in long-term applications feasible.

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.

Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.

In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.

In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration.

Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.

Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.

Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty

Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements.

Examination of Inertial Sensor-Based Estimation Methods of Lower Limb Joint Moments and Ground Reaction Force: Results for Squat and Sit-to-Stand Movements in the Sagittal Plane

Joint moment estimation by a camera-based motion measurement system and a force plate has a limitation of measurement environment and is costly. The purpose of this paper is to evaluate quantitatively inertial sensor-based joint moment estimation methods with five-link, four-link and three-link rigid body models using different trunk segmented models. Joint moments, ground reaction forces (GRF) and center of pressure (CoP) were estimated for squat and sit-to-stand movements in the sagittal plane measured with six healthy subjects. The five-link model and the four-link model that the trunk was divided at the highest point of the iliac crest (four-link-IC model) were appropriate for joint moment estimation with inertial sensors, which showed average RMS values of about 0.1 Nm/kg for all lower limb joints and average correlation coefficients of about 0.98 for hip and knee joints and about 0.80 for ankle joint. Average root mean square (RMS) errors of horizontal and vertical GRFs and CoP were about 10 N, 15 N and 2 cm, respectively. Inertial sensor-based method was suggested to be an option for estimating joint moments of the trunk segments. Inertial sensors were also shown to be useful for the bottom-up estimation method using measured GRFs, in which average RMS values and average correlation coefficients were about 0.06 Nm/kg and larger than about 0.98 for all joints.

Inertial sensor-based smoother for gait analysis

An off-line smoother algorithm is proposed to estimate foot motion using an inertial sensor unit (three-axis gyroscopes and accelerometers) attached to a shoe. The smoother gives more accurate foot motion estimation than filter-based algorithms by using all of the sensor data instead of using the current sensor data. The algorithm consists of two parts. In the first part, a Kalman filter is used to obtain initial foot motion estimation. In the second part, the error in the initial estimation is compensated using a smoother, where the problem is formulated in the quadratic optimization problem. An efficient solution of the quadratic optimization problem is given using the sparse structure. Through experiments, it is shown that the proposed algorithm can estimate foot motion more accurately than a filter-based algorithm with reasonable computation time. In particular, there is significant improvement in the foot motion estimation when the foot is moving off the floor: the z-axis position error squared sum (total time: 3.47 s) when the foot is in the air is 0.0807 m2 (Kalman filter) and 0.0020 m2 (the proposed smoother).