State-of-the-art in-home activity recognition schemes with wearable devices are mostly capable of detecting coarsegrained activities (e.g., sitting, standing, walking, or lying down), but are not able to distinguish complex activities (e.g., sitting on floor vs. sofa vs. bed). Such schemes are often not effective for emerging critical healthcare applications, for example in remote monitoring of patients with Alzheimer's disease, Bulimia, or Anorexia, because they require a more comprehensive, contextual and fine-grained recognition of complex daily activities of users. In this work, we propose a novel approach for in-home, fine-grained activity recognition with the help of multi-modal wearable sensors on multiple body positions of the users and lightly deployed Bluetooth beacons in the environment. In particular, our solution exploits measuring user's ambient environment and location context with wearable sensing and Bluetooth beacons, along with user movement captured with accelerometer and gyroscope sensors. The proposed algorithm is a two-level supervised classifier with both level running on server. In the first level, multi-sensor data from wearable on each body position are collected and analyzed using our proposed modified Conditional Random Field (CRF) based supervised activity classifier. The classified activity state from each of the wearables data are then fused for deciding the final activity state of user. Preliminary experimental results are presented on the classification of 19 complex daily activities of a user at home.
More than a year after the COVID-19 pandemic was declared, the need still exists for accurate, rapid, inexpensive and non-invasive diagnostic methods that yield high specificity and sensitivity towards the current and newly emerging SARS-CoV-2 strains. Compared to the nasopharyngeal (NP) swabs, several studies have established saliva as a more amenable specimen type for early detection of SARS-CoV-2. Considering the limitations and high demand for COVID-19 testing, we employed MALDI-ToF mass spectrometry in the analysis of 60 gargle samples from human donors and compared the resultant spectra against COVID-19 status. Several standards, including isolated human serum immunoglobulins, and controls, such as pre-COVID-19 saliva and heat inactivated SARS-CoV-2 virus, were simultaneously analyzed to provide a relative view of the saliva and viral proteome as they would appear in this workflow. Five potential biomarker peaks were established that demonstrated high concordance with COVID-19 positive individuals. Overall, the agreement of these results with RT-qPCR testing on NP swabs was ≥90% for the studied cohort, which consisted of young and largely asymptomatic student athletes. From a clinical standpoint, the results from this pilot study suggest that MALDI-ToF could be used to develop a relatively rapid and inexpensive COVID-19 assay.
We design a framework based on Mask Region-based Convolutional Neural Network to automatically detect and separately extract anatomical components of mosquitoes-thorax, wings, abdomen and legs from images. Our training dataset consisted of 1500 smartphone images of nine mosquito species trapped in Florida. In the proposed technique, the first step is to detect anatomical components within a mosquito image. Then, we localize and classify the extracted anatomical components, while simultaneously adding a branch in the neural network architecture to segment pixels containing only the anatomical components. Evaluation results are favorable. To evaluate generality, we test our architecture trained only with mosquito images on bumblebee images. We again reveal favorable results, particularly in extracting wings. Our techniques in this paper have practical applications in public health, taxonomy and citizen-science efforts.
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