A novel accelerometry approach combining information on classified behaviors and quantified physical activity for assessing health status of cattle: a preliminary study

Uenishi, Shogo; Oishi, Kazato; Kojima, Tomoki; Kitajima, Kaho; Yasunaka, Yudai; Sakai, Kyosuke; Sonoda, Yuta; Kumagai, Hajime; Hirooka, Hiroyuki

doi:10.1016/j.applanim.2021.105220

Cited by 8 publications

(10 citation statements)

References 41 publications

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“…The results of the present study indicated that HRV was reduced in RES steers relative to SUP steers during the VA restriction period, but HRV was increased to a level similar to that in SUP steers at 24 months of age. In contrast, our previous study showed that the influence of latent VA deficiency caused by dietary VA restriction on the physical activity of fattening steers at 18 months of age still remained at 24 months of age despite the increase in blood VA concentration (Uenishi et al, 2021). One possible reason for the different results is that RES steers in the present study might be desensitized to long-term mild stress caused by the latent VA deficiency.…”

Section: Resultscontrasting

confidence: 96%

“…For example, Ladewig and Smidt (1989) reported that increased secretion of cortisol in bulls due to tethering disappeared and the return of the basic secretion to normal occurred at the adrenal level, which could involve desensitization of the cells, although the bulls did not show any observable behavioral adaptations. Therefore, the difference between the results of the present study and those of Uenishi et al (2021) that, in cattle recovered from some physiological stress such as latent VA deficiency, the physiological stress response shown by a decrease in HRV might be diminished The results of the present study indicated that feeding VArestricted diets could lead to a decrease in HRV in fattening steers, suggesting the effect of VA intake on the autonomic nervous system of animals. Hence, it can be hypothesized that based on the characteristics of VA, it may act on the autonomic nervous system through several pathways.…”

Section: Resultscontrasting

confidence: 91%

“…The experiment was conducted with the same design as that of our previously study (Uenishi et al, 2021) at the Aichi Agricultural Research Center (35 • 16 ′ N, 137 • 07 ′ E) in Japan. Six clinically healthy Japanese Black fattening steers with similar body weights (BW) at the start of the experiment were equally allocated into two experimental groups (three for each): VA-restricted (RES) and VA-supplemented (SUP) groups.…”

Section: Study Sites and Tested Animalsmentioning

confidence: 99%

“…The steers had ad libitum access to water and mineral blocks. The feeding regimens for the two groups were the same as those reported by Uenishi et al (2021). During the growth stage (age 11-13 months), diets were gradually replaced by those fed during the fattening stage for adaptation.…”

Section: Study Sites and Tested Animalsmentioning

confidence: 99%

“…The autonomic nervous system regulates heartbeats; therefore, it may be possible that vitamin A restriction affects the autonomic nervous system as a feedback control. Although clear clinical symptoms of VA deficiency are now less likely to be seen because of the establishment of dietary VA control in Japan, VA restriction might still be a cause of subclinical health problems in beef cattle (Uenishi et al, 2021). Therefore, the present study investigated the effect of dietary vitamin A restriction during the fattening period on the autonomic nervous system of Japanese Black (a breed of Wagyu) cattle using HRV analysis.…”

Section: Introductionmentioning

confidence: 99%

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An Assessment of Stress Status in Fattening Steers by Monitoring Heart Rate Variability: A Case of Dietary Vitamin A Restriction

Kitajima

Oishi

Kojima³

et al. 2022

Front. Anim. Sci.

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Heart rate variability (HRV), heart beat-to-beat variations, is a measure of cardiac autonomic functions, and HRV monitoring using sensor technology can be a non-invasive way to assess stress of animals. The objective of this study was to investigate the effect of dietary vitamin A (VA) restriction to enhance beef quality on the physiological status of fattening steers by HRV analysis. Six Japanese Black steers were equally allocated into VA-restricted (RES) and VA-supplemented (SUP) groups. The RES steers were fed VA-restricted diets from 11 to 20 months of age. The inter-beat intervals and blood VA concentration were measured at 18 and 24 months of age. HRV parameters in time, frequency, and non-linear domains were calculated using the inter-beat intervals. Blood VA concentration was significantly lower in RES steers than in SUP steers at 18 months of age (P < 0.05) but did not differ between the groups at 24 months of age. The HRV analysis indicated greater sympathetic and lower parasympathetic activities in RES steers than in SUP steers (P < 0.05). However, there were significant interactions of the group and age on HRV parameters (P < 0.05), indicating that although RES steers at 18 months of age might suffer from slight stress, the response could recover to a level similar to SUP steers at 24 months of age by the increase in blood VA concentration.

