scholarly journals Remotely Sensed Imagery for Early Detection of Respiratory Disease in Pigs: A Pilot Study

Animals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 451 ◽  
Author(s):  
Maria Jorquera-Chavez ◽  
Sigfredo Fuentes ◽  
Frank R. Dunshea ◽  
Robyn D. Warner ◽  
Tomas Poblete ◽  
...  
Keyword(s):  
Computer Vision ◽  
Heart Rate ◽  
Pilot Study ◽  
Early Detection ◽  
Respiratory Diseases ◽  
Base Temperature ◽  
Computer Vision Technology ◽  
Thermal Infrared Imagery ◽  
Clinical Illness ◽  
The Impact

Respiratory diseases are a major problem in the pig industry worldwide. Due to the impact of these diseases, the early identification of infected herds is essential. Computer vision technology, using RGB (red, green and blue) and thermal infrared imagery, can assist the early detection of changes in animal physiology related to these and other diseases. This pilot study aimed to identify whether these techniques are a useful tool to detect early changes of eye and ear-base temperature, heart rate and respiration rate in pigs that were challenged with Actinobacillus pleuropneumoniae. Clinical observations and imagery were analysed, comparing data obtained from animals that showed some signs of illness with data from animals that showed no signs of ill health. Highly significant differences (p < 0.05) were observed between sick and healthy pigs in heart rate, eye and ear temperature, with higher heart rate and higher temperatures in sick pigs. The largest change in temperature and heart rate remotely measured was observed around 4–6 h before signs of clinical illness were observed by the skilled technicians. These data suggest that computer vision techniques could be a useful tool to detect indicators of disease before the symptoms can be observed by stock people, assisting the early detection and control of respiratory diseases in pigs, promoting further research to study the capability and possible uses of this technology for on farm monitoring and management.

scholarly journals A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases

2020 ◽  
Vol 17 (22) ◽  
pp. 8644 ◽  
Author(s):  
Alberto Comesaña-Campos ◽  
Manuel Casal-Guisande ◽  
Jorge Cerqueiro-Pequeño ◽  
José-Benito Bouza-Rodríguez
Keyword(s):  
Early Detection ◽  
Expert Systems ◽  
Respiratory Diseases ◽  
Treatment Options ◽  
Blood Oxygen Saturation ◽  
Physical Measurements ◽  
Inference Systems ◽  
The Impact ◽  
Early Detection And Prevention ◽  
Clinical Cases

Respiratory diseases are currently considered to be amongst the most frequent causes of death and disability worldwide, and even more so during the year 2020 because of the COVID-19 global pandemic. Aiming to reduce the impact of these diseases, in this work a methodology is developed that allows the early detection and prevention of potential hypoxemic clinical cases in patients vulnerable to respiratory diseases. Starting from the methodology proposed by the authors in a previous work and grounded in the definition of a set of expert systems, the methodology can generate alerts about the patient’s hypoxemic status by means of the interpretation and combination of data coming both from physical measurements and from the considerations of health professionals. A concurrent set of Mamdani-type fuzzy-logic inference systems allows the collecting and processing of information, thus determining a final alert associated with the measurement of the global hypoxemic risk. This new methodology has been tested experimentally, producing positive results so far from the viewpoint of time reduction in the detection of a blood oxygen saturation deficit condition, thus implicitly improving the consequent treatment options and reducing the potential adverse effects on the patient’s health.

scholarly journals Exercise Training in Patients with Chronic Respiratory Diseases: Are Cardiovascular Comorbidities and Outcomes Taken into Account?—A Systematic Review

2019 ◽  
Vol 8 (9) ◽  
pp. 1458 ◽  
Author(s):  
Ana Machado ◽  
Kirsten Quadflieg ◽  
Ana Oliveira ◽  
Charly Keytsman ◽  
Alda Marques ◽  
...  
Keyword(s):  
Systematic Review ◽  
Heart Rate ◽  
Exercise Training ◽  
Respiratory Diseases ◽  
Cardiovascular Outcomes ◽  
Chronic Obstructive ◽  
Resting Heart Rate ◽  
Cardiovascular Comorbidities ◽  
Exercise Programmes ◽  
The Impact

