What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

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Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Sensors ◽

10.3390/s22010174 ◽

2021 ◽

Vol 22 (1) ◽

pp. 174

Author(s):

Junhyuk Kang ◽

Jieun Shin ◽

Jaewon Shin ◽

Daeho Lee ◽

Ahyoung Choi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Input Signal ◽

Short Term Memory ◽

Learning Model ◽

Sensor Data ◽

Accelerometer Data ◽

Accelerometer Sensor ◽

Skeleton Image ◽

Deep Learning Model

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

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Motion Type Verification Studies Using Accelerometer Sensor Data With Local Mean Decomposition

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.117 ◽

2019 ◽

Vol 2 (3) ◽

pp. 1051-1057

Author(s):

Mustafa Yasin Esas ◽

Fatma Latifoğlu

Keyword(s):

Human Physiology ◽

Cell Phone ◽

High Accuracy ◽

Sensor Data ◽

Accelerometer Data ◽

Accelerometer Sensor ◽

Local Mean Decomposition ◽

A Cell ◽

Local Mean ◽

High Degree

It is a significant improvement that the physical movements directly related to human physiology can be detected with high accuracy using sensors. In our study, three-axis accelerometer data recorded using a cell phone sensor in a controlled manner were used. Validation of walking, jogging, up-stairs, down-stair movements is aimed. For this purpose, local mean decomposition (LMD) function was used. The axis (x, y, z) in which the orthogonality value obtained from LMD was high was determined. Then, it was evaluated that there is movement in the direction of high value axis. While there is a high degree of accuracy in up-stair, down-stair and jogging movements, the desired success in walking movement was not achieved.

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Human Movement Monitoring and Analysis for Prehabilitation Process Management

Journal of Sensor and Actuator Networks ◽

10.3390/jsan9010009 ◽

2020 ◽

Vol 9 (1) ◽

pp. 9

Author(s):

Khalid Al-Naime ◽

Adnan Al-Anbuky ◽

Grant Mawston

Keyword(s):

Fourier Analysis ◽

High Intensity ◽

Hospital Setting ◽

Human Movement ◽

Dominant Frequency ◽

Sensor Data ◽

Moderate Intensity ◽

Activity Levels ◽

Accelerometer Data ◽

Accelerometer Sensor

Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity.

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Оrganic Meat Production of Broiler Chickens Hubbard Redbro Cross

International Journal of Veterinary Science ◽

10.37422/ijvs/20.072 ◽

2020 ◽

Keyword(s):

Broiler Chickens ◽

Raw Materials ◽

Animal Health ◽

Meat Products ◽

Poultry Meat ◽

Meat Production ◽

Significant Difference ◽

Environmentally Safe ◽

The Impact

This article presents the results of studying the impact of housing and feeding conditions on broiler chickens of Hubbard RedBro cross, as well as the quality of products obtained when using floor and cage content, in a farm. It established that when receiving a mixed feed of own production using feed raw materials grown on a farm without the use of pesticides, a statistically significant decrease in potentially dangerous substances for animal health is recorded. Compared with factory feed, it has reduced the content of pesticides by 14 times, and mercury and arsenic by 24 times, cadmium by five times, and lead by ten times. The results of the study of economic indicators of growing Hubbard RedBro cross broiler chickens, as well as the chemical composition and quality of carcasses, indicated that there was no significant difference between the floor and cell conditions of keeping. Still, the use of a diet based on eco-feeds contributed to a statistically significant decrease in the concentration of toxic metals in the muscles of the poultry of the experimental groups. As a result, it found that the use of the studied compound feed in the diets of broiler chickens increased the indicators of Biosafety and ensured the production of environmentally safe ("organic") poultry meat products.

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BOARD INVITED REVIEW: The pig microbiota and the potential for harnessing the power of the microbiome to improve growth and health1

Journal of Animal Science ◽

10.1093/jas/skz208 ◽

2019 ◽

Vol 97 (9) ◽

pp. 3741-3757 ◽

Cited By ~ 8

Author(s):

Nirosh D Aluthge ◽

Dana M Van Sambeek ◽

Erin E Carney-Hinkle ◽

Yanshuo S Li ◽

Samodha C Fernando ◽

...

Keyword(s):

Gastrointestinal Tract ◽

Gut Microbiota ◽

Therapeutic Potential ◽

Animal Health ◽

Microbial Colonization ◽

Critical Importance ◽

Specific Host ◽

Microbiome Research ◽

Health And Disease ◽

The Impact

Abstract A variety of microorganisms inhabit the gastrointestinal tract of animals including bacteria, archaea, fungi, protozoa, and viruses. Pioneers in gut microbiology have stressed the critical importance of diet:microbe interactions and how these interactions may contribute to health status. As scientists have overcome the limitations of culture-based microbiology, the importance of these interactions has become more clear even to the extent that the gut microbiota has emerged as an important immunologic and metabolic organ. Recent advances in metagenomics and metabolomics have helped scientists to demonstrate that interactions among the diet, the gut microbiota, and the host to have profound effects on animal health and disease. However, although scientists have now accumulated a great deal of data with respect to what organisms comprise the gastrointestinal landscape, there is a need to look more closely at causative effects of the microbiome. The objective of this review is intended to provide: 1) a review of what is currently known with respect to the dynamics of microbial colonization of the porcine gastrointestinal tract; 2) a review of the impact of nutrient:microbe effects on growth and health; 3) examples of the therapeutic potential of prebiotics, probiotics, and synbiotics; and 4) a discussion about what the future holds with respect to microbiome research opportunities and challenges. Taken together, by considering what is currently known in the four aforementioned areas, our overarching goal is to set the stage for narrowing the path towards discovering how the porcine gut microbiota (individually and collectively) may affect specific host phenotypes.

