Electronic Monitoring System for Measuring Heart Rate and Skin Temperature in Small Ruminants

Costa, Da; Turco, Sílvia Helena Nogueira; Ramos, Renato Moran; Silva, Flaviane M. F. M.; Freire, Murilo Santos

doi:10.1590/1809-4430-eng.agric.v38n2p166-172/2018

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…After analog to digital conversion, each HR analog signal was sent by the transmitter in the slave unit and then received by the receiver in the master unit, which sent the digital pulses of sensing data to the master microcontroller. Specifically, the sampling interval between pulse signals that were sent by the receiver was analyzed through the external interrupt timer and it provided a time base for heart rate calculation [19]. Similarly, the ST signal was acquired by the A/D converter of the slave microcontroller, and was then sent to the master microcontroller through the wireless communication [19,20].…”

Section: Data Collectionmentioning

confidence: 99%

“…Specifically, the sampling interval between pulse signals that were sent by the receiver was analyzed through the external interrupt timer and it provided a time base for heart rate calculation [19]. Similarly, the ST signal was acquired by the A/D converter of the slave microcontroller, and was then sent to the master microcontroller through the wireless communication [19,20]. Transferring wireless data through a generic Universal Synchronous/Asynchronous Receiver/Transmitter (USART) serial communication protocol.…”

Section: Data Collectionmentioning

confidence: 99%

“…Transferring wireless data through a generic Universal Synchronous/Asynchronous Receiver/Transmitter (USART) serial communication protocol. For a variety of serial protocols, the USART peripherals were asynchronously configured with a baud rate of 9600 bps [19,21]. In addition, data of HR and ST were stored in the Micro Secure Digital Memory (Micro SD) card for permanent record while using the Serial Peripheral Interface (SPI) communication protocol [19].…”

Section: Data Collectionmentioning

confidence: 99%

“…For a variety of serial protocols, the USART peripherals were asynchronously configured with a baud rate of 9600 bps [19,21]. In addition, data of HR and ST were stored in the Micro Secure Digital Memory (Micro SD) card for permanent record while using the Serial Peripheral Interface (SPI) communication protocol [19]. However, the sensing data of transportation environment were obtained by the A/D converter in the master microcontroller.…”

Section: Data Collectionmentioning

confidence: 99%

“…When considering the issue of software compatibility, the latest version v1.8.8 was not recommended. The Arduino firmware has pre-configured all of the hardware parameters, including interrupts, A/D converter settings, digital/analog input and output (I/O) pins, timers, and communication peripherals [19,32]. It should be noted that the corresponding firmware board should be selected when downloading the programs since the master and slave units had different microcontrollers.…”

Section: Electric Circuitry For Wsmsmentioning

confidence: 99%

See 4 more Smart Citations

WSMS: Wearable Stress Monitoring System Based on IoT Multi-Sensor Platform for Living Sheep Transportation

Cui

Zhang

et al. 2019

Electronics

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Farming herdsmen, sheep dealers, and veterinarians are increasingly interested in continuously monitoring sheep basic physiological characteristics (such as the heart rate and skin temperature) outside the laboratory environment, with the aim of identifying the physiological links between stress, uncomfortable, excitement, and other pathological states. This paper proposes a non-invasive Wearable Stress Monitoring System (WSMS) with PhotoPlethysmoGram (PPG), Infrared Temperature Measurement (ITM), and Inertial Measurement Units (IMU) that aimed to remotely and continuously monitor the stress signs of sheep during transportation. The purpose of this study was implemented by following the multi-dimensional sensing platform to identify more pressure information. The designed WSMS showed sufficient robustness in recording and transmitting sensing data of physiology and environment during transport. The non-contact and non-destructive monitoring method that was proposed in this paper was helpful in minimizing the effects of sheep stress load.

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Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Electric Circuitry For Wsmsmentioning

confidence: 99%

See 3 more Smart Citations

WSMS: Wearable Stress Monitoring System Based on IoT Multi-Sensor Platform for Living Sheep Transportation

Cui

Zhang

et al. 2019

Electronics

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Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

et al. 2023

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Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

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Internet of Things (IoT) and Machine Learning (ML) enabled Livestock Monitoring

Chaudhry

Mumtaz

Zaidi

et al. 2020

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)

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Electronic Monitoring System for Measuring Heart Rate and Skin Temperature in Small Ruminants

Cited by 7 publications

References 19 publications

WSMS: Wearable Stress Monitoring System Based on IoT Multi-Sensor Platform for Living Sheep Transportation

WSMS: Wearable Stress Monitoring System Based on IoT Multi-Sensor Platform for Living Sheep Transportation

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

Internet of Things (IoT) and Machine Learning (ML) enabled Livestock Monitoring

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