A comparison of uniaxial and triaxial accelerometers for the assessment of physical activity in dogs

Cheung, Kitty W.; Starling, Melissa J.; McGreevy, Paul D.

doi:10.1016/j.jveb.2013.11.003

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Unlike a triaxial accelerometer, a uniaxial accelerometer could only detect vibration in one direction. However, triaxial accelerometers are more expensive than uniaxial accelerometers, but their memory capacity is greater [27]. Due to its dependability, simplicity, and resilience, an accelerometer is an extensively used sensor.…”

Section: Sensormentioning

confidence: 99%

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Dubaish,

Jaber

2023

ETJ

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Section: Sensormentioning

confidence: 99%

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Dubaish,

Jaber

2023

ETJ

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“…Vibration sensors are often permanently fixed at a particular location in the machine to allow for continuous or online monitoring of the machine's status. The sensor is fastened to the machine by being fitted into a stud [11][12][13]. In comparison to other mounting techniques, this one not only has the largest frequency response but is also extremely trustworthy and safe.…”

Section: Sensorsmentioning

confidence: 99%

Machine Learning Motor Vibration Monitoring System with a Service Estimation Date

Ibrahim,

Ilyas,

et al. 2023

Advances in Transdisciplinary Engineering

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This paper presents machine learning motor vibration with service estimation date. AI and machine learning algorithms are used to evaluate electric motor vibration patterns and predict maintenance and repair needs. Efficiency, downtime, and maintenance and repair schedule optimisation are project goals. Over time, machine learning algorithms analyse electric motor data to identify vibration patterns. This will predict maintenance and repair needs. KNN, CatBoost, and Neural Network were studied. Machine learning algorithms predicted maintenance and repair needs with over 90% accuracy. Algorithms also calculated the service estimation date, improving maintenance and repair scheduling. It improved maintenance and repair programmes, reduced downtime, and increased reliability. An ESP8266 and a vibration sensor to record and send electric motor data to ThingsSpeak, an IoT website that analyses it. Machine learning algorithms is then used to classify the motor vibration to determine the service estimation date. This project taught me how to maintain and repair electric motors using machine learning and AI algorithms.

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“…Over the past decade, researchers have started to experiment with their suitability for animal populations, including domestic dogs ( Canis familiaris ). Particular focus has been given to measuring Physical Activity as a proxy for dysfunction or disease [ 6 – 10 ]. Research grade devices such as such as the Actical (Philips Respironics, Netherlands) and GT3X (Actigraph Inc., USA) (used in [ 11 , 12 ] respectively) are attractive as they have software that facilitates data export in formats suitable for post-processing.…”

Section: Introductionmentioning

confidence: 99%

A step in the right direction: an open-design pedometer algorithm for dogs

et al. 2018

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BackgroundAccelerometer-based technologies could be useful in providing objective measures of canine ambulation, but most are either not tailored to the idiosyncrasies of canine gait, or, use un-validated or closed source approaches. The aim of this paper was to validate algorithms which could be applied to accelerometer data for i) counting the number of steps and ii) distance travelled by a dog.To count steps, an approach based on partitioning acceleration was used. This was applied to accelerometer data from 13 dogs which were walked a set distance and filmed. Each footfall captured on video was annotated. In a second experiment, an approach based on signal features was used to estimate distance travelled. This was applied to accelerometer data from 10 dogs with osteoarthritis during normal walks with their owners where GPS (Global Positioning System) was also captured. Pearson’s correlations and Bland Altman statistics were used to compare i) the number of steps measured on video footage and predicted by the algorithm and ii) the distance travelled estimated by GPS and predicted by the algorithm.ResultsBoth step count and distance travelled could be estimated accurately by the algorithms presented in this paper: 4695 steps were annotated from the video and the pedometer was able to detect 91%. GPS logged a total of 20,184 m meters across all dogs; the mean difference between the predicted and GPS estimated walk length was 211 m and the mean similarity was 79%.ConclusionsThe algorithms described show promise in detecting number of steps and distance travelled from an accelerometer. The approach for detecting steps might be advantageous to methods which estimate gross activity because these include energy output from stationary activities. The approach for estimating distance might be suited to replacing GPS in indoor environments or others with limited satellite signal. The algorithms also allow for temporal and spatial components of ambulation to be calculated. Temporal and spatial aspects of dog ambulation are clinical indicators which could be used for diagnosis or monitoring of certain diseases, or used to provide information in support of canine weight-loss programmes.

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A comparison of uniaxial and triaxial accelerometers for the assessment of physical activity in dogs

Cited by 10 publications

References 21 publications

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Machine Learning Motor Vibration Monitoring System with a Service Estimation Date

A step in the right direction: an open-design pedometer algorithm for dogs

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