• Unsupervised Segmentation and Labeling for Smartphone Acquired Gait Data

      Martinez, Matthew; De Leon, Phillip L.; New Mexico State University, Klipsch School of Elec. & Comp. Eng.; Sandia National Laboratories (International Foundation for Telemetering, 2016-11)
      As the population ages, prediction of falls risk is becoming an increasingly important research area. Due to built-in inertial sensors and ubiquity, smartphones provide an at- tractive data collection and computing platform for falls risk prediction and continuous gait monitoring. One challenge in continuous gait monitoring is that signi cant signal variability exists between individuals with a high falls risk and those with low-risk. This variability increases the di cultly in building a universal system which segments and labels changes in signal state. This paper presents a method which uses unsu- pervised learning techniques to automatically segment a gait signal by computing the dissimilarity between two consecutive windows of data, applying an adaptive threshold algorithm to detect changes in signal state, and using a rule-based gait recognition al- gorithm to label the data. Using inertial data,the segmentation algorithm is compared against manually segmented data and is capable of achieving recognition rates greater than 71.8%.

      Holmeide, Ø.; Schmitz, M.; OnTime Networks AS; OnTime Networks LLC (International Foundation for Telemetering, 2016-11)
      As Ethernet based networks have become the dominant choice for Flight Test Instrumentation (FTI) network applications, it is also clear that Ethernet based camera integration and applications have yet to become more wide spread for system level design and integration. A significant customer base utilizes either separate video compression systems or even just stand-a-lone gopro cameras for recording purposes in an unsynchronized ways. The use of uncompressed high definition (HD) video from GigE Vision Ethernet cameras for flight test applications is a significant issue in managing the large volumes of data produced by the cameras and forwarding them to any 1000BASE-T(x) switch port without packet loss and significant delays. Of course an easy approach to overcome this issue would be to just increase the network bandwidth from 1000BASE-T(x) to 10GBASE-SR, but most FTI systems just moved to 1000BASE-T(x) in the past years and therefore changing the overall system hardware is cost prohibited. One concern has been the use of compression algorithms to reduce the required video bandwidth, with the negative side effect that the image quality reduces and end-to-end latency increases, which is not acceptable for some applications. Further, it is important that data from cameras is available to a number of different multicast consumers within the FTI network, for example workstations, recorders and telemetry systems. These video data stream also require synchronization so that they can be analyzed in post processing.

      Lee, Hua; Radzicki, Vincent R.; University of California, Department of Electrical and Computer Engineering (International Foundation for Telemetering, 2016-11)
      This paper introduces a generalized and computationally efficient approach for the estimation of target motion parameters from received wavefield data collected from coherent sensing systems such as radar and sonar measurement arrays. The mathematical content of the algorithm is described, along with the general processing procedure to perform on recorded data. The algorithm presents a solution to the joint estimation of translational motion and periodic motion of targets, which has many practical applications for sensing and detection tasks. Experimental and simulation results are included supporting the effectiveness of the method.

      Marcellin, Michael; Melde, Kathleen; Fajardo, Nicolas; Garrick, Kevin; Giroud, Xaviere; Kehn, Brian; Maggio, Andrew; Read, Cecilia; Univ Arizona, Dept Elect & Comp Engn (International Foundation for Telemetering, 2016-11)
      This document will provide a detailed description of the original design behind our device, device casing, and iOS application. It will cover process of assembly, as well as failure analysis and future directions for the project.