Search for: All records

Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%. 

ABSTRACT Isothermal nucleic acid amplification tests (iNATs), such as loop-mediated isothermal amplification (LAMP), are good alternatives to PCR-based amplification assays, especially for point-of-care and low-resource use, in part because they can be carried out with relatively simple instrumentation. However, iNATs can often generate spurious amplicons, especially in the absence of target sequences, resulting in false-positive results. This is especially true if signals are based on non-sequence-specific probes, such as intercalating dyes or pH changes. In addition, pathogens often prove to be moving, evolving targets and can accumulate mutations that will lead to inefficient primer binding and thus false-negative results. Multiplex assays targeting different regions of the analyte and logical signal readout using sequence-specific probes can help to reduce both false negatives and false positives. Here, we describe rapid conversion of three previously described SARS-CoV-2 LAMP assays that relied on a non-sequence-specific readout into individual and multiplex one-pot assays that can be visually read using sequence-specific oligonucleotide strand exchange (OSD) probes. We describe both fluorescence-based and Boolean logic-gated colorimetric lateral flow readout methods and demonstrate detection of SARS-CoV-2 virions in crude human saliva. IMPORTANCE One of the key approaches to treatment and control of infectious diseases, such as COVID-19, is accurate and rapid diagnostics that is widely deployable in a timely and scalable manner. To achieve this, it is essential to go beyond the traditional gold standard of quantitative PCR (qPCR) that is often faced with difficulties in scaling due to the complexity of infrastructure and human resource requirements. Isothermal nucleic acid amplification methods, such as loop-mediated isothermal amplification (LAMP), have been long pursued as ideal, low-tech alternatives for rapid, portable testing. However, isothermal approaches often suffer from false signals due to employment of nonspecific readout methods. We describe general principles for rapidly converting nonspecifically read LAMP assays into assays that are read in a sequence-specific manner by using oligonucleotide strand displacement (OSD) probes. We also demonstrate that inclusion of OSD probes in LAMP assays maintains the simplicity of one-pot assays and a visual yes/no readout by using fluorescence or colorimetric lateral-flow dipsticks while providing accurate sequence-specific readout and the ability to logically query multiplex amplicons for redundancy or copresence. These principles not only yielded high-surety isothermal assays for SARS-CoV-2 but might also aid in the design of more sophisticated molecular assays for other analytes. 

Flexible systems that can conform to any shape are desirable for wearable applications. Over the past decade, there have been tremendous advances in the domain of flexible electronics which enabled printing of devices, such as sensors on a flexible substrate. Despite these advances, pure flexible electronics systems are limited by poor performance and large feature sizes. Flexible hybrid electronics (FHE) is an emerging technology which addresses these issues by integrating high performance rigid integrated circuits and flexible devices. Yet, there are no system-level design flows and algorithms for the design of FHE systems. To this end, this paper presents a multi-objective design algorithm to implement a target application optimally using a library of rigid and flexible components. Our algorithm produces a set of Pareto frontiers that optimize the physical flexibility, energy per operation and area metrics. Simulation studies show a 32× range in area and 4× range in flexibility across the set of Pareto-optimal design points.