Corneal potential maps obtained in response to full-field flash stimuli are altered in eyes with scotomas in the central and far-peripheral retina. The meERG approach yields useful spatial information following a single brief flash, analogous to body-surface potential maps used to evaluate heart and brain.

No abstract

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 regression model that uses minimal clinical data—a set of six features easily obtained in the clinic—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 a ground-truth dataset 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 standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.

Inexpensive wearable sensors are expected to transform both research and clinical practice by monitoring patient movement outside of the laboratory and helping personalize the treatment of mobility impairments [1]. To meet these expectations, wearable sensors need to be benchmarked against clinical standards, be robust to placement errors by non-experts, and provide reliable data over long periods of time. Inertial sensing remains the only wearable technology that has been comprehensively characterized and benchmarked against gold-standard biomechanical measurements, but it is sensitive to both drift and placement error [2] and does not provide estimations of muscle activity, which are relevant to numerous mobility impairments. Here we characterize capacitive touch sensing [3] as a gait rehabilitation monitoring technology for the first time, finding that it captures clinically relevant biomechanical measures with the fidelity of laboratory tools. We also show that a circumferential lower-limb capacitive sensing sleeve is more effective than electromyography and musculoskeletal simulations at detecting therapeutically relevant gait modifications used to prevent osteoarthritis progression. Finally, we show that our capacitive sensing approach is robust to placement errors and measurement drift over a 6-hour trial, both of which are insignificant to tracking adherence to therapeutic gait prescriptions. Our results indicate that capacitive sensing wearables could make rehabilitation monitoring outside laboratory environments more feasible and could be used synergistically with other emerging wearable technologies to provide real-time feedback to patients during daily life [4]. We expect this foundational study of capacitive sensing for rehabilitation monitoring to be translatable to other parts of the body and applicable to a wide range of mobility-related pathologies and emerging human-in-the-loop wearable health technologies [5–7].