Background: Varus thrust during walking, visualized as excessive frontal plane knee motion during weight acceptance, is a modifiable risk factor for progression of knee osteoarthritis. However, visual assessment does not capture thrust severity and quantification with optical motion capture is often not feasible. Inertial sensors may provide a convenient alternative to optical motion capture. This proof-ofconcept study sought to compare wearable inertial sensors to optical motion capture for the quantification of varus thrust.Methods: Twenty-six participants with medial knee osteoarthritis underwent gait analysis at selfselected and fast speeds. Linear regression with generalized estimating equations assessed associations between peak knee adduction velocity or knee adduction excursion from optical motion capture and peak thigh or shank adduction velocity from two inertial sensors on the lower limb. Relationships between inertial measures and peak external knee adduction moment were assessed as a secondary aim.Findings: Both thigh and shank inertial sensor measures were associated with the optical motion capture measures for both speeds (P < 0.001 to P = 0.020), with the thigh measures having less variability than the shank. After accounting for age, sex, body mass index, radiographic severity, and limb alignment, thigh adduction velocity was also associated with knee adduction moment at both speeds (both P < 0.001).Interpretation: An inertial sensor placed on the mid-thigh can quantify varus thrust in people with medial knee osteoarthritis without the need for optical motion capture. This single sensor may be useful for risk screening or evaluating the effects of interventions in large samples.

Background Measuring and modifying movement-related joint loading is integral to the management of lower extremity osteoarthritis (OA). Although traditional approaches rely on measurements made within the laboratory or clinical environments, inertial sensors provide an opportunity to quantify these outcomes in patients’ natural environments, providing greater ecological validity and opportunities to develop large data sets of movement data for the development of OA interventions. Objective This narrative review aimed to discuss and summarize recent developments in the use of inertial sensors for assessing movement during daily activities in individuals with hip and knee OA and to identify how this may translate to improved remote health care for this population. Methods A literature search was performed in November 2018 and repeated in July 2019 and March 2021 using the PubMed and Embase databases for publications on inertial sensors in hip and knee OA published in English within the previous 5 years. The search terms encompassed both OA and wearable sensors. Duplicate studies, systematic reviews, conference abstracts, and study protocols were also excluded. One reviewer screened the search result titles by removing irrelevant studies, and 2 reviewers screened study abstracts to identify studies using inertial sensors as the main sensing technology and a primary outcome related to movement quality. In addition, after the March 2021 search, 2 reviewers rescreened all previously included studies to confirm their relevance to this review. Results From the search process, 43 studies were determined to be relevant and subsequently included in this review. Inertial sensors have been successfully implemented for assessing the presence and severity of OA (n=11), assessing disease progression risk and providing feedback for gait retraining (n=7), and remotely monitoring intervention outcomes and identifying potential responders and nonresponders to interventions (n=14). In addition, studies have validated the use of inertial sensors for these applications (n=8) and analyzed the optimal sensor placement combinations and data input analysis for measuring different metrics of interest (n=3). These studies show promise for remote health care monitoring and intervention delivery in hip and knee OA, but many studies have focused on walking rather than a range of activities of daily living and have been performed in small samples (<100 participants) and in a laboratory rather than in a real-world environment. Conclusions Inertial sensors show promise for remote monitoring, risk assessment, and intervention delivery in individuals with hip and knee OA. Future opportunities remain to validate these sensors in real-world settings across a range of activities of daily living and to optimize sensor placement and data analysis approaches.

Introduction Lower-limb prosthesis users (LLPUs) experience increased fall risk due to gait and balance impairments. Clinical outcome measures are useful for measuring balance impairment and fall risk screening but experience limited resolution and ceiling effects. Recent advances in wearable sensors that can measure different components of gait stability may address these limitations. This study assessed feasibility and construct validity of a wearable sensor system (APDM Mobility Lab) to measure postural control and gait stability. Materials and Methods Lower-limb prosthesis users (n = 22) and able-bodied controls (n = 24) completed an Instrumented Stand-and-Walk Test (ISAW) while wearing the wearable sensors. Known-groups analysis (prosthesis versus controls) and convergence analysis (Prosthetic Limb Users Survey of Mobility [PLUS-M] and Activities-Specific Balance Confidence [ABC] scale) were performed on 20 stability-related measures. Results The system was applied without complications; however, missing anticipatory postural adjustment data points for nine subjects affected the analysis. Of the 20 analyzed measures output by the sensors, only three significantly differed (P < 0.05) between two cohorts, and two demonstrated statistically significant correlations with the self-report measures. Conclusions The results of this study suggest the clinical feasibility but only partial construct validity of the wearable sensor system in conjunction with the ISAW test to measure LLPU stability and balance. The sample consisted of high-functioning LLPUs, so further research should evaluate a more representative sample with additional outcome measures and tasks.

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BACKGROUND The objective of this study is to review and summarize recent developments in wearable technology detailing the key enabling technologies (i.e., sensor components) and applications of wearable technology as they relate to lower extremity osteoarthritis. OBJECTIVE The objective of this study is to review and summarize recent developments in wearable technology detailing the key enabling technologies (i.e., sensor components) and applications of wearable technology as they relate to lower extremity osteoarthritis. METHODS A literature search was performed in March 2021 using the PubMed and EMBASE databases for publications on wearable movement technologies in lower-limb OA. Papers published within the previous 5 years were identified. The search was limited to original research studies published in English. Duplicate studies, systematic reviews, conference abstracts, and study protocols were removed. Sample keywords and their combinations included: (osteoarthritis OR TKA OR total knee arthroplasty OR total knee replacement) AND (wearable* OR sensor). RESULTS From the literature, 72 studies were determined relevant and subsequently included in this review. Wearable technology has successfully been implemented for gait assessment, movement pattern training using feedback, assessment of intervention outcomes, and physical activity monitoring. Additionally, some studies demonstrated algorithms or measurement systems that could be used for movement pattern training with feedback in future implementations. Study participants identified appearance and comfort during use as key aspects for the acceptance of wearable technology, and enjoyed seeing both quantitative sensor data as well as qualitative patient-reported outcomes. CONCLUSIONS Advancements in wearable sensor technology allow for data collection and analysis in both accurate and unobtrusive ways. The technology can be used to passively collect data, implement exercise interventions, or actively retrain movement patterns. Future opportunities remain to have more efficient, smaller systems and provide biofeedback for new, previously unused metrics.