The most important indicator of potential cardiovascular disease is blood pressure (BP), yet traditional cuff-based methods for measuring BP can lead to inaccurate measurements. These methods are, moreover, not practical for the continuous monitoring required for a personalized approach to detecting abnormal BP fluctuations. This study proposes a novel machine-learning model for estimating BP using a subject’s previous BP measurements combined with wavelet decomposition to extract features from the photoplethysmogram (PPG) signal, a simple and popular tool for non-invasive diagnosis. To process the arterial BP time series, the researchers use an exponentially weighted moving average (EMWA) and a peak direction technique to derive systolic blood pressure (SBP), diastolic blood pressure (DBP) and their corresponding trends. They then construct a predictive model based on these features using a random forest model, as well as the MIMIC dataset to analyze and compare the results with other BP estimation methods. The proposed approach demonstrates a smaller estimation error than the cuff-based standard and all other methods studied, with mean average errors for SBP and DBP equal to 3.43 and 1.73 respectively.
The Center of Mass (CoM) calculation is an important tool for evaluating a person’s balance and predicting fall risk (particularly as it applies to the effectiveness of physical rehabilitation programs). Traditional techniques for measuring the CoM position include laboratory-grade devices such as a force plate, which are expensive and inconvenient for home use. Inspired by the rapid development of vision-based techniques, this paper proposes a deep learning-based framework that uses a single depth camera to measure the CoM position. The framework estimates the horizontal CoM position of a subject using body parameters obtained from depth image data collected from multiple subjects in various postures. The proposed approach has proven to be highly accurate in estimating the CoM of existing subjects or a new subject and does not require the complicated calibration or subject identification characteristic of existing CoM techniques. It thereby creates a portable and low-cost alternative for enabling automated balance evaluation at home.
The continuing proliferation of wireless electronic devices, coupled with the promise of fifth generation mobile networks (5G) and Internet-of-Things (IoT) scale connectivity, will demand innovative design techniques and solutions on all network and device layers for both wireless and optical systems. Broadband and software-defined connectivity is at the forefront of research efforts to address these new challenges. The work presented in this paper explores the limits of current CMOS technology with the goal of achieving a true DC-100 GHz software-defined transmitter front-end, and with the maximum achievable instantaneous bandwidth. This paper presents a DC-60 GHz I/Q modulator/transmitter chip in 45 nm SOI CMOS, that can serve as a critical building block for next generation multi-standard and high-capacity wireless backhaul links. This new transmitter will address new applications, such as short-range device-to-device communications, server-to-server connectivity in data centers, and fifth generation mm-wave software-defined transceivers, while still supporting traditional mobile links and connectivity below 6 GHz.
Blood pressure (BP) is one of the most important indicator of human health. In this paper, we investigate the relationship between BP and health behavior (e.g. sleep and exercise). Using the data collected from off-the-shelf wearable devices and wireless home BP monitors, we propose a data driven personalized model to predict daily BP level and provide actionable insight into health behavior and daily BP. In the proposed machine learning mode l using Random Forest (RF), trend and periodicity features of BP time-series are extracted to improve prediction. To further enhance the performance of the prediction model, we propose RF with Feature Selection (RFFS), which performs RF-based feature selection to filter out unnecessary features. Our experimental results demonstrate that the proposed approach is robust to different individuals and has smaller prediction error than existing methods. We also validate the effectiveness of personalized recommendation of health behavior generated by RFFS model.
As 360-degree videos and virtual reality (VR) applications become popular for consumer and enterprise use cases, the desire to enable truly mobile experiences also increases. Delivering 360-degree videos and cloud/edge-based VR applications require ultra-high bandwidth and ultra-low latency , challenging to achieve with mobile networks. A common approach to reduce bandwidth is streaming only the field of view (FOV). However, extracting and transmitting the FOV in response to user head motion can add high latency, adversely affecting user experience. In this paper, we propose a predictive view generation approach, where only the predicted view is extracted (for 360-degree video) or rendered (in case of VR) and transmitted in advance, leading to a simultaneous reduction in bandwidth and latency. The view generation method is based on a deep-learning-based viewpoint prediction model we develop, which uses past head motions to predict where a user will be looking in the 360-degree view. Using a very large dataset consisting of head motion traces from over 36,000 viewers for nineteen 360-degree/VR videos, we validate the ability of our viewpoint prediction model and predictive view generation method to offer very high accuracy while simultaneously significantly reducing bandwidth.
We explore the viability of Solar-powered Road Side Units (SRSU), consisting of small cell base stations and Mobile Edge Computing (MEC) servers, and powered solely by solar panels with battery, to provide connected vehicles with a low- latency, easy-to-deploy and energy-efficient communication and edge computing infrastructure. However, SRSU may entail a high risk of power deficiency, leading to severe Quality of Service (QoS) loss due to spatial and temporal fluctuation of solar power generation. Meanwhile, the data traffic demand also varies with space and time. The mismatch between solar power generation and SRSU power consumption makes optimal use of solar power challenging. In this paper, we model the above problem with three sub-problems, the SRSU power consumption minimization problem, the temporal energy balancing problem and spatial energy balancing problem. Three algorithms are proposed to solve the above sub-problems, and they together provide a complete joint battery charging and user association control algorithm to minimize the QoS loss under delay constraint of the computing tasks. Results with a simulated urban environment using actual solar irradiance and vehicular traffic data demonstrates that the proposed solution reduces the QoS loss significantly compared to greedy approaches.
Traditional physical therapy treatment for patients with Parkinson’s disease (PD) requires regular visits with the physical therapist (PT), which may be expensive and inconvenient. In this paper, we propose a learning-based personalized treatment system to enable home-based training for PD patients. It uses the Kinect sensor to monitor the patient’s movements at home. Three physical therapy tasks with multiple difficulty levels are selected by our PT co-author to help PD patients improve balance and mobility. Criteria for each task are carefully designed such that patient’s performance can be automatically evaluated by the proposed system. Given the patient’s motion data, we propose a two-phase human action understanding algorithm TPHAU to understand the patient’s movements. To evaluate patient performance, we use Support Vector Machine to identify the patient’s error in performing the task. Therefore, the patient’s error can be reported to the PT, who can remotely supervise the patient’s performance and conformance on the training tasks. Moreover, the PT can update the tasks that the patient should perform through the cloud-based platform in a timely manner, which enables personalized treatment for the patient. To validate the proposed approach, we have collected data from PD patients in the clinic. Experiments on real patient data show that the proposed methods can accurately understand patient’s actions and identify patient’s movement error in performing the task.