, pp.180-185 http://dx.doi.org/10.14257/astl.2015.117.42 Development of Effective Cattle Health Monitoring System based on Biosensors Myeong-Chul Park 1 and Ok-Kyoon Ha 2 1 Software Academy, Songho College, 66 Namsanri, Gangwon, 25242, Korea 2 Engineering Research Institute, Gyeongsang National University, 501 Jinjudea-ro, Jinju, 52828, Korea africa@songho.ac.kr, jassmin@gnu.ac.kr Abstract. This paper presents a biosensor-based cattle health monitoring system capable of collecting bio-signals of farm animals in an effective way. For the presented monitoring system. We design an integrated monitoring device consisting of a sensing module to measure bio-signals of cattle such as the heartbeat, the breath rate and the momentum, as well as a Zigbee module designed to transmit the biometric data based on Wireless Sensor Network (WSN). Keywords: Livestock diseases, cattle health monitoring, biosensors 1 Introduction The highly contagious livestock diseases lead to not only direct economic damages such as cattle death loss and decreased productivity of cattle farms but also indirect social costs including decline in production in the livestock industry, in related product consumption, in the tourism industry, and in exports, and riffle effects in the other related industries, all of which require enormous restoration work. Since the infectious diseases may cause fatal damages in the communities and countries, it is greatly important to prevent livestock diseases, such as foot-and-mouse disease, from spreading through monitoring and forecasting efforts. In order for Korea s cattle farms with high density of livestock to effectively control the infectious diseases, it is urgently required to introduce a monitoring and forecasting system as well as a new management system based on the bottom-up approach, in which a case of an individual farm can be adopted to other farms in the region. Previous cattle health monitoring systems are restricted to monitor changes in the environmental conditions of livestock barns such as temperature and humidity, or collect data of cattle activities by means of a pedometer or thermal images, making it difficult to produce precise predictive information regarding the occurrence of livestock diseases. Meanwhile, in the livestock industry, biosensors have been increasingly utilized not only to efficiently produce dairy goods but also monitor the livestock s health conditions. In this paper, we design an effective livestock monitoring system (LMS) using biosensors for cattle health monitoring systems. The monitoring system intends to ISSN: 2287-1233 ASTL Copyright 2015 SERSC
collect biometric data directly associated with the diseases from an individual entity and prevent them from occurring or spreading. For the presented system, we designed and configured LMS as a small-sized comprehensive system for the livestock consisting of a censing module to measure bio-signals such as the heartbeat, the breath rate and the momentum, and a Zigbee module for transmitting the collected biometric data to the forecasting system on Wireless Sensor Network (WSN). We verified the validity of the cattle health monitoring system by comparing the results measured by a commercial ECG equipment for cattle to those of LMS in terms of the heartbeat and the breath rate, and obtained the average correlation coefficient of 0.97, indicating a highly positive correlation. 2 Proposed Cattle Health Monitoring System We designed LMS that consist of a biosensor module to measure bio-signals of cattle and a Zigbee module to transmit the biometric data to the forecasting system on the wireless sensor network. Figure 1 shows the overall architecture of LMS for a cattle health monitoring system. Fig. 1. The overall architecture of LMS for a cattle health monitoring system Our LMS uses an electrocardiogram (ECG), a force sensing resistor (FSR), and an accelerometer to measure the bio-signals of each individual cattle. The raw data is filtered using a Band Pass Filter (BPF), a Low Pass Filter (LPF), and a High Pass Filter (HPF) to create digital biometric data. The biometric data is then transmitted to an integrated management system that stores the vital cattle information using a Zigbee WSN module after the signals are amplified using a processing amplifier. 3 Implementation We implemented both the bio-sensor module and the Zigbee WSN module as an integrated device on a single board for an easy installation on the trunk of each cattle. Copyright 2015 SERSC 181
Figure 2 illustrates implemented hardware. In the figure, the ECG component used for heartbeat detection (1 in Figure 2), the Respiration component used for the breath measurement (2 in Figure 2), and the Accelerometer component used for the momentum measurement (3 in Figure 2). Our integrated device includes a MCU for signal processing and a Zigbee WSN module (4 and 5 in Figure 2, respectively). 1 2 4 5 3 Fig. 2. Implementation of the integrated device for LMS 3.1 Biosensor Module Among the three components that are used to measure and collect the cattle s biosignals, the ECG component for detecting the pulse frequency of cattle is configured by means of a 1/2 Vcc reference voltage to design a single power supply. We employed INA326 as a processing amplifier for ECG detection that features low offset, low offset drift, true rail-to-rail I/O, and low cost. The ECG component for the biosensor module is designed to allow 1~30Hz Bandwidth and achieve 1,000 times gain, because its purpose is intended not to measure normal ECG but to detect heartbeat. We set up the RLD circuit to feedback noise for the purpose of reducing external noise, and employed a shield cable to prevent noise arising from the ECG cable. We implemented a Respiration component that includes HPF with 0.159~1Hz bandwidth and 10 times gain, so that signals from the FSR sensor can be amplified through an amplifier to be processed. We employed a protective case made of rubber to protect the pressure-sensitive and waterproof FSR sensor from foreign substances and moisture. Accelerometer component is configured to measure the momentum of each animal entity. We employed a sensor model including the analogue output and Sleep mode as well as 3-axis acceleration range of ±1.5g or ±6g. The raw data obtained through the Accelerometer is transformed into digital signals via LPF (cutoff frequency: 150Hz) and its results are limited to range from 0 to 255. 182 Copyright 2015 SERSC
