Biology subjects

Halilaj, E.

Publications and source records attributed to Halilaj, E..

3 recordsLinked to original sources

bioRxiv2022

Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model

Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused RGB video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of RGB video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model significantly improved estimation of lower-extremity kinematics over the video-only approach and estimation of joint centers over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved estimation of joint kinematics and joint centers over unconstrained fusion, while unconstrained fusion was advantageous in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.

biophysics↗

bioRxiv2021

Predicting Knee Adduction Moment Response to Gait Retraining with Minimal Clinical Data

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a model that uses minimal clinical data to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from ground-truth datasets with both baseline and toe-in gait trials (N=12) enabled the creation of a large (N=138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N=15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the test sets subject average standard deviation of the first peak during baseline walking (0.306 %BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and may provide clinicians with a streamlined pathway to identify a patient-specific gait retraining outcome without requiring gait lab instrumentation. Author SummaryGait retraining as a conservative intervention for knee osteoarthritis shows great promise in extending pain-free mobility and preserving joint health. Although customizing a treatment plan for each patient may help to ensure a therapeutic response, this procedure cannot yet be performed outside of the gait laboratory, preventing research advances from becoming a part of clinical practice. Our work aims to predict the extent to which a patient with knee osteoarthritis will benefit from a non-invasive gait retraining therapy using measures that can be easily collected in the clinic. To overcome a lack of normative databases for gait retraining, we generated data synthetically based on limited ground-truth examples, and provided experimental evidence for the models ability to generalize to new subjects by evaluating on data collected by a separate research group. Our results can contribute to a future in which predicting the therapeutic benefit of a potential treatment can determine a custom treatment path for any patient.

bioengineering↗

bioRxiv2020

Estimation of Kinematics from Inertial Measurement Units Using a Combined Deep Learning and Optimization Framework

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean ({+/-} STD) root mean squared error of less than 1.27 {degrees} ({+/-} 0.38 {degrees}) in flexion/extension, less than 2.52 {degrees} ({+/-} 0.98 {degrees}) in ad/abduction, and less than 3.34 {degrees} ({+/-} 1.02 {degrees}) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit (IMU) data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer an unprecedented view into disease progression, patient recovery, and sports biomechanics.

bioengineering↗