scholarly journals IoT-based measurement system for classifying cow behavior from tri-axial accelerometer

2019 ◽  
Vol 49 (6) ◽  
Author(s):  
Jun Wang ◽  
Zhitao He ◽  
Jiangtao Ji ◽  
Kaixuan Zhao ◽  
Haiyang Zhang
Keyword(s):  
Measurement System ◽  
Classification Algorithm ◽  
Ensemble Classification ◽  
Movement Behavior ◽  
Future Research ◽  
Behavior Patterns ◽  
Remote Measurement ◽  
Adaptive Boosting ◽  
Acceleration Data ◽  
Rf Module

ABSTRACT: A cow behavior monitoring system based on the Internet of Things (IoT) has been designed and implemented using tri-axial accelerometer, MSP430 microcontroller, wireless radio frequency (RF) module, and a laptop. The implemented system measured cow movement behavior and transmitted acceleration data to the laptop through the wireless RF module. Results were displayed on the laptop in a 2D graph, through which behavior patterns of cows were predicted. The measured data from the system were analyzed using the Multi-Back Propagation-Adaptive Boosting algorithm to determine the specific behavioral state of cows. The developed system can be used to increase classification performance of cow behavior by detecting acceleration data. Accuracy exceeded 90% for all the classified behavior categories, and the specificity of normal walking reached 96.98%. The sensitivity was good for all behavior patterns except standing up and lying down, with a maximum of 87.23% for standing. Overall, the IoT-based measurement system provides accurate and remote measurement of cow behavior, and the ensemble classification algorithm can effectively recognize various behavior patterns in dairy cows. Future research will improve the classification algorithm parameters and increase the number of enrolled cows. Once the functionality and reliability of the system have been confirmed on a large scale, commercialization may become possible.

Development of a Wireless Measurement System for Classifying Cow Behavior Using Accelerometer Data and Location Data

2019 ◽  
Vol 35 (2) ◽  
pp. 135-147
Author(s):  
Jun Wang ◽  
Haiyang Zhang ◽  
Jiangtao Ji ◽  
Kaixuan Zhao ◽  
Gang Liu
Keyword(s):  
Dairy Cows ◽  
Measurement System ◽  
Evidence Theory ◽  
Behavior Patterns ◽  
Laptop Computer ◽  
Fcm Algorithm ◽  
Location Data ◽  
Acceleration Data ◽  
Standing Up ◽  
Wireless Measurement

Abstract. A wireless measurement system for assessing behavior patterns in dairy cows has been designed and constructed. The system mainly consisted of 12 leg-tags, 6 location sensors, and a laptop computer. Twelve lactating Holstein dairy cows were selected in the trial. The leg-tags used a 433 MHz radio channel for transmitting the acceleration data and location data at 1 Hz. The data were logged to a laptop computer in real time. An ensemble classification algorithm was proposed in our study. The algorithm can be divided into two stages: at the first stage, the Semi-Supervised Fuzzy C-Means (SS-FCM) algorithm was used to interpret the acceleration data and classify all classes of behavior. Classified behavior patterns included feeding, lying, standing, lying down, standing up, normal walking, and active walking. Accuracy, sensitivity, and precision were used as statistical parameters of classification performance. The SS-FCM algorithm achieved a reasonable identification level of feeding (80% accuracy, 53% sensitivity, 56% precision), lying (92%, 96%, 89%), standing (90%, 51%, 58%), lying down (99%, 82%, 91%), standing up (99%, 66%, 78%), normal walking (97%, 95%, 86%), and active walking (99%, 95%, 88%). At the second stage, a D-S evidence theory method fused the result of SS-FCM algorithm and the cow position to classify all the behaviors predicted as feeding or standing at the previous stage. The sensitivity and precision of the two behaviors increased by an average of 17% and 16.5%, respectively. Overall, we found that the wireless measurement system provided an accurate, remote measure for cow behavior over the trial period, and the ensemble algorithm could effectively recognize various behavior patterns in dairy cows. Keywords: Behavior classification, Back propagation, D-S evidence theory, Received signal strength indication, Three-dimensional accelerometer.