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Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Digital Biomarkers ◽

10.1159/000516619 ◽

2021 ◽

pp. 158-166

Author(s):

Noah Balestra ◽

Gaurav Sharma ◽

Linda M. Riek ◽

Ania Busza

Keyword(s):

Machine Learning ◽

Upper Extremity ◽

Sensor Data ◽

Inpatient Setting ◽

Accelerometer Data ◽

Data Set ◽

Machine Learning Classification ◽

Exercise Type ◽

Exercise Dose ◽

Rehabilitation Exercises

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objectives: The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results: We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

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Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

Movement Ecology ◽

10.1186/s40462-021-00243-z ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Melinda G. Conners ◽

Théo Michelot ◽

Eleanor I. Heywood ◽

Rachael A. Orben ◽

Richard A. Phillips ◽

...

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Flapping Flight ◽

Sensor Data ◽

Accelerometer Data ◽

Fine Scale ◽

Magnetometer Data ◽

Behavioral Classification ◽

Soaring Flight

AbstractBackgroundInertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.MethodsWe deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.ResultsHMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.ConclusionsThe use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

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WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks

Sensors ◽

10.3390/s21020539 ◽

2021 ◽

Vol 21 (2) ◽

pp. 539

Author(s):

Saleh Seyedzadeh ◽

Andrew Agapiou ◽

Majid Moghaddasi ◽

Milan Dado ◽

Ivan Glesk

Keyword(s):

Optical Networks ◽

Video Surveillance ◽

Multiple Access ◽

Optical Sensing ◽

Multiple Access Interference ◽

Service Differentiation ◽

Sensor Data ◽

Variable Weight ◽

Code Division Multiple ◽

The Impact

The growing demand for extensive and reliable structural health monitoring resulted in the development of advanced optical sensing systems (OSS) that in conjunction with wireless optical networks (WON) are capable of extending the reach of optical sensing to places where fibre provision is not feasible. To support this effort, the paper proposes a new type of a variable weight code called multiweight zero cross-correlation (MW-ZCC) code for its application in wireless optical networks based optical code division multiple access (WON-OCDMA). The code provides improved quality of service (QoS) and better support for simultaneous transmission of video surveillance, comms and sensor data by reducing the impact of multiple access interference (MAI). The MW-ZCC code’s power of two code-weight properties provide enhanced support for the needed service differentiation provisioning. The performance of this novel code has been studied by simulations. This investigation revealed that for a minimum allowable bit error rate of 10−3, 10−9 and 10−12 when supporting triple-play services (sensing, datacomms and video surveillance, respectively), the proposed WON-OCDMA using MW-ZCC codes could support up to 32 simultaneous services over transmission distances up to 32 km in the presence of moderate atmospheric turbulence.

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Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

Scientific Data ◽

10.1038/s41597-021-00831-z ◽

2021 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Gloria Vergara-Diaz ◽

Jean-Francois Daneault ◽

Federico Parisi ◽

Chen Admati ◽

Christina Alfonso ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Symptom Severity ◽

Wearable Sensors ◽

Motor Fluctuations ◽

Sensor Data ◽

Accelerometer Data ◽

Motor Tasks ◽

Specific Symptom ◽

At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

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The RUMA Alliance targets: an overview

Livestock ◽

10.12968/live.2021.26.4.176 ◽

2021 ◽

Vol 26 (4) ◽

pp. 176-179

Author(s):

Chris Lloyd

Keyword(s):

Task Force ◽

Animal Health ◽

Antibiotic Use ◽

Codes Of Practice ◽

The Government ◽

The Uk ◽

Ongoing Support ◽

The Impact ◽

Insight Into ◽

Roll Out

The Responsible Use of Medicines in Agriculture Alliance (RUMA) was established to promote the highest standards of food safety, animal health and animal welfare in the British livestock industry. It has a current focus to deliver on the Government objective of identifying sector-specific targets for the reduction, refinement or replacement of antibiotics in animal agriculture. The creation and roll out of sector specific targets in 2017 through the RUMA Targets Task Force, has helped focus activity across the UK livestock sectors to achieve a 50% reduction in antibiotic use since 2014. This has been realised principally through voluntary multi-sector collaboration, cross sector initiatives, codes of practice, industry body support and farm assurance schemes. This article provides an overview of RUMA's work to date providing insight into the methods used to create the targets, why they are so important, the impact they are having and how ongoing support and robust data are vital components in achieving the latest set of targets.

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