Surface electromyographic signals collected during long-lasting ground walking of young able-bodied subjects
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The present dataset is composed of long-lasting (around 5 minutes) surface electromyographic (sEMG) signals recorded from 2011 and 2018 during ground walking of 31 young (20 years < age < 30 years) able-bodied subjects in the Movement Analysis Lab, Universita Politecnica delle Marche, Ancona, Italy. Underweight, overweight, and obese people (body mass index, BMI < 18.5 Kg/m2 and BMI > 25 Kg/m2) and subjects affected by any pathological condition, joint pain, or undergone orthopedic surgery are not included in the present dataset. sEMG signals are acquired from the following ten different leg muscles (five per leg): gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), hamstrings (Ham), and vastus lateralis (VL). Synchronized footswitch and electrogoniometric data are provided in order to allow users to achieve a spatial/temporal characterization of the sEMG signals. Data have been acquired in subjects walking barefoot on level ground for around 5 min at their natural speed and pace, following an eight-shaped path which includes rectilinear segments and curves. The considerable length of the signals makes this dataset very suitable for those studies where the numerosity of the data is essential, such as machine/deep learning approaches, studies for analyzing and quantifying the variability of muscle recruitment during physiological walking, and creation of reference dataset in the characterization of pathological conditions.
本数据集收录了2011年至2018年间，于意大利安科纳马尔凯理工大学运动分析实验室中，31名年龄介于20至30岁的健康年轻受试者平地行走时采集的长时程（约5分钟）表面肌电（surface electromyographic, sEMG）信号。 本数据集未纳入体重过轻（身体质量指数BMI<18.5kg/m²）、超重或肥胖（BMI>25kg/m²）人群，以及存在任何病理状况、关节疼痛或曾接受骨科手术的受试者。 本次采集的表面肌电信号来自双侧腿部共10块肌肉（每侧5块）：腓肠肌外侧头（gastrocnemius lateralis, GL）、胫骨前肌（tibialis anterior, TA）、股直肌（rectus femoris, RF）、腘绳肌（hamstrings, Ham）及股外侧肌（vastus lateralis, VL）。 同时配套提供同步的足底压力开关（footswitch）与电子测角（electrogoniometric）数据，便于研究者对表面肌电信号进行时空特征表征。 所有受试者均以自然步速与步态节律赤足行走于平坦地面，沿包含直线段与弯道的八字形路径行走约5分钟。 该数据集的信号时长充足，十分适用于对数据量有较高要求的研究方向，例如机器学习/深度学习相关研究、生理行走过程中肌肉募集模式的变异性分析与量化研究，以及病理状态特征表征领域的参考数据集构建工作。
