Doppler Vital Signs Detection in the Presence of Large-Scale Random Body Movements

2018 ◽  
Vol 66 (9) ◽  
pp. 4261-4270 ◽  
Author(s):  
Qinyi Lv ◽  
Lei Chen ◽  
Kang An ◽  
Jun Wang ◽  
Huan Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Vital Signs ◽  
Body Movements

scholarly journals Humans use minimum cost movements in a whole-body task

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lijia Liu ◽  
Dana Ballard
Keyword(s):  
Dynamic Model ◽  
Tool Use ◽  
Large Scale ◽  
Minimum Cost ◽  
Whole Body ◽  
Energetic Cost ◽  
Body Movements ◽  
Closed Curves ◽  
General Movements ◽  
Musculoskeletal Systems

AbstractHumans have elegant bodies that allow gymnastics, piano playing, and tool use, but understanding how they do this in detail is difficult because their musculoskeletal systems are extraordinarily complicated. Nonetheless, common movements like walking and reaching can be stereotypical, and a very large number of studies have shown their energetic cost to be a major factor. In contrast, one might think that general movements are very individuated and intractable, but our previous study has shown that in an arbitrary set of whole-body movements used to trace large-scale closed curves, near-identical posture sequences were chosen across different subjects, both in the average trajectories of the body’s limbs and in the variance within trajectories. The commonalities in that result motivate explanations for its generality. One explanation could be that humans also choose trajectories that are economical in cost. To test this hypothesis, we situate the tracing data within a forty eight degree of freedom human dynamic model that allows the computation of movement cost. Using the model to compare movement cost data from nominal tracings against various perturbed tracings shows that the latter are more energetically expensive, inferring that the original traces were chosen on the basis of minimum cost.

Large-Scale Sensor Network Analysis

2013 ◽  
pp. 314-347 ◽  
Author(s):  
Joaquin Vanschoren ◽  
Ugo Vespier ◽  
Shengfa Miao ◽  
Marvin Meeng ◽  
Ricardo Cachucho ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Vital Signs ◽  
Sensor Data ◽  
Atmospheric Conditions ◽  
Big Data Applications ◽  
The World ◽  
Sheer Size ◽  
Effective Use

Sensors are increasingly being used to monitor the world around us. They measure movements of structures such as bridges, windmills, and plane wings, human’s vital signs, atmospheric conditions, and fluctuations in power and water networks. In many cases, this results in large networks with different types of sensors, generating impressive amounts of data. As the volume and complexity of data increases, their effective use becomes more challenging, and novel solutions are needed both on a technical as well as a scientific level. Founded on several real-world applications, this chapter discusses the challenges involved in large-scale sensor data analysis and describes practical solutions to address them. Due to the sheer size of the data and the large amount of computation involved, these are clearly “Big Data” applications.

scholarly journals Cyclostationary-Based Vital Signs Detection Using Microwave Radar at 2.5 GHz

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3396
Author(s):  
Fatima Sekak ◽  
Kawtar Zerhouni ◽  
Fouzia Elbahhar ◽  
Madjid Haddad ◽  
Christophe Loyez ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Vital Signs ◽  
The Body ◽  
Contact Detection ◽  
Body Movements ◽  
Non Invasive ◽  
Microwave Radar ◽  
Reflected Signal ◽  
The Impact ◽  
Invasive Measurement

Non-contact detection and estimation of vital signs such as respiratory and cardiac frequencies is a powerful tool for surveillance applications. In particular, the continuous wave bio-radar has been widely investigated to determine the physiological parameters in a non-contact manner. Since the RF-reflected signal from the human body is corrupted by noise and random body movements, traditional Fourier analysis fails to detect the heart and breathing frequencies. In this effort, cyclostationary analysis has been used to improve the radar performance for non-invasive measurement of respiratory rate and heart rate. However, the preliminary works focus only on one frequency and do not include the impact of attenuation and random movement of the body in the analysis. Hence in this paper, we evaluate the impact of distance and noise on the cyclic features of the reflected signal. Furthermore, we explore the assessment of second order cyclostationary signal processing performance by developing the cyclic mean, the conjugate cyclic autocorrelation and the cyclic cumulant. In addition, the analysis is carried out using a reduced number of samples to reduce the response time. Implementation of the cyclostationary technique using a bi-static radar configuration at 2.5 GHz is shown as an example to demonstrate the proposed approach.

