scholarly journals PSXI-20 Milking collar activity data is associated with health events and feed intake in lactating Holstein cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 392-393
Author(s):  
Abigail E Jantzi ◽  
Cori J Siberski ◽  
Brady M Goetz ◽  
Mary Healey ◽  
Kristen Hayman ◽  
...  
Keyword(s):  
Health Status ◽  
Feed Intake ◽  
Predictive Ability ◽  
Sensor Data ◽  
Activity Data ◽  
Health Events ◽  
Lactating Cows ◽  
Heritable Trait ◽  
Average Activity ◽  
The Relationship

Abstract Feed is the largest expense for dairy farms, thus feed efficiency is essential to the sustainability and future of the industry. Our objective was to evaluate the association of milking collar activity with feed intake and health status in lactating cows. Health status was classified for impact of three durations of time (overall, current, or post diagnosis) and as: healthy, mastitis, lame, multiple, or other. Activity data for 155 lactating cows with feed intake records were averaged across two-hour windows to obtain a daily two-hour average. A larger population (n > 1,600) was used to filter out sensor failures and normalize data. Sensor data were adjusted for parity and contemporary group creating adjusted sensor measure (ASM). Dry matter intake (DMI) was adjusted (aDMI) for metabolic body weight, days in milk, and energy sinks used to calculate residual feed intake. Associations between ASM and aDMI, DMI, or health were conducted in SAS9.4. An association of ASM with aDMI was identified (estimate = 0.1635 kg/log count of average activity in a 2-hour period; P < 0.0029). ASM was also associated with DMI (0.2329 kg/log count of average activity, P < 0.0007). ASM was associated with current and overall health timeframes (P < 0.0008 and P < 0.0001, respectively). When health, ASM, and their interaction were included in a model with the response variable aDMI, significant associations were found in the models, including current and overall health (current health: ASM and health: P < 0.0001, interaction: P < 0.0009; overall health: ASM, health, and interaction: P < 0.0001). These results indicate that milking collar data may be useful as a predictor of feed intake either directly or indirectly through detection of health events. Additional studies are needed to determine the predictive ability of collar activity data and the relationship between collar data and health, and to assess if collar activity is an environmental proxy or heritable trait.

scholarly journals PSXI-16 Inclusion of automated sensor data as a predictor of feed intake increases the variance explained by a random forest model

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 394-395
Author(s):  
Cori J Siberski ◽  
Mary S Mayes ◽  
Patrick J Gorden ◽  
Adam Copeland ◽  
Mary Healey ◽  
...  
Keyword(s):  
Random Forest ◽  
Feed Intake ◽  
Predictive Ability ◽  
Training Data ◽  
Sensor Data ◽  
Rumen Temperature ◽  
Full Study ◽  
Rumen Bolus ◽  
Automated Sensors ◽  
Variance Explained

Abstract Prediction of feed intake from indicators would benefit the dairy industry since on-farm feed intake data are rare. The objective of this study was to examine the ability of sensor data to improve predictions of feed intake. Dry matter intake (DMI), milk yield (MY) and components, metabolic body weight (MBW; body weight0.75), and veterinary health records were collected from two cow groups (n1=47, n2=60). Automated sensors (ear tags, rumen bolus, environmental) captured measurements of cow activity, temperature, rumination and rumen pH, and barn temperature and humidity which were used to calculate THI. Random forest (RF) models were trained in R (Caret package) by 10-fold cross validation, with DMI as the response variable. Training data originated from the full study with the exception of the final day, for which DMI was then predicted. Predictive ability was evaluated against a base model excluding automated sensor data to determine changes in accuracy and the percent of variance explained (VAR). The base model included MY and components, MBW, THI, health status and parity. Base model mean square error (MSE) was 9.86, 13.25 and 12.50 kg of DMI and VAR 44.71, 42.9 and 44.85% (n = 92, 56 and 41, respectively). The correlation between actual and predicted final day DMI (CORR) was 0.05, 0.03 and 0.02 (n = 92, 56 and 41, respectively). Adding activity and temperature (first ear tag; n = 92) reduced MSE to 9.70 kg and VAR increased to 45.62% (CORR=0.20). Independently adding bolus activity, rumen temperature and pH (n = 56) to the base model also decreased MSE to 12.53 kg (VAR=46.24% and CORR=0.26). Lastly, adding activity and rumination from the second ear tag (n = 41) to the base model decreased MSE to 12.32 kg (VAR=45.63%, CORR=0.18). Automated sensors appear to explain additional variation in DMI that is not captured in the typical energy sink variables utilized when predicting intake.

The Relationship Between The Perception of Elderly’s Health Status and Health Behaviors in Nakhon Nayok Province, Thailand

2019 ◽  
Vol 12 (1) ◽  
pp. 420-423
Author(s):  
Prapada Watcharanat ◽  
Prasong Tanpichai ◽  
Ravee Sajjasophon
Keyword(s):  
Regression Analysis ◽  
Health Status ◽  
Health Behaviors ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
The Elderly ◽  
Cross Sectional ◽  
Congenital Diseases ◽  
The Relationship

Purpose: This research aims to study the relationship between perception of elderly’s health and health behaviors in Nakhon Nayok province, Thailand Methods: This research was a cross-sectional study. The questionnaire was used to collect the data. This research was conducted in Nakhon Nayok province. The sample size was 270 which applied Taro Yamane's formula at a significant level 0.05. The descriptive statistics was implemented to describe the variables by presenting the frequency, percentage, mean and standard deviation. Furthermore, multiple regression analysis was applied to analyze the relationship between perception of elderly’s health and health behaviors. The statistical significance was considered to reject Hypothesis-null at < 0.05. Results: From a total of 270 people, more than 58.22% of the elderly perceived that they had moderate health conditions. Most elderly had congenital diseases (62.2%). The multiple regression analysis results showed that health status perception and health status perception when compared to their cohort related significantly to health behavior. Conclusion: The government should support the elderly on participation, trust, engagement, and cultural concern of the people in the community, which can contribute to promoting the physical, mental and social condition of the elderly.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

scholarly journals Reduction in mobility and COVID-19 transmission

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Pierre Nouvellet ◽  
Sangeeta Bhatia ◽  
Anne Cori ◽  
Kylie E. C. Ainslie ◽  
Marc Baguelin ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Control Measures ◽  
Social Distancing ◽  
Beneficial Effects ◽  
Mobility Data ◽  
Strict Control ◽  
Before And After ◽  
Proxy Measure ◽  
Lower Transmission ◽  
The Relationship

AbstractIn response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27–77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49–91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12–48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

scholarly journals Similarity Embedding Networks for Robust Human Activity Recognition

2021 ◽  
Vol 15 (6) ◽  
pp. 1-17
Author(s):  
Chenglin Li ◽  
Carrie Lu Tong ◽  
Di Niu ◽  
Bei Jiang ◽  
Xiao Zuo ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Real Space ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Activity Data ◽  
Extensive Evaluation ◽  
Sensor Signals ◽  
Public Datasets

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.

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