Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices.

Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls. It is hypothesized that one reason for the significant prediction errors in the current control systems for powered lower-limb prostheses is due to the inter- and intra-subject variability of the data sources used for prediction. Environmental data, recorded from a depth sensor worn on a belt, should have less variability across trials and subjects as compared to kinetics, kinematics and EMG data, and thus its addition is proposed. The variability of each data source was analyzed, once normalized, to determine the intra-activity and intra-subject variability for each sensor modality. Then measures of separability, repeatability, clustering and overall desirability were computed. Results showed that combining Vision, EMG, IMU (inertial measurement unit), and Goniometer features yielded the best separability, repeatability, clustering and desirability across subjects and activities. This will likely be useful for future application in a forward predictor, which will incorporate Vision-based environmental data into a forward predictor for powered lower-limb prosthesis and exoskeletons.

Deep generative models with data augmentation to learn robust representations of movement intention for powered leg prostheses.

Intent recognition is a data-driven alternative to expert-crafted rules for triggering transitions between pre-programmed activity modes of a powered leg prosthesis. Movement-related signals from prosthesis sensors detected prior to movement completion are used to predict the upcoming activity. Usually, training data comprised of labeled examples of each activity are necessary; however, the process of collecting a sufficiently large and rich training dataset from an amputee population is tedious. In addition, covariate shift can have detrimental effects on a controller's prediction accuracy if the classifier's learned representation of movement intention is not robust enough. Our objective was to develop and evaluate techniques to learn robust representations of movement intention using data augmentation and deep neural networks. In an offline analysis of data collected from four amputee subjects across three days each, we demonstrate that our approach produced realistic synthetic sensor data that helped reduce error rates when training and testing on different days and different users. Our novel approach introduces an effective and generalizable strategy for augmenting wearable robotics sensor data, challenging a pre-existing notion that rehabilitation robotics can only derive limited benefit from state-of-the-art deep learning techniques typically requiring more vast amounts of data.

Fusion of Bilateral Lower-Limb Neuromechanical Signals Improves Prediction of Locomotor Activities.

Wearable lower-limb assistive devices have the potential to dramatically improve the walking ability of millions of individuals with gait impairments. However, most control systems for these devices do not enable smooth transitions between locomotor activities because they cannot continuously predict the user's intended movements. Intent recognition is an alternative control strategy that uses patterns of signals detected before movement completion to predict future states. This strategy has already enabled amputees to walk and transition seamlessly and intuitively between activities (e.g., level ground, stairs, ramps) using control signals from mechanical sensors embedded in the prosthesis and muscles of their residual limb. Walking requires interlimb coordination because the leading and trailing legs have distinct biomechanical functions. For unilaterally-impaired individuals, these differences tend to be amplified because they develop asymmetric gait patterns; however, state-of-the-art intent recognition approaches have not been systematically applied to bilateral neuromechanical control signals. The purpose of this study was to determine the effect of including contralateral side signals for control in an intent recognition framework. First, we conducted an offline analysis using signals from bilateral lower-limb electromyography (EMG) and joint and limb kinematics recorded from 10 able-bodied subjects as they freely transitioned between level ground, stairs, and ramps without an assistive device. We hypothesized that including information from the contralateral side would reduce classification errors. Compared to ipsilateral sensors only, bilateral sensor fusion significantly reduced error rates; moreover, only one additional sensor from the contralateral side was needed to achieve a significant reduction in error rates. To the best of our knowledge, this is the first study to systematically investigate using simultaneously recorded bilateral lower-limb neuromechanical signals for intent recognition. These results provide a device-agnostic benchmark for intent recognition with bilateral neuromechanical signals and suggest that bilateral sensor fusion can be a simple but effective modular strategy for enhancing the control of lower-limb assistive devices. Finally, we provide preliminary offline results from one above-knee amputee walking with a powered leg prosthesis as a proof-of-concept for the generalizability and benefit of using bilateral sensor fusion to control an assistive device for an impaired population.

Corrigendum: Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals.

[This corrects the article DOI: 10.3389/frobt.2018.00014.].