No abstract

A liter-scale system showed real world potential for energy positive wastewater treatment and desalination.

Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject’s abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power.

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].