Wireless AI-Powered IoT Sensors for Laboratory Mice Behavior Recognition

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Yifan Liu
  • John Chung Tam
  • Chishing Chan


Original languageEnglish
Number of pages13
Journal / PublicationIEEE Internet of Things Journal
Publication statusOnline published - 18 Jun 2021


More than 100 million animals used in research, education, and testing per year, and 95% of them are mice and rats. We have developed wireless Artificial Intelligent (AI)-powered IoT (Internet of Things) Sensors (AIIS) for laboratory mice motion recognition utilizing embedded micro-inertial measurement units (uIMUs) – a new sensing platform fills an important research gap of monitoring bahviors of many laboratory mice in parallel. We have demonstrated a wireless IoT sensor that could be attached and carried by mice (i.e., animals that typically weigh only 20g and with body length of ∼10cm) and used the collected motion data to recognize 5 common mice behaviors (e.g., sleeping, walking, rearing, digging and shaking) in cages with accuracy of ∼76%. For comparison, current commercial video-based tracking systems that track animal behaviors in real-time can reach only 70% accuracy and with limited number of parallelly tracked animals. Furthermore, several machine learning algorithms were explored to solve the imbalanced sample data problem, which allowed the accuracy of mice motion recognition to improve from ∼48% to ∼76% (if shaking is removed from classification, an average accuracy of 86.46% could be achieved). Less frequent mice behaviors such as rearing, digging, grooming, drinking and scratching could also be recognized at an average accuracy of 96.35%. We believe this work has the potential to revolutionize animal behavioral tracking methodology by offering a solution for large batches of simultaneous small animal motion tracking and AI-based behavior recognition.

Research Area(s)

  • Behavior recognition, IoT sensors, small animal motion tracking, imbalanced learning, feature selection