Patients with chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases (ILD) frequently suffer from cardiovascular comorbidities (CVC). Exercise training is a cornerstone intervention for the management of these conditions, however recommendations on tailoring programmes to patients suffering from respiratory diseases and CVC are scarce. This systematic review aimed to identify the eligibility criteria used to select patients with COPD, asthma or ILD and CVC to exercise programmes; assess the impact of exercise on cardiovascular outcomes; and identify how exercise programmes were tailored to CVC. PubMed, Scopus, Web of Science and Cochrane were searched. Three reviewers extracted the data and two reviewers independently assessed the quality of studies with the Quality Assessment Tool for Quantitative Studies. MetaXL 5.3 was used to calculate the individual and pooled effect sizes (ES). Most studies (58.9%) excluded patients with both stable and unstable CVC. In total, 26/42 studies reported cardiovascular outcomes. Resting heart rate was the most reported outcome measure (n = 13) and a small statistically significant effect (ES = −0.23) of exercise training on resting heart rate of patients with COPD was found. No specific adjustments to exercise prescription were described. Few studies have included patients with CVC. There was a lack of tailoring of exercise programmes and limited effects were found. Future studies should explore the effect of tailored exercise programmes on relevant outcome measures in respiratory patients with CVC.

scholarly journals Enhancing camera surveillance using computer vision: a research note

2018 ◽  
Vol 41 (2) ◽  
pp. 292-307 ◽  
Author(s):  
Haroon Idrees ◽  
Mubarak Shah ◽  
Ray Surette
Keyword(s):  
Computer Vision ◽  
Law Enforcement ◽  
Police Department ◽  
Law Enforcement Agencies ◽  
Ongoing Research ◽  
Content Type ◽  
Surveillance Camera ◽  
Surveillance Cameras ◽  
Computer Vision Technology ◽  
The Impact

Purpose The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues. Design/methodology/approach Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered. Findings Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field. Originality/value This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade.

scholarly journals Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1089 ◽  
Author(s):  
Maria Jorquera-Chavez ◽  
Sigfredo Fuentes ◽  
Frank R. Dunshea ◽  
Robyn D. Warner ◽  
Tomas Poblete ◽  
...  
Keyword(s):  
Computer Vision ◽  
Heart Rate ◽  
Respiration Rate ◽  
Core Body Temperature ◽  
Correlation Coefficients ◽  
Individual Characteristics ◽  
Farm Animals ◽  
Base Temperature ◽  
Livestock Farming ◽  
Eye Temperature

Precision livestock farming has emerged with the aim of providing detailed information to detect and reduce problems related to animal management. This study aimed to develop and validate computer vision techniques to track required features of cattle face and to remotely assess eye temperature, ear-base temperature, respiration rate, and heart rate in cattle. Ten dairy cows were recorded during six handling procedures across two consecutive days using thermal infrared cameras and RGB (red, green, blue) video cameras. Simultaneously, core body temperature, respiration rate and heart rate were measured using more conventional ‘invasive’ methods to be compared with the data obtained with the proposed algorithms. The feature tracking algorithm, developed to improve image processing, showed an accuracy between 92% and 95% when tracking different areas of the face of cows. The results of this study also show correlation coefficients up to 0.99 between temperature measures obtained invasively and those obtained remotely, with the highest values achieved when the analysis was performed within individual cows. In the case of respiration rate, a positive correlation (r = 0.87) was found between visual observations and the analysis of non-radiometric infrared videos. Low to high correlation coefficients were found between the heart rates (0.09–0.99) obtained from attached monitors and from the proposed method. Furthermore, camera location and the area analysed appear to have a relevant impact on the performance of the proposed techniques. This study shows positive outcomes from the proposed computer vision techniques when measuring physiological parameters. Further research is needed to automate and improve these techniques to measure physiological changes in farm animals considering their individual characteristics.

