(Background) Inertial Measurement Units (IMUs) provide a low-cost, portable solution to obtain functional measures similar to those captured with three-dimensional gait analysis, including spatiotemporal gait characteristics. The primary aim of this study was to determine the feasibility of a remote patient monitoring (RPM) workflow using ankle-worn IMUs measuring impact load, limb impact load asymmetry and knee range of motion in combination with patient-reported outcome measures. (Methods) A pilot cohort of 14 patients undergoing primary knee arthroplasty for osteoarthritis was prospectively enrolled. RPM in the community was performed weekly from 2 up to 6 weeks post-operatively using wearable IMUs. The following data were collected using IMUs: mobility (Bone Stimulus and cumulative impact load), impact load asymmetry and maximum knee flexion angle. In addition, scores from the Oxford Knee Score (OKS), EuroQol Five-dimension (EQ-5D) with EuroQol visual analogue scale (EQ-VAS) and 6 Minute Walk Test were collected. (Results) On average, the Bone Stimulus and cumulative impact load improved 52% (p = 0.002) and 371% (p = 0.035), compared to Post-Op Week 2. The impact load asymmetry value trended (p = 0.372) towards equal impact loading between the operative and non-operative limb. The mean maximum flexion angle achieved was 99.25° at Post-Operative Week 6, but this was not significantly different from pre-operative measurements (p = 0.1563). There were significant improvements in the mean EQ-5D (0.20; p = 0.047) and OKS (10.86; p < 0.001) scores both by 6 weeks after surgery, compared to pre-operative scores. (Conclusions) This pilot study demonstrates the feasibility of a reliable and low-maintenance workflow system to remotely monitor post-operative progress in knee arthroplasty patients. Preliminary data indicate IMU outputs relating to mobility, impact load asymmetry and range of motion can be obtained using commercially available IMU sensors. Further studies are required to directly correlate the IMU sensor outputs with patient outcomes to establish clinical significance.

Using Inertial Measurement Units (IMUs) in combination with machine learning (ML) techniques has become a common approach for predicting specific gait time series, such as lower-limb joint kinematics and kinetics. However, the optimal number of IMUs, sensor placement, number of features, and window size applicable for predicting all gait time series have not been previously determined. This study aimed to investigate how a random forest (RF) model’s performance can be affected by different combinations of IMU location, the number of features, and window sizes used for feature extraction. We recorded optical motion capture marker trajectories, ground reaction forces, and seven IMUs data from twenty participants during gait. Joint angles and moments were calculated using OpenSim inverse kinematics and dynamics tools from the marker’s trajectory and ground reaction forces. For different combinations of IMUs numbers and locations, the most important features to predict each target (joint kinematics and kinetics) were extracted and used to build RF models. We showed that using only one pair of IMUs placed on the feet was as accurate as using seven IMUs when combined with an RF model to predict gait time series. Once the best IMU combination was determined, we explored the effect of the number of features and window size on RF model performance. A multi-output RF model with ten features per target and a window size of 0.75s outperformed other models by providing lower prediction error for predicting most gait time series.

Knee kinematics is a valuable measure of knee joint function. However, collecting that data outside the clinic is difficult, especially with a limited number of wearable sensors and when you only use an ankle-mounted inertial measurement unit to estimate knee kinematics. Due to the cyclic nature of gait, it is possible to use machine learning to extract joint angles from only ankle-mounted sensors. This study aimed to use time-series feature extraction and a random forest regressor to generate a person-specific surrogate model for estimating knee joint flexion angles from a single-mounted inertial measurement unit above the ankle. Optical motion capture and inertial data from 10 healthy participants walking on a treadmill were collected to create 10 personalised surrogate models for estimating right knee flexion angles during gait. An additional 10 models were created for a leave-one-out analysis to test the generalisability of the models. Temporal cross-validation of the personalised models and a leave-one-out analysis was performed on the selected feature set. The personalised models achieved an average root mean square error of 2.45 ± 0.65 degrees (R ² of 0.98) compared to a gold-standard optical motion capture. The generalised models achieved an average root mean square error of 6.77 ± 3.38 degrees (R ² of 0.83) in the leave-one-out analysis. Time series feature-based personalised surrogate models could be used to accurately estimate knee kinematics by using a single ankle-mounted sensor. However, more data is required to train a generalised model using the presented method.

