目的 建立一种基于DeepLabCut(DLC)算法用于评价老年小鼠运动功能的步态分析系统.方法 基于深度学习技术中的DLC算法,采用跑台装置和全封闭设计,构建系统软硬件;应用本系统评价不同运动模式下衰老所致小鼠的步态差异;通过相关性分析探究体质量与体长对步态指标的影响.结果 本系统实现特定步速下小鼠三维立体步态(侧面和腹平面)的同步分析,自动量化47项步态指标.应用本系统发现,步行时(15 cm·s-1),相比2月龄、8月龄和15月龄小鼠体转角标准偏差下降,前肢摆动时长、膝关节(Knee)角度标准偏差、左后爪和右后爪向外角度平均值增加;15月龄小鼠还出现步频降低,步幅、双支撑总时长、Knee伸展和收缩距离增加.小跑时(20 cm·s-1),15月龄小鼠无法稳定行走,相比2月龄,8月龄小鼠左后爪向外角度平均值和双支撑总时长增加.相关性分析发现,步频、步幅、前肢摆动时长、后爪向外角度平均值、双支撑总时长、Knee角度标准偏差、Knee伸展和收缩距离等指标均未受到体质量和体长变化的影响.结论 基于DLC算法的小鼠步态分析系统实现了对老年小鼠步态更加敏感、准确、全面的评价,区分出老年小鼠为维持步态稳定性所表现的步态特征,并筛选出更能反映老年小鼠步态变化的行为指标.为今后更有效评估抗衰老、抗运动协调功能下降药物的药效与副作用提供方法学基础.

The vast diversity of morphologies,body size,and lifestyles of snakes represents an important source of information that can be used to derive bio-inspired robots through a biology-push and pull process.An understanding of the detailed kinematics of swimming snakes is a fundamental prerequisite to conceive and design bio-inspired aquatic snake robots.However,only limited information is available on the kinematics of swimming snake.Fast and accurate methods are needed to fill this knowledge gap.In the present paper,three existing methods were compared to test their capacity to characterize the kin-ematics of swimming snakes.(1)Marker tracking(Deftac),(2)Markerless pose estimation(DeepLabCut),and(3)Motion capture were considered.(4)We also designed and tested an automatic video processing method.All methods provided different albeit complementary data sets;they also involved different technical issues in terms of experimental conditions,snake manipulation,or processing resources.Marker tracking provided accurate data that can be used to calibrate other methods.Motion capture posed technical difficulties but can provide limited 3D data.Markerless pose estimation required deep learning(thus time)but was efficient to extract the data under various experimental conditions.Finally,automatic video processing was particularly efficient to extract a wide range of data useful for both biology and robotics but required a specific experimental setting.