Abstract
It is being observed that the use of Internet of Medical Things (IoMT) in health sciences research grows as the technology and miniaturization of devices occur. Those devices often times suffer from several issues such as: being invasive, lacking the ability to collect many signals concurrently and communication. The present work proposes an approach to the use of an ultra-small IoMT device to fulfill the functions of a bio-signals data collector, a mathematical models processor, a data transmitter-receptor through the network, and a mechanical actuators controller. Our test environment was conceived to be used in animal models. This is an ongoing project from which partial results indicate that, when functioning, the device will use real-time data for monitoring bio-signals and helping with decision-making as it interacts with the organism. The data collection is going to be made through low-cost non-invasive sensors. Therefore, it can favor installation, maintenance, access, and promote a better quality of life for the user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
The Linux Kernel Archives. GPIO Documentation. In: The Linux Kernel (2022)
Arduino. Language Reference (2022). https://www.arduino.cc/reference/en/
Braga, V.A., Burmeister, M.A.: Applications of telemetry in small laboratory animals for studying cardiovascular diseases. In: Modern Telemetry (Oct 2011). https://doi.org/10.5772/22989
Crow, B.P., et al.: IEEE 802.11 wireless local area networks. IEEE Commun. Mag. 35(9), 116–126 (1997). https://doi.org/10.1109/35.620533
Dwivedi, R., Mehrotra, D., Chandra, S.: Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. J. Oral Biol. Craniofacial Res. 12(2), 302–318 (2022). https://doi.org/10.1016/j.jobcr.2021.11.010
Ho, D., et al.: Heart rate and electrocardiography monitoring in mice. Curr. Protoc. Mouse Biol. 1, 1–24 (2012). https://doi.org/10.1002/9780470942390.mo100159
Texas Instruments. TMP-117 high-accuracy, low-power, digital temperature sensor with SMBus - and I2C-compatible interface. Texas Instruments Incorporated, pp. 1–50 (September 2022)
Texas Instruments. Ultra-small, low-power, 16-bit analog-to-digital converter with internal reference. Texas Instruments Incorporated, pp. 1–37 (October 2009)
Invensense. MPU-6000 and MPU-6050 Product Specification Revision 3.4. In: Invensense, pp. 1–52 (August 2013)
Isikdag, U.: Internet of Things: Single-Board Computers. In: Enhanced Building Information Models, pp. 43–53 (2015)
Jin, T.: OpenWrt Development Guide. In: Wireless Networks Lab. CCIS, pp. 1–11 (Febuary 2012)
Joyia, G.J., et al.: Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain. J. Commun. 12, 240–247 (2017) https://doi.org/10.12720/jcm
Mitsunaga, Jr. J.K., et al.: Rat an experimental model for burns. A systematic review. Acta Cirúrgica Brasileira 27, 417–423 (2012). https://doi.org/10.1590/S0102-86502012000600010
Konopelski, et al.: Electrocardiography in Rats: a Comparison to Human. Physiological research/Academia Scientiarum Bohemoslovaca 65, 717–725 (2016). https://doi.org/10.33549/physiolres.933270
Mauerer, W.: Professional Linux Kernel Architecture. Wiley Publishing, Inc. (2008)
MicroPython. MicroPython (2018). https://micropython.org/
Normann, S., Priest, R.E., Benditt, E.P.: Electrocardiogram in the normal rat and its alteration with experimental coronary occlusion. Circulation Res. 9, 282–287 (1961). https://doi.org/10.1161/01.RES.9.2.282
Raspberry Pi. Raspberry Pi Documentation (2022). https://www.raspberrypi.com/documentation/microcontrollers/rp2040.html
Rohm. Digital 16bit serial output type ambient light sensor IC. In: Rohm Co., pp. 1–18 (April 2010),
van Rossum, G.: Glue it all together with Python. In: OMG-DARPAMCC Workshop on Compositional Software Architecture (January 1998)
VoCore Studio. VoCore2. In: VoCore Studio (2022)
Al-Turjman, F., Nawaz, M.H., Ulusar, U.D.: Intelligence in the Internet of Medical Things era: A systematic review of current and future trends. Comput. Commun. 150, 644–660 (2020). https://doi.org/10.1016/j.comcom.2019.12.030
Zhao, C.W., Jegatheesan, J., Loon, S.C.: Exploring IOT application using Raspberry Pi. In. J. Comput. Netw. Appli. 2, 27–34 (2015)
Acknowledgements
This project has been funded by the Center for Research and Education on Aging (CREA), UC Berkeley.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Barbosa, B.C.M., Garan, S.A., Quintela, B.M., Dantas, M.R. (2023). Data Logging and Non-invasive IoMT Approach for Rats Monitoring in Laboratory Experiments. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-031-29056-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-031-29056-5_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29055-8
Online ISBN: 978-3-031-29056-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)