scholarly journals Impact of a Cold Environment on the Performance of Professional Cyclists: A Pilot Study

Life ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1326
Author(s):  
Florence Riera ◽  
Samuel Bellenoue ◽  
Simon Fischer ◽  
Henri Méric
Keyword(s):  
Heart Rate ◽  
Pilot Study ◽  
Ambient Temperature ◽  
Core Temperature ◽  
Environmental Conditions ◽  
Elevation Gradient ◽  
Body Core Temperature ◽  
Ambient Conditions ◽  
Performance Factors ◽  
The Impact

The practice of physical activity in a variable climate during the same competition is becoming more and more common due to climate change and increasingly frequent climate disturbances. The main aim of this pilot study was to understand the impact of cold ambient temperature on performance factors during a professional cycling race. Six professional athletes (age = 27 ± 2.7 years; height = 180.86 ± 5.81 cm; weight = 74.09 ± 9.11 kg; % fat mass = 8.01 ± 2.47%; maximum aerobic power (MAP) = 473 ± 26.28 W, undertook ~20 h training each week at the time of the study) participated in the Tour de la Provence under cold environmental conditions (the ambient temperature was 15.6 ± 1.4 °C with a relative humidity of 41 ± 8.5% and the normalized ambient temperature (Tawc) was 7.77 ± 2.04 °C). Body core temperature (Tco) was measured with an ingestible capsule. Heart rate (HR), power, speed, cadence and the elevation gradient were read from the cyclists’ onboard performance monitors. The interaction (multivariate analysis of variance) of the Tawc and the elevation gradient has a significant impact (F(1.5) = 32.2; p < 0.001) on the variables (cadence, power, velocity, core temperature, heart rate) and on each individual. Thus, this pilot study shows that in cold environmental conditions, the athlete’s performance was limited by weather parameters (ambient temperature associated with air velocity) and race characteristics. The interaction of Tawc and elevation gradient significantly influences thermal (Tco), physiological (HR) and performance (power, speed and cadence) factors. Therefore, it is advisable to develop warm-up, hydration and clothing strategies for competitive cycling under cold ambient conditions and to acclimatize to the cold by training in the same conditions to those that may be encountered in competition.

scholarly journals Portraying Footwear Customer Enter-Store Behavior in China: Based on Computer Vision Technology

2021 ◽  
Vol 21 (1) ◽  
pp. 3-12
Author(s):  
Jingjing LI ◽  
Keyu HOU ◽  
Wei XU ◽  
Jin ZHOU
Keyword(s):  
Computer Vision ◽  
Consumer Behavior ◽  
Data Collection ◽  
Conversion Rate ◽  
High Efficiency ◽  
Customer Behavior ◽  
Consumption Behavior ◽  
Computer Vision Technology ◽  
The Impact

Existing studies on customer behavior lack quantitative and high efficiency study, their technologies rely heavily on hardware. Therefore, the information of consumers in offline stores was insufficient, which made enterprises unable to accurately track consumers. However, computer vision (CV) is an expert in identifying and tracking people’s behavior, and its function is suitable for investigating enter-store customer behavior. Therefore, the aim of our study was to develop an offline consumer behavior portraying system based on CV. Then we used this system to investigate enter-store consumption behavior. We selected 71 shoe stores in China, then installed the system in store for a three-month data collection, and evaluated the impact of customer's age, gender, enter time, and region factors on enter-store behavior in China. Through our system, we successfully study ways to improve the purchase conversion rate of enter-store consumers, which could guide enterprises to adjust better marketing and operation strategies.

Pilot Study: The Impact of Yoga on Health

2007 ◽  
Author(s):  
Danielle V. Shelov ◽  
Sonia Suchday ◽  
Jennifer P. Friedberg
Keyword(s):  
Pilot Study ◽  
The Impact
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