scholarly journals Multi-Modal, Remote Breathing Monitor

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1229 ◽  
Author(s):  
Nir Regev ◽  
Dov Wulich
Keyword(s):  
Human Subjects ◽  
Elderly Care ◽  
Spectral Estimation ◽  
Harmonic Signal ◽  
Estimation Algorithm ◽  
Maximum Error ◽  
Ultra Wideband ◽  
Ratio Test ◽  
Breathing Rate ◽  
Set Up

Monitoring breathing is important for a plethora of applications including, but not limited to, baby monitoring, sleep monitoring, and elderly care. This paper presents a way to fuse both vision-based and RF-based modalities for the task of estimating the breathing rate of a human. The modalities used are the F200 Intel® RealSenseTM RGB and depth (RGBD) sensor, and an ultra-wideband (UWB) radar. RGB image-based features and their corresponding image coordinates are detected on the human body and are tracked using the famous optical flow algorithm of Lucas and Kanade. The depth at these coordinates is also tracked. The synced-radar received signal is processed to extract the breathing pattern. All of these signals are then passed to a harmonic signal detector which is based on a generalized likelihood ratio test. Finally, a spectral estimation algorithm based on the reformed Pisarenko algorithm tracks the breathing fundamental frequencies in real-time, which are then fused into a one optimal breathing rate in a maximum likelihood fashion. We tested this multimodal set-up on 14 human subjects and we report a maximum error of 0.5 BPM compared to the true breathing rate.

scholarly journals Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3529
Author(s):  
Nir Regev ◽  
Dov Wulich
Keyword(s):  
Breathing Pattern ◽  
Elderly Care ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Breathing Rate ◽  
Human Presence ◽  
Hotel Room ◽  
Energy Expenditures ◽  
Human Presence Detection ◽  
Ml Estimator

Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures. In home security there is an obvious necessity to detect human presence inside the residence. For elderly care and healthcare, the system would like to know if the person is sleeping in the room, sitting on a sofa or conversely, is not present. This paper focuses on the problem of detecting presence using only the minute movements of breathing while at the same time estimating the breathing rate, which is the secondary aim of the paper. We extract the suspected breathing signal, and construct its Fourier series (FS) equivalent. Then we employ a generalized likelihood ratio test (GLRT) on the FS signal to determine if it is a breathing pattern or noise. We will show that calculating the GLRT also yields the maximum likelihood (ML) estimator for the breathing rate. We tested this algorithm on sleeping babies as well as conducted experiments on humans aged 12 to 44 sitting on a chair in front of the radar. The results are reported in the sequel.

Application of Spectral Extrapolation Technique to Stepped-Frequency RCS Measurement

2020 ◽  
Vol 35 (9) ◽  
pp. 999-1005
Author(s):  
Chufeng Hu ◽  
Nanjing Li ◽  
Chonghua Fang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spectral Estimation ◽  
Estimation Algorithm ◽  
Maximum Error ◽  
Extrapolation Method ◽  
Ar Model ◽  
Stepped Frequency ◽  
Core Idea ◽  
The Time Domain

“Time domain gating” used in the stepped-frequency radar cross section (RCS) measurement causes the inaccurate frequency domain data, especially at two ends of the band. This paper proposes a spectral extrapolation method for improving the measured RCS at two ends of the band more exactly. The core idea is: the measured frequency domain data are extrapolated to obtain the unknown value out of band with an auto-regressive model (AR model). The parameter in the AR model is calculated by the maximum entropy spectral estimation algorithm. Therefore, the span of the original band is extended, and both ends of frequency on the original band are inside the range of the new band. If the time domain gating is adding to the new band, the precision at two ends of the original band can be greatly improved. The simulation and experimental results show that more effective frequency domain data near the two ends of the band can be predicted by using the spectral extrapolation method, and the maximum error at the ends of the original band is less than 1dB after extrapolation, so it can ensure the accuracy of RCS measurement over the whole frequency band.

scholarly journals EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ben Somers ◽  
Christopher J. Long ◽  
Tom Francart
Keyword(s):  
Cochlear Implant ◽  
Auditory Evoked Potentials ◽  
Human Subjects ◽  
Electrode Array ◽  
Brain Regions ◽  
Hearing Impaired ◽  
Brain Responses ◽  
Listening Environments ◽  
Set Up ◽  
Neural Feedback

AbstractThe cochlear implant is one of the most successful medical prostheses, allowing deaf and severely hearing-impaired persons to hear again by electrically stimulating the auditory nerve. A trained audiologist adjusts the stimulation settings for good speech understanding, known as “fitting” the implant. This process is based on subjective feedback from the user, making it time-consuming and challenging, especially in paediatric or communication-impaired populations. Furthermore, fittings only happen during infrequent sessions at a clinic, and therefore cannot take into account variable factors that affect the user’s hearing, such as physiological changes and different listening environments. Objective audiometry, in which brain responses evoked by auditory stimulation are collected and analysed, removes the need for active patient participation. However, recording of brain responses still requires expensive equipment that is cumbersome to use. An elegant solution is to record the neural signals using the implant itself. We demonstrate for the first time the recording of continuous electroencephalographic (EEG) signals from the implanted intracochlear electrode array in human subjects, using auditory evoked potentials originating from different brain regions. This was done using a temporary recording set-up with a percutaneous connector used for research purposes. Furthermore, we show that the response morphologies and amplitudes depend crucially on the recording electrode configuration. The integration of an EEG system into cochlear implants paves the way towards chronic neuro-monitoring of hearing-impaired patients in their everyday environment, and neuro-steered hearing prostheses, which can autonomously adjust their output based on neural feedback.

