A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis
Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automati...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4289 |
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| author | Guanghui Li Salif Komi Jakob Fleng Sorensen Rune W. Berg |
| author_facet | Guanghui Li Salif Komi Jakob Fleng Sorensen Rune W. Berg |
| author_sort | Guanghui Li |
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| description | Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement. |
| format | Article |
| id | doaj-art-139a2e5e62a8445a8485a0efb945338f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-139a2e5e62a8445a8485a0efb945338f2025-08-20T03:56:47ZengMDPI AGSensors1424-82202025-07-012514428910.3390/s25144289A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait AnalysisGuanghui Li0Salif Komi1Jakob Fleng Sorensen2Rune W. Berg3Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, DenmarkDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, DenmarkDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, DenmarkDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, DenmarkTreadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement.https://www.mdpi.com/1424-8220/25/14/4289real-time computer visionintelligent treadmilladaptive controlOpenMV4object trackingFOMO MobileNetV2 |
| spellingShingle | Guanghui Li Salif Komi Jakob Fleng Sorensen Rune W. Berg A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis Sensors real-time computer vision intelligent treadmill adaptive control OpenMV4 object tracking FOMO MobileNetV2 |
| title | A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis |
| title_full | A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis |
| title_fullStr | A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis |
| title_full_unstemmed | A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis |
| title_short | A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis |
| title_sort | real time vision based adaptive follow treadmill for animal gait analysis |
| topic | real-time computer vision intelligent treadmill adaptive control OpenMV4 object tracking FOMO MobileNetV2 |
| url | https://www.mdpi.com/1424-8220/25/14/4289 |
| work_keys_str_mv | AT guanghuili arealtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT salifkomi arealtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT jakobflengsorensen arealtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT runewberg arealtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT guanghuili realtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT salifkomi realtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT jakobflengsorensen realtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis AT runewberg realtimevisionbasedadaptivefollowtreadmillforanimalgaitanalysis |