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Section: Resultscontrasting

confidence: 96%

Section: Resultscontrasting

confidence: 91%

Section: Study Sites and Tested Animalsmentioning

confidence: 99%

Section: Study Sites and Tested Animalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Assessment of Stress Status in Fattening Steers by Monitoring Heart Rate Variability: A Case of Dietary Vitamin A Restriction

Kitajima

Oishi

Kojima³

et al. 2022

Front. Anim. Sci.

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Recognition of the behaviors of dairy cows by an improved YOLO

Bai,

Gao,

et al. 2024

Intell Robot

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The physiological well-being of dairy cows is intimately tied to their behavior. Detecting aberrant dairy cows early and reducing financial losses on farms are both possible with real-time and reliable monitoring of their behavior. The behavior data of dairy cows in real environments have dense occlusion and multi-scale issues, which affect the detection results of the model. Therefore, we focus on both data processing and model construction to improve the results of dairy cow behavior detection. We use a mixed data augmentation method to provide the model with rich cow behavior features. Simultaneously refining the model to optimize the detection outcomes of dairy cow behavior amidst challenging conditions, such as dense occlusion and varying scales. First, a Res2 backbone was constructed to incorporate multi-scale receptive fields and improve the YOLOv3’s backbone for the multi-scale feature of dairy cow behaviors. In addition, YOLOv3 detectors were optimized to accurately locate individual dairy cows in different dense environments by combining the global location information of images, and the Global Context Predict Head was designed to enhance the performance of recognizing dairy cow behaviors in crowded surroundings. The dairy cow behavior detection model we built has an accuracy of 90.6%, 91.7%, 80.7%, and 98.5% for the four behaviors of dairy cows standing, lying, walking, and mounting, respectively. The average accuracy of dairy cow detection is 90.4%, which is 1.2% and 12.9% higher than the detection results of YOLOV3, YOLO-tiny and other models respectively. In comparison to YOLOv3, the Average Precision evaluation of the model improves by 2.6% and 1.4% for two similar features of walking and standing behaviors, respectively. The recognition results prove that the model generalizes better for recognizing dairy cow behaviors using behavior videos in various scenes with multi-scale and dense environment features.

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FedAAR: A Novel Federated Learning Framework for Animal Activity Recognition with Wearable Sensors

Mao¹,

Huang²,

Gan³

et al. 2022

Preprint

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Deep learning dominates automated animal activity recognition (AAR) tasks due to the high performance on large-scale datasets. However, constructing centralised data across diverse farms raises data privacy issues. Federated learning (FL) provides a distributed learning solution to train a shared model by coordinating multiple farms (clients) without sharing their private data. Whereas directly applying FL to AAR tasks often faces two challenges: client-drift during local training and gradient conflicts during global aggregation. In this study, we develop a novel FL framework called FedAAR to achieve AAR with sensor data. Specifically, we devise a prototype-guided local update module to alleviate the client-drift issue, which introduces global prototypes as shared knowledge to force clients to learn consistent features. To reduce gradient conflicts between clients, we design a gradient refinement-based aggregation module to eliminate conflicting components between client gradients during global aggregation, thereby improving the agreement between clients. Experiments are conducted on a public dataset to verify FedAAR’s effectiveness, which consists of 87,621 2-s motion data. The results demonstrate that FedAAR outperforms state-of-the-arts, with precision (75.23%), recall (75.17%), F1-score (74.70%), and accuracy (88.88%), respectively. The ablation experiments also show FedAAR’s robustness against various factors (i.e., different data sizes, communication frequency, and client numbers).

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A novel accelerometry approach combining information on classified behaviors and quantified physical activity for assessing health status of cattle: a preliminary study

Cited by 8 publications

References 41 publications

An Assessment of Stress Status in Fattening Steers by Monitoring Heart Rate Variability: A Case of Dietary Vitamin A Restriction

An Assessment of Stress Status in Fattening Steers by Monitoring Heart Rate Variability: A Case of Dietary Vitamin A Restriction

Recognition of the behaviors of dairy cows by an improved YOLO

FedAAR: A Novel Federated Learning Framework for Animal Activity Recognition with Wearable Sensors

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