Sleep and Cannabis

2021 ◽  
pp. 257-276
Author(s):  
Param Dedhia ◽  
Alison Kole
Keyword(s):  
Chronic Pain ◽  
Obstructive Sleep Apnea ◽  
Circadian Rhythm ◽  
Behavior Disorder ◽  
Early Research ◽  
Movement Behavior ◽  
Future Research ◽  
Medicinal Use ◽  
Obstructive Sleep ◽  
Medicinal Cannabis

With the rising interest in cannabis, the concept of medicinal cannabis for sleep has quickly grown. The chemistry of cannabis and its medicinal effects are becoming known. Within sleep science, cannabis research has noted a connection to circadian rhythm and sleep staging. Early research has looked at insomnia, obstructive sleep apnea, restless leg syndrome, rapid eye movement behavior disorder, posttraumatic stress disorder, and chronic pain. At this time, there is encouragement for future research based on available studies. However, rigorous studies are needed before the medicinal use of cannabis for sleep can be supported by medical literature. Nevertheless, cannabis is being self-administered, and patients are looking for education. The healthcare provider has a unique opportunity to partner with patients through education and guidance, making it important for medical experts to learn about cannabis.

scholarly journals Performance measurement systems in auditing firms

2015 ◽  
Vol 5 (4) ◽  
pp. 395-423 ◽  
Author(s):  
Mohamed Hegazy ◽  
Myada Tawfik
Keyword(s):  
Performance Measurement ◽  
Performance Measures ◽  
Measurement System ◽  
Performance Measurement System ◽  
Future Research ◽  
High Quality ◽  
Content Type ◽  
Financial Measures ◽  
Measurement Systems ◽  
Performance Measurement Systems

Purpose – The purpose of this paper is to investigate challenges facing auditing firms in designing and measuring their performance and discusses why and how the balance scorecard (BSC) could support the auditing firms overcome such challenges. The paper contributes to the existing literature by identifying the peculiarity of the auditing firms in designing and implementing performance measurement systems including the need for sound and advanced information systems, subjectivity embedded in measuring customer satisfaction, growth and success of the firms and restrictions imposed by regulations and auditing standards for the provision of non-audit services which may increase the firms’ revenues and profits to help maintain high-quality outputs. Also, the paper provided evidence for the use of non-financial measures in service industry in particular for customers and finance. The unique dilemma in the auditing firms to provide services to satisfy customers yet maintaining distance and independence from them represent an important research question requiring investigation and study. Design/methodology/approach – A review of the literature for performance evaluation in general and in particular BSCs in service industries was made to identify challenges facing auditing firms when measuring their performance. Data were collected using case study approach; two auditing firms, one of the Big 4 and a medium size auditing firm with international affiliation operating in the Egyptian market were selected. Interviews, document analysis and participant observations were used in the analysis of each firm performance measurement system. Findings – The paper suggests that major challenges face auditing firms in measuring their performance mainly the size of the firm and its affiliation with international auditing firm, the qualification and experience of partners and audit managers needed for the design and implementation of a BSC or similar performance measures, the resources required for the introduction of such performance measure and the peculiarity of the auditor and client relationship with the need to maintain independence and confidentiality while providing high-quality services. Although both auditing firms being studied have formal performance measurement systems, they differ in their degree of comprehensiveness. In particular, the performance measurement system of the larger firm is more elaborate than that of the smaller one and both place more emphasis on qualitative measures such as learning and growth and internal business processes than financial measures. Research limitations/implications – Overall, the results have implications for understanding the performance measurement process of auditing firms in general and in particular in an emerging economy such as Egypt. The identification of the challenges facing auditing firms in measuring their performance and how the implementation of BSC can help partners and employees to overcome those challenges will add to the literature for performance evaluation in service companies. Future research should be carried to compare and assess differences between the behavioural aspects of performance measures in auditing firms and possible application of BSC in such firms and those used in services industry. Also, the practicality of implementing a BSC measures for different auditing firms should be investigated further in future research. Originality/value – The research among the first to investigate the challenges facing auditing firms in designing and operating a performance measurement system and to discuss, using case studies, how a BSC could support the auditing firms to overcome such challenges. Further, the research provides insights into performance measures in auditing firms in developing economies like Egypt which are sparse since most studies have been conducted in developed economies. Also, the paper enriches the literature of performance measurement systems in service rather than the manufacturing sector especially for medium and small size firms.