scholarly journals Improved energy detection receiver for ranging in IEEE 802.15.4a standard

2017 ◽  
Vol 10 (2) ◽  
pp. 141-148
Author(s):  
Abdelmadjid Maali ◽  
Geneviève Baudoin ◽  
Ammar Mesloub
Keyword(s):  
Estimation Algorithm ◽  
Energy Detection ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Time Of Arrival ◽  
Power Delay Profile ◽  
Wideband Signals ◽  
Toa Estimation ◽  
Ieee 802.15.4A

In this paper, we propose a novel energy detection (ED) receiver architecture combined with time-of-arrival (TOA) estimation algorithm, compliant to the IEEE 802.15.4a standard. The architecture is based on double overlapping integrators and a sliding correlator. It exploits a series of ternary preamble sequences with perfect autocorrelation property. This property ensures coding gain, which allows an accurate estimation of power delay profile (PDP). To improve TOA estimation, the interpolation of PDP samples is proposed and the architecture is validated by using an ultra-wideband signals measurements platform. These measurements are carried out in line-of-sight and non-line-of-sight multipath environments. The experimental results show that the ranging performances obtained by the proposed architecture are higher than those obtained by the conventional architecture based on a single-integrator in both LOS and NLOS environments.

The series elastic gripper design, object detection and recognition by touch

2021 ◽  
pp. 1-33
Author(s):  
Ozan Kaya ◽  
Gokce Burak Taglioglu ◽  
Seniz Ertugrul
Keyword(s):  
Synthetic Data ◽  
Estimation Algorithm ◽  
Robot Arm ◽  
Force Estimation ◽  
Tactile Sensors ◽  
Stl File ◽  
Detection Of Objects ◽  
Set Up ◽  
Force Calculation ◽  
Series Elastic

Abstract In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with serial elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A Deep Neural Network model is trained by synthetic data extracted from STL file of selected objects . For experimental set-up, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation and the DNN model. The best DNN model capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence the proposed design of gripper and the algorithm achieved the recognition of selected objects without need for additional force/torque or tactile sensors.

scholarly journals A Channel Estimation Algorithm of Ultra-wideband System Using Multi-Templates

2010 ◽  
Vol 32 (10) ◽  
pp. 2468-2472
Author(s):  
Wei Xu ◽  
Jia-xiang Zhao ◽  
Dong Wang ◽  
Xiao-xi Ai
Keyword(s):  
Channel Estimation ◽  
Estimation Algorithm ◽  
Ultra Wideband

Double Phase Differences Correction Method for Frequency and Phase in Spectrum Analysis

2013 ◽  
Vol 584 ◽  
pp. 87-91
Author(s):  
Jiu Fei Luo ◽  
Zhi Jiang Xie ◽  
Ping Chen
Keyword(s):  
Harmonic Signal ◽  
Maximum Error ◽  
Correction Method ◽  
Spectral Lines ◽  
Frequency Resolution ◽  
Two Phase ◽  
Double Phase ◽  
New Formula ◽  
Frequency Phase ◽  
Phase Differences

This paper advances a new method, double phase differences correction method, which aims at correcting the errors of frequency, phase and amplitude of the harmonic signal. The employment of this method involves that two phase differences of two highest spectral lines in the main lob are used to get the correcting value of frequency and then phase and amplitude can be rectified by corresponding formula. It solves the problem that the traditional formula of translation of window center is only for rectangle window. This new formula can correct for the different window by the principle of translation of window center. Simulation shows that the maximum error of frequency of the signal with white noise is less than 1% of frequency resolution. The maximum error of phase is around 1.5º, and the maximum error of amplitude is within 0.01. The average errors in frequency, phase and amplitude are approximately 0.002Hz, 1º and 0.005, respectively.

Study on the Deformation Forecasting for the Tunnel Surrounding Rock Using the Neural Network

2013 ◽  
Vol 353-356 ◽  
pp. 828-832
Author(s):  
Guo Feng Wang ◽  
Wen Zhao ◽  
Yong Ping Guan ◽  
Lei Liang
Keyword(s):  
Neural Network ◽  
Maximum Error ◽  
Mining Area ◽  
Surrounding Rock ◽  
Rock Deformation ◽  
The Neural Network ◽  
Sublevel Caving ◽  
Iron Mine ◽  
Set Up ◽  
Surrounding Rock Deformation

The non-pillar sublevel caving method is used in Iron Mine in Banshi. In the mining area, there are many folds and faults, the inclination of ore body changes greatly, and ore and rock are fragmentized. The tunnel often collapsed and the surrounding rock deformation was getting large during the construction stage. Using the data of tunnel surrounding rock deformation, we adopt the neural network method to set up the mapping relation between the tunnel surrounding rock deformation and the project factors, including tunnel deepness, tunnel dimension, measuring time and surrounding rock quality. The analyzing results show that the maximum error between the forecast and the testing data is 13%, which indicates that this method is useful and feasible to the mining engineering. Key words: rock pressure; measure, deformation of the tunnel surrounding rock; neural network; data normalization; mapping

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