AI 中文总结
该研究构建了健康成人下肢活动的同步AMG与EMG多模态数据集，通过基准测试评估其用于关节角估计的性能，为下肢肌肉协调相关研究提供了可重复的资源。
AI 中文摘要
了解下肢肌肉群的协调方式对研究运动损伤、康复及身体机能表现具有重要意义。对这种协调关系的可重复分析需要多模态记录，以关联局部肌肉相关信号与身体层面的运动学数据。EMG捕捉神经层面的电激活，而AMG则提供了一种监测肌肉活动的有价值的力学方法，二者互为补充。本文介绍了一个针对健康成人下肢活动的同步多模态数据集。在左腿数据采集时，将16个三轴加速度计平均分为4组肌肉位点簇以进行AMG记录，同时辅以4个表面EMG通道；采用15标记点的光学动作捕捉（MoCap）系统采集下肢运动学数据，所得标记点轨迹用于计算双侧膝、踝关节角度。该数据集包含30名受试者在16种任务条件下的1918次试次。我们通过从带通滤波至5-100 Hz的AMG数据的300 ms窗口估计4个关节角，对数据集进行基准测试，并在单独的模态消融实验中评估匹配的EMG特征。在跨受试者基准测试中，4个参考模型的平均绝对误差为8.840°-9.591°。基准测试与消融实验结果刻画了不同受试者、任务及关节角下的性能，考察了传感器配置、模态、训练受试者数量及频率表示的影响。数据集发布内容包括已记录的时间定义、处理后的数据及可重复的基准测试资源。
英文摘要
Understanding how lower-limb muscle groups coordinate is important for studying movement impairment, rehabilitation, and physical performance. Reproducible analysis of this coordination requires multimodal recordings that relate local muscle-related signals with body-level kinematics. Complementing neural-level electrical activation captured by EMG, AMG provides a valuable mechanical approach to monitoring muscle activity. Here, we introduce a synchronized, multimodal dataset for healthy-adult lower-limb activities. For data collection on the left leg, 16 triaxial accelerometers were evenly divided into four muscle-site clusters for AMG recording, complemented by four surface EMG channels. A 15-marker optical motion-capture (MoCap) system captured lower-body kinematics, with the resulting marker trajectories used to compute bilateral knee and ankle joint angles. Our dataset contains 1,918 trials from 30 subjects across 16 task conditions. We benchmark the dataset by estimating four joint angles from 300 ms windows of the 5-100 Hz band-pass-filtered AMG data and assess matched EMG features in a separate modality ablation. In the primary cross subject benchmark, the four reference models achieved mean absolute errors of 8.840$^\circ$-9.591$^\circ$. The benchmark and ablation results characterize performance across subjects, tasks, and joint angles and examine the effects of sensor configuration, modality, the number of training subjects, and frequency representation. The release includes documented timing definitions, processed data, and reproducible benchmark resources. https://dongxutang918-afk.github.io/SAME-Limb/
