Shull, P. B. | Search bioRxiv

bioRxiv2022

Synthetic EMG Based on Adversarial Style Transfer can Effectively Attack Biometric-based Personal Identification Models

Biometric-based personal identification models are generally considered to be accurate and secure because biological signals are too complex and person-specific to be fabricated, and EMG signals, in particular, have been used as biological identification tokens due to their high dimension and non-linearity. We investigate the possibility of effectively attacking EMG-based identification models with biological adversarial input via a novel EMG signal individual style transformer based on a generative adversarial network. EMG hand gesture data from eighteen subjects and three well-recognized deep EMG classifiers were used to demonstrate the effectiveness of the proposed attack methods. The proposed methods achieved an average of 99.41% success rate on confusing identification models and an average of 91.51% success rate on manipulating identification models. These results demonstrate that EMG classifiers based on deep neural networks can be vulnerable to synthetic data attacks. The proof-of-concept results reveal that synthetic EMG biological signals must be considered in biological identification system design across a vast array of relevant biometric systems to ensure personal identification security for individuals and institutions.

neuroscience↗

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↗