scholarly journals Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4183 ◽  
Author(s):  
Zi-Kai Yang ◽  
Heping Shi ◽  
Sheng Zhao ◽  
Xiang-Dong Huang
Keyword(s):  
Vital Sign ◽  
Large Scale ◽  
Doppler Radar ◽  
Noise Cancellation ◽  
Home Healthcare ◽  
Adaptive Noise Cancellation ◽  
Body Movements ◽  
Radar Sensor ◽  
Sign Detection ◽  
Adaptive Noise

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

Objective estimation of pain based on facial expression, vital signs and body movements

2000 ◽  
Vol 14 (5) ◽  
pp. 413-414 ◽  
Author(s):  
Taketoshi Mori ◽  
Yukiko Okazaki ◽  
Tomoya Kawai ◽  
Tomomasa Sato
Keyword(s):  
Facial Expression ◽  
Vital Signs ◽  
Body Movements

scholarly journals Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5503
Author(s):  
Xinyue Zhang ◽  
Xiuzhu Yang ◽  
Yi Ding ◽  
Yili Wang ◽  
Jialin Zhou ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vital Signs ◽  
Motion Artifacts ◽  
Body Motion ◽  
Ultra Wideband ◽  
Body Movements ◽  
Range Resolution ◽  
Vital Signs Monitoring ◽  
Uwb Radar ◽  
Empirical Wavelet Transform

Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest–abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.

scholarly journals A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications

2017 ◽  
Author(s):  
Giovanni Diraco ◽  
Alessandro Leone ◽  
Pietro Siciliano
Keyword(s):  
Technology Acceptance ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Daily Activities ◽  
Smart Sensor ◽  
Body Movements ◽  
Ambient Lighting ◽  
Wideband Radar

Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

scholarly journals Comparing Classroom Instruction to Individual Instruction as an Approach to Teach Avatar-Based Patient Monitoring With Visual Patient: Simulation Study (Preprint)

2020 ◽  
Author(s):  
Julian Rössler ◽  
Alexander Kaserer ◽  
Benjamin Albiez ◽  
Julia Braun ◽  
Jan Breckwoldt ◽  
...  
Keyword(s):  
Large Scale ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Care Providers ◽  
Instruction Group ◽  
Anesthesia Providers ◽  
Individual Instruction ◽  
Significant Difference ◽  
Historical Sample ◽  
Class Instruction

BACKGROUND Visual Patient is an avatar-based alternative to standard patient monitor displays that significantly improves the perception of vital signs. Implementation of this technology in larger organizations would require it to be teachable by brief class instruction to large groups of professionals. Therefore, our study aimed to investigate the efficacy of such a large-scale introduction to Visual Patient. OBJECTIVE In this study, we aimed to compare 2 different educational methods, one-on-one instruction and class instruction, for training anesthesia providers in avatar-based patient monitoring. METHODS We presented 42 anesthesia providers with 30 minutes of class instruction on Visual Patient (class instruction group). We further selected a historical sample of 16 participants from a previous study who each received individual instruction (individual instruction group). After the instruction, the participants were shown monitors with either conventional displays or Visual Patient displays and were asked to interpret vital signs. In the class instruction group, the participants were shown scenarios for either 3 or 10 seconds, and the numbers of correct perceptions with each technology were compared. Then, the teaching efficacy of the class instruction was compared with that of the individual instruction in the historical sample by 2-way mixed analysis of variance and mixed regression. RESULTS In the class instruction group, when participants were presented with the 3-second scenario, there was a statistically significant median increase in the number of perceived vital signs when the participants were shown the Visual Patient compared to when they were shown the conventional display (3 vital signs, <i>P</i>&lt;.001; effect size –0.55). No significant difference was found for the 10-second scenarios. There was a statistically significant interaction between the teaching intervention and display technology in the number of perceived vital signs (<i>P</i>=.04; partial η<sup>2</sup>=.076). The mixed logistic regression model for correct vital sign perception yielded an odds ratio (OR) of 1.88 (95% CI 1.41-2.52; <i>P</i>&lt;.001) for individual instruction compared to class instruction as well as an OR of 3.03 (95% CI 2.50-3.70; <i>P</i>&lt;.001) for the Visual Patient compared to conventional monitoring. CONCLUSIONS Although individual instruction on Visual Patient is slightly more effective, class instruction is a viable teaching method; thus, large-scale introduction of health care providers to this novel technology is feasible.

scholarly journals Predicting Fall Risk Through Automatic Wearable Monitoring

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Markey Olson ◽  
Thurmon Lockhart
Keyword(s):  
Machine Learning ◽  
Fall Risk ◽  
Large Scale ◽  
Meta Analysis ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Step Length ◽  
Free Living ◽  
Learning Methods ◽  
Machine Learning Methods

Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.

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