A major issue after total knee replacement (TKR) surgery is asymmetric gait kinetics, which increases knee loads on the non-operated knee. This imbalance accelerates osteoarthritis (OA) progression, often leading to a second contralateral TKR. There is a clear need for an advanced wearable system with multiple sensors to accurately estimate gait kinetics in natural environments. This study aims to develop a machine learning framework that exclusively uses wearable inertial measurement units (IMUs) during overground and treadmill walking to estimate knee flexion moment (KFM) and knee adduction moment (KAM), significant biomechanical factors linked to OA. We introduce a novel deep learning model that combines a Long Short-Term Memory (LSTM)-based Autoencoder and Variational Gaussian Process (VGP) to estimate the mean and uncertainty region of the KAM and KFM. Seventeen healthy participants performed treadmill walking trials, while a separate group of seventeen healthy participants performed overground walking trials for model training and validation. Results demonstrated Root Mean Square Errors (RMSE) of 0.49%BW$\cdot$BH (body weight × body height) and 0.73%BW$\cdot$BH for KAM and KFM, respectively, during treadmill walking and 0.74%BW$\cdot$BH and 0.49%BW$\cdot$BH for KAM and KFM respectively during overground walking, which is more accurate than existing approaches. The proposed model with wearable IMUs could enable knee health monitoring and rehabilitation for these key biomechanical factors linked to the progression of knee joint pathologies outside of traditional biomechanical laboratories with large, tethered equipment and into clinics, hospitals, and the community.

Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating step width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs. Twelve patients with neurodegenerative, clinically diagnosed Spinocerebellar ataxia type 3 (SCA3) performed over ground walking trials, and seventeen healthy individuals performed treadmill walking trials at various speeds and gait modifications while wearing IMUs on each shank and the pelvis. Results demonstrated step width mean absolute errors of 3.3 0.7 cm and 2.9 0.5 cm for the neurodegenerative and healthy groups, respectively, which were below the minimal clinically important difference of 6.0 cm. Step width variability mean absolute errors were 1.5 cm and 0.8 cm for neurodegenerative and healthy groups, respectively. Data augmentation significantly improved accuracy performance in the neurodegenerative group, likely because they exhibited larger variations in walking kinematics as compared with healthy subjects. These results could enable clinically meaningful and accurate portable step width monitoring for individuals with and without neurodegenerative disease, potentially enhancing rehabilitative training, assessment, and dynamic balance control in clinical and real-life settings.

Wearable sensing and computer vision could move biomechanics from specialized laboratories to natural environments, but better algorithms are needed to extract meaningful outcomes from these emerging modalities. In this article, we present new models for estimating biomechanical outcomes—the knee adduction moment (KAM) and knee flexion moment (KFM)—from fusion of smartphone cameras and wearable inertial measurement units (IMUs) among young healthy nonobese males. A deep learning model was developed to extract features, fuse multimodal data, and estimate KAM and KFM. Walking data from 17 subjects were recorded with eight IMUs and two smartphone cameras. The model that used IMU-camera fusion was significantly more accurate than those using IMUs or cameras alone. The root-mean-square errors of the fusion model were 0.49$\%\;\mathbf {BW}\cdot \mathbf {BH}$for KAM and 0.66$\%\;\mathbf {BW}\cdot \mathbf {BH}$for KFM estimation, which are lower than clinically significant thresholds. With larger and more diverse data, this model could enable assessment of knee moments in clinics and homes.

This work investigates real-time estimation of vertical ground reaction force (vGRF) and external knee extension moment (KEM) during single- and double-leg drop landings via wearable inertial measurement units (IMUs) and machine learning. A real-time, modular LSTM model with four sub-deep neural networks was developed to estimate vGRF and KEM. Sixteen subjects wore eight IMUs on the chest, waist, right and left thighs, shanks, and feet and performed drop landing trials. Ground embedded force plates and an optical motion capture system were used for model training and evaluation. During single-leg drop landings, accuracy for the vGRF and KEM estimation was$R^{2}$= 0.88$\pm$0.12 and$R^{2}$= 0.84$\pm$0.14, respectively, and during double-leg drop landings, accuracy for the vGRF and KEM estimation was$R^{2}$= 0.85$\pm$0.11 and$R^{2}$= 0.84$\pm$0.12, respectively. The best vGRF and KEM estimations of the model with the optimal LSTM unit number (130) require eight IMUs placed on the eight selected locations during single-leg drop landings. During double-leg drop landings, the best estimation on a leg only needs five IMUs placed on the chest, waist, and the leg's shank, thigh, and foot. The proposed modular LSTM-based model with optimally-configurable wearable IMUs can accurately estimate vGRF and KEM in real-time with relatively low computational cost during single- and double-leg drop landing tasks. This investigation could potentially enable in-field, non-contact anterior cruciate ligament injury risk screening and intervention training programs.

Wearable activity recognition can collate the type, intensity, and duration of each child's physical activity profile, which is important for exploring underlying adolescent health mechanisms. Traditional machine-learning-based approaches require large labeled data sets; however, child activity data sets are typically small and insufficient. Thus, we proposed a transfer learning approach that adapts adult-domain data to train a high-fidelity, subject-independent model for child activity recognition. Twenty children and twenty adults wore an accelerometer wristband while performing walking, running, sitting, and rope skipping activities. Activity classification accuracy was determined via the traditional machine learning approach without transfer learning and with the proposed subject-independent transfer learning approach. Results showed that transfer learning increased classification accuracy to 91.4% as compared to 80.6% without transfer learning. These results suggest that subject-independent transfer learning can improve accuracy and potentially reduce the size of the required child data sets to enable physical activity monitoring systems to be adopted more widely, quickly, and economically for children and provide deeper insights into injury prevention and health promotion strategies.

OBJECTIVE: Enable accurate estimation of vertical average loading rate (VALR) in runners with one or more wearable inertial measurement units (IMUs). METHODS: A subject-independent convolutional neural network (CNN) model was developed to estimate VALR from wearable IMUs. Fifteen runners wore IMUs at the trunk, pelvis, thigh, shank, and foot and ran on an instrumented treadmill for combinations of the following conditions: foot-strike (forefoot, mid-foot, rear-foot), step rate (90% to 110% of baseline), running speed (2.4 m/s and 2.8 m/s) and footwear (standard and minimalist running shoes). Thirty-one IMU placement configurations with combinations of one to five IMUs were evaluated. VALR estimations from the wearable IMUs were compared with force-plate VALR measurements. RESULTS: VALR estimations via the subject-independent CNN model with a single shank-worn IMU were highly correlated (ρ = 0.94) with force-plate VALR measurements and were substantially higher than previously reported peak tibial acceleration correlations with force-plate VALR measurements from shank-worn accelerometers (ρ = 0.44-0.66). Correlation results from the CNN model for a single IMU placed at the foot, pelvis, trunk, and thigh were ρ = 0.91, 0.76, 0.69, and 0.65, respectively. There was no improvement in accuracy from the shank-worn IMU when adding 1-4 additional IMUs from the trunk, pelvis, thigh, or foot. CONCLUSION: The proposed subject-independent CNN model with a single shank-worn IMU provides more accurate estimation of VALR than previous wearable sensing approaches. SIGNIFICANCE: This could enable runners to more accurately assess impact loading rates and potentially provide insights into running-related injury risk and prevention.