A domain-agnostic approach for characterization of lifelong learning systems.

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.

E-textile based modular sEMG suit for large area level of effort analysis.

We present a novel design for an e-textile based surface electromyography (sEMG) suit that incorporates stretchable conductive textiles as electrodes and interconnects within an athletic compression garment. The fabrication and assembly approach is a facile combination of laser cutting and heat-press lamination that provides for rapid prototyping of designs in a typical research environment without need for any specialized textile or garment manufacturing equipment. The materials used are robust to wear, resilient to the high strains encountered in clothing, and can be machine laundered. The suit produces sEMG signal quality comparable to conventional adhesive electrodes, but with improved comfort, longevity, and reusability. The embedded electronics provide signal conditioning, amplification, digitization, and processing power to convert the raw EMG signals to a level-of-effort estimation for flexion and extension of the elbow and knee joints. The approach we detail herein is also expected to be extensible to a variety of other electrophysiological sensors.