Morphology and cortical thickness of tibia bone influence the strength and strain distribution of bone and also influence fatigue fracture risk. However, current studies have not extensively explored the effect of morphology and cortical thickness on tibial strain distribution during different activities. This study aims to assess the effect of tibial morphology and cortical thickness on tibial strain during six different sports movements. The tibial surfaces were reconstructed from 40 males' CT data, with cortical thickness assessed at the outer surface. A statistical shape model captured main variations in tibial morphology and cortical thickness. Finite Element models were created by scaling the mean shape along the first four principal components. Muscle and joint forces from different activities were calculated using static optimization and joint reaction analysis and applied to the models, assessing strained volume and peak strain at middle and distal tibia. The first four principal components accounted for 87 % of the total cumulative variance. Perturbations in the second principal components resulted in the greatest relative differences in peak mid-tibia tensile (128 %) and distal-tibia compressive (160 %) strain during sidestep cutting, but perturbations in the first principal components resulted in the greatest relative differences during other activities (70 %∼118 %, 107 %∼129 %). Perturbations in the first four principal components resulted in the small relative differences in strained volume during walking (-9%∼5%). For runners, tibial size and cortical thickness are more related to tibial fatigue fracture risk, whereas for athletes with frequent directional changes, like basketball players, the tibial shaft size is more relevant.

Aims: The purpose of this study was to evaluate early outcomes of patients who underwent robotic-assisted total knee arthroplasty (RA-TKA) and conventional TKA using wearable sensors and traditional patient-reported outcome measures (PROMs). Methods: This was a prospective, matched, parallel cohort study of 100 patients with symptomatic end-stage knee osteoarthritis undergoing primary TKA (44 RA-TKA, 56 conventional TKA). Functional outcomes were assessed using wearable inertial measurement units (IMUs). IMU-based outcomes included impact load, impact asymmetry, maximum knee flexion angle, and bone stimulus. PROMs, including Oxford Knee Score, EuroQol five-dimension five-level questionnaire, EuroQol visual analogue scale, and Forgotten Joint Score, were evaluated at preoperative baseline and weeks 2 to 6 postoperatively. Results: By postoperative week 6, RA-TKA patients showed improved function compared with the conventional TKA group, with increased maximum knee flexion angle (118° (SD 6.6°) vs 113° (SD 5.4°); p = 0.038), improved symmetry with regard to limb loading (82.3% (SD 16.1) vs 22.4% (SD 9.5); p < 0.001), increased cumulative impact load (146.6% (SD 16.7) vs 37% (SD 8.1); p < 0.001), and increased bone stimulus (25.1% vs 13.6%; p = 0.003). RA-TKA patients demonstrated an earlier return to symmetrical limb loading compared with conventional TKA by postoperative week 3. There were no significant differences in PROMs between the two groups at any timepoint. Conclusion: RA-TKAs using personalized alignment were associated with faster return of symmetrical gait and improved range of motion when assessed using IMUs compared with conventional TKA, which were not detected by traditional PROMs during the early postoperative period. Cite this article: Bone Joint J 2026;108-B(4):478–489.

Cam-type femoroacetabular impingement (FAI) is traditionally diagnosed using the alpha angle, a two-dimensional metric that inadequately represents the three-dimensional nature of cam morphology. This study introduces a novel image-based framework that optimizes the alpha angle to better predict 3D cam deformity volume. Forty-five participants underwent MRI scans to obtain traditional alpha angle measurements and to develop a non-cam-deformed statistical shape model, which was fitted to all segmentations to calculate volumetric cam deviation. Automated alpha angles were calculated across radial planes, with the circle fitting radius varied systematically. Regression analyses compared alpha angle predictions of cam volume between traditional and optimized methods. Results demonstrated that adjusting the circle radius by +2 mm from the anatomical contour yielded the most accurate volume predictions. Sex-specific differences emerged, with the 2o'clock orientation optimal for males and 3o'clock for females. The optimized method outperformed traditional measurements, reducing cam volume prediction error by 30 % in females and 23 % in males. These findings highlight the limitations of current 2D imaging practices and advocate for sex-specific alpha angle parameters that retain clinical feasibility while improving diagnostic precision. By bridging the gap between 2D measurements and 3D deformity assessment, this approach enhances the clinical utility of the alpha angle in evaluating cam morphology.