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AI-enabled Micro Motion Sensors for Revealing the Random Daily Activities of Caged Mice
  • +5
  • Yifan Liu,
  • Meng Chen,
  • Chishing Chan,
  • Ho-yin Chan,
  • Jianping Wang,
  • Xinge Yu,
  • Xinyue Li,
  • Wen Jung Li
Yifan Liu
City University of Hong Kong

Corresponding Author:[email protected]

Author Profile
Meng Chen
City University of Hong Kong
Chishing Chan
Ho-yin Chan
City University of Hong Kong
Jianping Wang
City University of Hong Kong
Xinge Yu
City University of Hong Kong
Xinyue Li
City University of Hong Kong
Wen Jung Li
City University of Hong Kong


More than 120 million mice and rats are used yearly for scientific purposes. While tracking their motion behaviors has been an essential issue for the past decade, present techniques, such as video-tracking and IMU-tracking have considerable problems, including requiring a complex setup or relatively large IMU modules that cause stress to the animals. Here, we introduce a wireless IoT motion sensor (i.e., weighing only 2 grams) that can be attached and carried by mice to collect motion data continuously for several days. We also introduce a combined segmentation method and an imbalanced learning process that are critical for enabling the recognition of common but random mouse behaviors (i.e., resting, walking, rearing, digging, eating, grooming, drinking water, and scratching) in cages with a macro-recall of 94.55%.

Corresponding author(s) Email:   [email protected]; [email protected]; [email protected]

06 Aug 2022Submitted to AISY Interactive Papers
09 Aug 2022Published in AISY Interactive Papers