Broken Rail Prediction and Detection Using Wavelets and Artificial Neural Networks

2011 ◽  
Author(s):  
Brad M. Hopkins ◽  
Saied Taheri
Keyword(s):  
Neural Networks ◽  
Defect Detection ◽  
Classification Algorithm ◽  
Vertical Acceleration ◽  
Acceleration Signal ◽  
Acceleration Data ◽  
Frequency Domains ◽  
Artificial Neural ◽  
The Fourier Transform ◽  
Signal Processing Methods

Current track health monitoring requires time consuming use of railway monitoring vehicles. This paper presents a rail defect detection and classification algorithm that could potentially be used with bogie side frame vertical acceleration data from a data acquisition system located onboard a train car during daily operation. The algorithm uses wavelets to process the vertical acceleration data and detect irregularities in the signal. Wavelets have proven themselves to be useful in event detection and other applications where localization is needed in both the time and frequency domains. Traditional signal processing methods may use the Fourier transform which is limited to localization only in the frequency domain. Wavelets provide a solution for recognizing rail defects and determining their location. The wavelet-processed data is fed into an artificial neural network for defect classification. Neural networks can be a powerful tool in pattern recognition and classification because of their ability to be taught. The network in this algorithm has been trained to recognize impending breaks and breaks in a rail from the original vertical acceleration signal and the first four scales of the wavelet transformed signal. This paper presents an offline analysis of a set of collected data using the proposed defect detection and classification algorithm.

scholarly journals Kinetic measurement system use in individuals following anterior cruciate ligament reconstruction: a scoping review of methodological approaches

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Wasim Labban ◽  
Meredith Stadnyk ◽  
Mark Sommerfeldt ◽  
Stephanie Nathanail ◽  
Liz Dennett ◽  
...  
Keyword(s):  
Anterior Cruciate Ligament ◽  
Anterior Cruciate Ligament Reconstruction ◽  
Measurement System ◽  
Ligament Reconstruction ◽  
Cruciate Ligament ◽  
Future Research ◽  
Kinetic Measurement ◽  
Cruciate Ligament Reconstruction ◽  
Measurement Systems ◽  
Anterior Cruciate

Abstract Purpose Our primary objectives were to (1) describe current approaches for kinetic measurements in individuals following anterior cruciate ligament reconstruction (ACLR) and (2) suggest considerations for methodological reporting. Secondarily, we explored the relationship between kinetic measurement system findings and patient-reported outcome measures (PROMs). Methods We followed the PRISMA extension for scoping reviews and Arksey and O’Malley’s 6-stage framework. Seven electronic databases were systematically searched from inception to June 2020. Original research papers reporting parameters measured by kinetic measurement systems in individuals at least 6-months post primary ACLR were included. Results In 158 included studies, 7 kinetic measurement systems (force plates, balance platforms, pressure mats, force-measuring treadmills, Wii balance boards, contact mats connected to jump systems, and single-sensor insoles) were identified 4 main movement categories (landing/jumping, standing balance, gait, and other functional tasks). Substantial heterogeneity was noted in the methods used and outcomes assessed; this review highlighted common methodological reporting gaps for essential items related to movement tasks, kinetic system features, justification and operationalization of selected outcome parameters, participant preparation, and testing protocol details. Accordingly, we suggest considerations for methodological reporting in future research. Only 6 studies included PROMs with inconsistency in the reported parameters and/or PROMs. Conclusion Clear and accurate reporting is vital to facilitate cross-study comparisons and improve the clinical application of kinetic measurement systems after ACLR. Based on the current evidence, we suggest methodological considerations to guide reporting in future research. Future studies are needed to examine potential correlations between kinetic parameters and PROMs.

scholarly journals Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study

10.2196/15700 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e15700 ◽  
Author(s):  
Youjin Hwang ◽  
Hyung Jun Kim ◽  
Hyung Jin Choi ◽  
Joonhwan Lee
Keyword(s):  
Topic Modeling ◽  
Emotional Eating ◽  
User Study ◽  
Topic Model ◽  
Behavioral Data ◽  
Abnormal Behavior ◽  
Stochastic Gradient Descent ◽  
Future Research ◽  
Behavior Patterns ◽  
Online Social Media

Background Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). Objective This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. Methods The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. Results The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001). Conclusions This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

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