Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform.

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than -10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%.

Interactive, on-line visualization tools to measure and drive equity in COVID-19 vaccine administrations.

Equitable distribution of vaccines is necessary to ensure those at highest risk of illness are protected from COVID-19 (coronavirus disease 2019). Unfortunately, there is significant evidence that vaccines have not been reaching the most vulnerable. At our large hospital system, we created interactive online tools to measure and visualize equitability of vaccine administrations and to help stakeholders identify populations at highest risk within state-designated eligible vaccine groups. Using race, ethnicity, gender, and social vulnerability, we are able to measure and reflect our vaccine administration performance against the communities that we serve. With our visualization tools, stakeholders have been able to target interventions to improve equity in vaccine administrations, including improvements in race, ethnicity, and social vulnerability. We plan to use the data elements incorporated in our electronic health record and data warehouse due to the COVID-19 pandemic to guide further population health efforts at decreasing disparities.

Wearable Sensor Gait Analysis of Fall Detection using Attention Network.

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding- founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.

FPGA-based Edge Inferencing for Fall Detection.

In the geriatric population, physical injuries sustained by an unintentional or an unpredictable fall on a hard surface is the leading cause of injury related morbidity and sometimes mortality. Each year, close to 30% of adults around the age group of 65 fall down at least once. In the year 2015, close to 2.9 million falls were reported, resulting in 33,000 deaths. As much as 61% of elderly nursing home residents fell at some point during their first year of residence.These falls may aggravate the situation leading to bone fracture, concussion, internal bleeding or traumatic brain injury when immediate medical attention is not offered to the person. Delay in course of the event may sometimes lead to death as well. Recently, many studies have come up with wearable devices. These devices that are now commercially available in the market are small, compact, wireless, battery operated and power efficient. This study discusses the findings that the optimal location for a Fall Detection Sensor on the human body is in front of the Shin bone. This is based on the 183 features collected from Inertial Measurement Unit (IMU) sensors placed on 16 human body locations and trained-tested using Convolutional Neural Networks (CNN) machine learning paradigm. The ultimate goal is to develop a mobile, wireless, wearable, low-power medical device that uses a small Lattice iCE40 Field Programmable Gate Array (FPGA) integrated with gyro and accelerometer sensors which detects whether the device wearer has fallen or not. This FPGA is capable of realizing the Neural Network model implemented in it. This Insitu or Edge inferencing wearable device is capable of providing real-time classifications without any Transmitting or Receiving capabilities over a wireless communication channel.

Real-Time Indoor Geolocation Tracking for Assisted Healthcare Facilities.

A leading cause of physical injury sustained by elderly persons is the event of unintentionally falling. A delay between the time of fall and the time of medical attention can exacerbate injury if the fall resulted in a concussion, traumatic brain injury, or bone fracture. The authors present a solution capable of finding and tracking, in real-time, the location of an elderly person within an indoor facility, using only existing Wi-Fi infrastructure. This paper discusses the development of an open source software framework capable of finding the location of an individual within 3m accuracy using 802.11 Wi-Fi in good coverage areas. This framework is comprised of an embedded software layer, a Web Services layer, and a mobile application for monitoring the location of individuals, calculated using trilateration, with Kalman filtering employed to reduce the effect of multipath interference. The solution provides a real-time, low cost, extendible solution to the problem of indoor geolocation to mitigate potential harm to elderly persons who have fallen and require immediate medical help.

TOWARDS MUSCULOSKELETAL SIMULATION-AWARE FALL INJURY MITIGATION: TRANSFER LEARNING WITH DEEP CNN FOR FALL DETECTION.

This paper presents early work on a fall detection method using transfer learning method, in conjunction with a long-term effort to combine efficient machine learning and prior personalized musculoskeletal modeling to deploy fall injury mitigation in geriatric subjects. Inspired by the tremendous progress in image-based object recognition with deep convolutional neural networks (DCNNs), we opt for a pre-trained kinematics-based machine learning approach through existing large-scale annotated accelerometry datasets. The accelerometry datasets are converted to images using time-frequency analysis, based on scalograms, by computing the continuous wavelet transform filter bank. Subsequently, data augmentation is performed on these scalogram images to increase accuracy, thereby complementing limited labeled fall sensor data, enabling transfer learning from the existing pre-trained model. The experimental results on publicly available URFD datasets demonstrate that transfer learning leads to a better performance than the existing methods in the case of scarce labeled training data.

Optimal Location for Fall Detection Edge Inferencing.

A leading cause of physical injury sustained by elderly persons is the event of unintentionally falling onto a hard surface. Approximately 32-42% of those 70 and over fall at least once each year, and those who live in assisted living facilities fall with greater frequency per year than those who live in residential communities. Delay between the time of fall and the time of medical attention can exacerbate injury if the fall resulted in concussion, traumatic brain injury, or bone fracture. Several implementations of mobile, wireless, wearable, low-power fall detection sensors (FDS) have become commercially available. These devices are typically worn around the neck as a pendant, or on the wrist, as a watch is worn. Based on features collected from IMU sensors placed at sixteen body locations, and used to train four different machine learning models, our findings show the optimal placement for an FDS on the body is in front of the shinbone.

A web service infrastructure for thermochemical data.

W3C standardized Web Services are becoming an increasingly popular middleware technology used to facilitate the open exchange of chemical data. While several projects in existence use Web Services to wrap existing commercial and open-source tools that mine chemical structure data, no Web Service infrastructure has yet been developed to compute thermochemical properties of substances. This work presents an infrastructure of Web Services for thermochemical data retrieval. Several examples are presented to demonstrate how our Web Services can be called from Java, through JavaScript using an AJAX methodology, and within commonly used commercial applications such as Microsoft Excel and MATLAB for use in computational work. We illustrate how a JANAF table, widely used by chemists and engineers, can be quickly reproduced through our Web Service infrastructure.