Engineered Nucleotide Chemicapacitive Microsensor Array Augmented with Physics-Guided Machine Learning for High-Throughput Screening of Cannabidiol.

The recent legalization of cannabidiol (CBD) to treat neurological conditions such as epilepsy has sparked rising interest across global pharmaceuticals and synthetic biology industries to engineer microbes for sustainable synthetic production of medicinal CBD. Since the process involves screening large amounts of samples, the main challenge is often associated with the conventional screening platform that is time consuming, and laborious with high operating costs. Here, a portable, high-throughput Aptamer-based BioSenSing System (ABS3 ) is introduced for label-free, low-cost, fully automated, and highly accurate CBD concentrations' classification in a complex biological environment. The ABS3 comprises an array of interdigitated microelectrode sensors, each functionalized with different engineered aptamers. To further empower the functionality of the ABS3 , unique electrochemical features from each sensor are synergized using physics-guided multidimensional analysis. The capabilities of this ABS3 are demonstrated by achieving excellent CBD concentrations' classification with a high prediction accuracy of 99.98% and a fast testing time of 22 µs per testing sample using the optimized random forest (RF) model. It is foreseen that this approach will be the key to the realistic transformation from fundamental research to system miniaturization for diagnostics of disease biomarkers and drug development in the field of chemical/bioanalytics.

Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition.

As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human-machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybrid-flexible wearable system, consisting of simple bimodal capacitive sensors and a customized low power interface circuit integrated with machine learning algorithms, to accurately recognize complex gestures. The 16 channel sensor array extracts spatial and temporal information of the finger movement (deformation) and hand location (proximity) simultaneously. Using machine learning, over 99 and 91% accuracy are achieved for user-independent static and dynamic gesture recognition, respectively. Our approach proves that an extremely simple bimodal sensing platform that identifies local interactions and perceives spatial context concurrently, is crucial in the field of sign communication, remote robotics, and smart manufacturing.

Detection of activities by wireless sensors for daily life surveillance: eating and drinking.

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody's (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb's three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.