RF Vital Sign Sensing under Free Body Movement

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Jian Gong ◽  
Xinyu Zhang ◽  
Kaixin Lin ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Vital Sign ◽  
Estimation Error ◽  
Body Movement ◽  
Vital Signs ◽  
Motion Artifacts ◽  
Body Parts ◽  
Training Scheme ◽  
Pattern Movement ◽  
Free Body ◽  
Average Estimation Error

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

Prototyping Sensor Network System for Automatic Vital Signs Collection

2013 ◽  
Vol 52 (03) ◽  
pp. 239-249 ◽  
Author(s):  
H. Noma ◽  
C. Naito ◽  
M. Tada ◽  
H. Yamanaka ◽  
T. Takemura ◽  
...  
Keyword(s):  
Sensor Network ◽  
Vital Sign ◽  
Paired Comparison ◽  
Estimation Error ◽  
Vital Signs ◽  
Position Estimation ◽  
University Hospital ◽  
Network System ◽  
Sensing System ◽  
Proximity Sensing

SummaryObjective: Development of a clinical sensor network system that automatically collects vital sign and its supplemental data, and evaluation the effect of automatic vital sensor value assignment to patients based on locations of sensors.Methods: The sensor network estimates the data-source, a target patient, from the position of a vital sign sensor obtained from a newly developed proximity sensing system. The proximity sensing system estimates the positions of the devices using a Bluetooth inquiry process. Using Bluetooth access points and the positioning system newly developed in this project, the sensor network collects vital sign and its 4W (who, where, what, and when) supplemental data from any Blue-tooth ready vital sign sensors such as Continua-ready devices. The prototype was evaluated in a pseudo clinical setting at Kyoto University Hospital using a cyclic paired comparison and statistical analysis.Results: The result of the cyclic paired analysis shows the subjects evaluated the proposed system is more effective and safer than POCS as well as paper-based operation. It halves the times for vital signs input and eliminates input errors. On the other hand, the prototype failed in its position estimation for 12.6% of all attempts, and the nurses overlooked half of the errors. A detailed investigation clears that an advanced interface to show the system’s “confidence”, i.e. the probability of estimation error, must be effective to reduce the oversights.Conclusions: This paper proposed a clinical sensor network system that relieves nurses from vital signs input tasks. The result clearly shows that the proposed system increases the efficiency and safety of the nursing process both subjectively and objectively. It is a step toward new generation of point of nursing care systems where sensors take over the tasks of data input from the nurses.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

scholarly journals Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor

2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Piezoelectric Ceramic ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
High Heart Rate ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Real Time Monitoring

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

scholarly journals The association between abnormal vital sign groups and undesirable patient outcomes

2018 ◽  
Vol 25 (3) ◽  
pp. 137-145
Author(s):  
Marina Lee ◽  
David McD Taylor ◽  
Antony Ugoni
Keyword(s):  
Emergency Department ◽  
Confidence Interval ◽  
Patient Outcomes ◽  
Vital Sign ◽  
Odds Ratio ◽  
Medical Emergency Team ◽  
Vital Signs ◽  
Medical Emergency ◽  
Emergency Team ◽  
Abnormal Vital Sign

Introduction: To determine the association between both abnormal individual vital signs and abnormal vital sign groups in the emergency department, and undesirable patient outcomes: hospital admission, medical emergency team calls and death. Method: We undertook a prospective cohort study in a tertiary referral emergency department (February–May 2015). Vital signs were collected prospectively in the emergency department and undesirable outcomes from the medical records. The primary outcomes were undesirable outcomes for individual vital signs (multivariate logistic regression) and vital sign groups (univariate analyses). Results: Data from 1438 patients were analysed. Admission was associated with tachycardia, tachypnoea, fever, ≥1 abnormal vital sign on admission to the emergency department, ≥1 abnormal vital sign at any time in the emergency department, a persistently abnormal vital sign, and vital signs consistent with both sepsis (tachycardia/hypotension/abnormal temperature) and pneumonia (tachypnoea/fever) (p < 0.05). Medical emergency team calls were associated with tachycardia, tachypnoea, ≥1 abnormal vital sign on admission (odds ratio: 2.3, 95% confidence interval: 1.4–3.8), ≥2 abnormal vital signs at any time (odds ratio: 2.4, 95% confidence interval: 1.2–4.7), and a persistently abnormal vital sign (odds ratio: 2.7, 95% confidence interval: 1.6–4.6). Death was associated with Glasgow Coma Score ≤13 (odds ratio: 6.3, 95% confidence interval: 2.5–16.0), ≥1 abnormal vital sign on admission (odds ratio: 2.6, 95% confidence interval: 1.2–5.6), ≥2 abnormal vital signs at any time (odds ratio: 6.4, 95% confidence interval: 1.4–29.5), a persistently abnormal vital sign (odds ratio: 4.3, 95% confidence interval: 2.0–9.0), and vital signs consistent with pneumonia (odds ratio: 5.3, 95% confidence interval: 1.9–14.8). Conclusion: Abnormal vital sign groups are generally superior to individual vital signs in predicting undesirable outcomes. They could inform best practice management, emergency department disposition, and communication with the patient and family.