3.2 Zigbee Sensor Module and Integration We configured a WSN that uses a Zigbee sensor module based on IEEE 802.15.4 to transfer biometric data from the biosensor module to the integrated management system. The Zigbee WSN module meets the specifications of 6dBm (typical) transmitter sensitivity and 98dBm (typical) receiver sensitivity as well as a RP-M100 (MAC) sensor that allows a 120-meter communication distance. Also, in order to enable the communication distance to be practically up to 120 meters, we did not employ an on-board chip antenna, and instead installed a Herical (1dBi) antenna on the outside of the device. For the Zigbee WSN module, we employed a machine control unit (MCU) that provides low-power consumption to control data flow and rapid signal processing with less than 1mA current consumption on the low power mode, 0.1 ua RAM sustained current, less than 1uA RTC operation current, ultra lower power architecture consuming 250uA per MIPS and various clock operations for battery extension. The employed MCU has 12-bit ADC (embedded) and provides rapid arithmetic operation using a Hardware Multiplier. We implemented both the biosensor module and the Zigbee WSN module as an integrated device on a single board. We covered the integrated module device and battery with a protective aluminum box, and then attached the band containing FSR to the trunk of the cattle. For lower power operation, we measured data for one minute after power is on. If a signal has no problem, we turned the error LED off and supplied power to the Zigbee module. Figure 3 shows the device installed on a real cattle. Fig. 3. Installed LMS on a real cattle 4 Evaluation We verified the effectiveness of our LMS by comparing the correlations of biosignals between the designed monitoring system and a commercial ECG equipment. Copyright 2015 SERSC 183
For the comparison, we installed both LMS and the EGC equipment on the same cattle, and measured the heartbeat and the breath rate of the cattle to get bio-signals using the two devices. We deducted the correlation coefficient between the two devices through analyzing the R-R Interval and Heart Rate (HR) using the R-peak of measured signals. The results of deduced correlation coefficient considering each HR which uses the R-R Interval per unit of region appear in Table 1. In the table, we used the HR region, from 30 to 160, considering the possibility of a heart rate for cattle. From the results, LMS shows an average similarity of 96% in HR 30 for the normal cattle. Especially, if the region of comparison is increased, more than 60 HR, the correlation of LMS is also increased as 97.7%, in the average case. The results show that our LMS is a suitable and effective system to continuous monitor the health of the cattle. Table 1. The results of deduced correlation coefficient for LMS HR Unit Area Correlation coefficient 30 2mV 0.9627 4mV 0.9587 60 2mV 0.9836 4mV 0.9794 80 2mV 0.9782 4mV 0.9785 Effectiveness Average 0.9741 5 Conclusion In this paper, we presented a livestock monitoring system, called LMS that collects vital information of each cattle, such as the heartbeat, the breath rate, and the momentum, to help forecast livestock diseases using biosensors based on WSN. We implemented an integrated device including a biosensor module and a Zigbee WSN module on a single board for easy installation on the trunk of cattle. We verified the validity of the cattle health monitoring system by comparing the results measured by a commercial ECG equipment for cattle to those of LMS in terms of the heartbeat and the breath rate, and obtained the average correlation coefficient of 0.97, indicating a highly positive correlation. 184 Copyright 2015 SERSC
Acknowledgments. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2060082). References 1. Hwang, J. H., Lee, M. H., Ju, H. D., Kang, H. J., & Yoe, H.: Implementation of swinery integrated management system in ubiquitous agricultural environments. The Journal of Korea Information and Communications Society 35(2B): 252 262 (2010) 2. Hwang, J. H., Shin, C. S., & Yoe, H.: Study on an agricultural environment monitoring server system using wireless sensor networks. Sensors (Basel) 10(12): 11189 11211 (2010). 3. Ju, M. & Kim, S.: Logistic services using RFID and mobile sensor network. International Journal of Multimedia and Ubiquitous Engineering, Vol. 1(2), pp. 25 29 (2006) 4. Kim, H., Yang, C., & Yoe, H.: Design and implementation of livestock disease forecasting system. The Journal of Korea Information and Communications Society Vol. 37(12), pp. 1263 1270 (2012) 5. Lee, J., Hwang, J. H., & Yoe, H.: Design of integrated control system for preventing the spread of livestock diseases. Lecture Notes in Computer Science, Vol. 7105, pp. 169 173. Springer, Heidelberg (2011) 6. Fujitsu, A jog trot system, http://www.fujitsu.com/kr/sustainability/agriculrure/ 7. Nare Trends Inc. Xspark: An intelligent management system, http://www.xspark.co.kr/ system/cattleshed.php Copyright 2015 SERSC 185