GET: A Function for Preferential Site Selection of Additional Borehole Drilling

2004 ◽  
Vol 13 (1-4) ◽  
pp. 139-146 ◽  
Author(s):  
A.A. HASSANIPAK ◽  
M. SHARAFODIN
Keyword(s):  
Site Selection ◽  
Estimation Error ◽  
Central Iran ◽  
Vertical Column ◽  
Preferential Site ◽  
Borehole Drilling ◽  
Average Estimation Error ◽  
Linear Product ◽  
Selection Of

Abstract The essential aims of additional borehole drilling are to improve the reliability of grade and tonnage estimates in each reserve class and to increase ore tonnages. The “GET” function presented in this paper considers strategies for achieving both of these goals simultaneously, and therefore is advantageous for selecting sites for additional boreholes. The “GET” function is either a linear or a non-linear product of three variables G, E, and T: f(G,E,T,) = GαEβTγ where the values of any or all of the exponents α, β, and γ may differ from unity at the discretion of the user. G and E are the average estimated block grade and the average estimation error for ore blocks in one vertical column, and T is the compounded ore thickness within the column. To illustrate its utility, the GET function has been used for determination of the most advantageous sites for additional drilling in the Shah-Kuh Pb-Zn deposit in west central Iran.

Advance Monitoring of Blood Pressure and Respiratory Rate Using De-Noising Auto Encoder

2021 ◽  
Author(s):  
Seung-Ho Park ◽  
Kyoung-Su Park
Keyword(s):  
Blood Pressure ◽  
Respiratory Rate ◽  
Vital Sign ◽  
Superficial Temporal Artery ◽  
Vital Signs ◽  
Pulse Signal ◽  
Temporal Artery ◽  
Sinus Arrhythmia ◽  
Vital Signs Monitoring ◽  
Vital Sign Measurement

Abstract As the importance of continuous vital signs monitoring increases, the need for wearable devices to measure vital sign is increasing. In this study, the device is designed to measure blood pressure (BP), respiratory rate (RR), and heartrate (HR) with one sensor. The device is in earphone format and is manufactured as wireless type using Arduino-based bluetooth module. The device measures pulse signal in the Superficial temporal artery using Photoplethysmograghy (PPG) sensor. The device uses the Auto Encoder to remove noise caused by movement, etc., contained in the pulse signal. Extract the feature from the pulse signal and use them for the vital sign measurement. The device is measured using Slope transit time (STT) method for BP and Respiratory sinus arrhythmia (RSA) method for RR. Finally, the accuracy is determined by comparing the vital signs measured through the device with the reference vital signs measured simultaneously.

Review: Continuous Monitoring to Detect Failure to Rescue in Adult Postoperative Inpatients

2018 ◽  
Vol 52 (4) ◽  
pp. 281-287 ◽  
Author(s):  
Sue Carol Verrillo ◽  
Bradford D. Winters
Keyword(s):  
Vital Sign ◽  
Sleep Disordered Breathing ◽  
Continuous Monitoring ◽  
Vital Signs ◽  
Clinical Deterioration ◽  
Failure To Rescue ◽  
Contributing Factors ◽  
Unexpected Death ◽  
Practice Model ◽  
Preventable Complication

Abstract Failure to rescue, or the unexpected death of a patient due to a preventable complication, is a nationally documented problem with numerous and multifaceted contributing factors. These factors include the frequency and method of collecting vital sign data, response to abnormal vital signs, and delays in the escalation of care for general ward patients who are showing signs of clinical deterioration. Patients' clinical deterioration can be complicated by concurrent secondary factors, including opioid abuse/dependence, being uninsured, or having sleep-disordered breathing. Using the Johns Hopkins Nursing Evidence-Based Practice Model, this integrative review synthesizes 43 research and nonresearch sources of evidence. Published between 2001 and 2017, these sources of evidence focus on failure to rescue, the multifaceted contributing factors to failure to rescue, and how continuous vital sign monitoring could ameliorate failure to rescue and its causes. Recommendations from the sources of evidence have been divided into system, structural, or technological categories.

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