SNR values for all sEMG signals.

Variability of myoelectric activity during walking is the result of human capability to adapt to both intrinsic and extrinsic perturbations. The availability of sEMG signals lasting at least some minutes (instead of seconds) is needed to comprehensively analyze the variability of surface electromyographic (sEMG) signals. The current study introduces a dataset of long-lasting sEMG signals recorded during walking sessions of 31 healthy subjects, aged between 20 and 30 years, conducted at the Movement Analysis Lab of Università Politecnica delle Marche, Ancona, Italy. The sEMG signals were captured from ten distinct lower-limb muscles (five per leg), including gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), hamstrings (Ham), and vastus lateralis (VL). Synchronized electrogoniometric and foot-floor-contact signals are also supplied to enable the spatial/temporal analysis of the sEMG signals. The experimental procedure involves subjects walking barefoot on level ground for approximately 5 minutes at their natural speed and pace, following an eight-shaped path featuring linear diagonal segments, curves, accelerations, and decelerations. An advanced analysis of the sEMG signals was performed to test the reliability and usability of the current dataset. The considerable duration of the signals makes this dataset particularly useful for studies where a significant volume of data is crucial, such as machine/deep learning approaches, investigations examining the variability of muscle recruitment during physiological walking, validations of the reliability of novel sEMG-based algorithms, and assembly of reference datasets for pathological condition characterization.行走过程中肌电活动的变异性，源于人类适应内在与外在扰动的生理能力。若要全面分析表面肌电（surface electromyographic, sEMG）信号的变异性，需要获取时长至少为数分钟（而非数秒）的sEMG信号。 本研究构建了一套长时程sEMG信号数据集，数据采集自意大利安科纳马尔凯理工大学（Università Politecnica delle Marche）运动分析实验室的31名年龄在20至30岁之间的健康受试者的行走实验。本次实验采集了双侧下肢共10块肌肉的sEMG信号（每条下肢对应5块），包括腓肠肌外侧头（gastrocnemius lateralis, GL）、胫骨前肌（tibialis anterior, TA）、股直肌（rectus femoris, RF）、腘绳肌（hamstrings, Ham）与股外侧肌（vastus lateralis, VL）。数据集同时提供同步采集的电子关节角度计信号与足地接触信号，以支持对sEMG信号开展时空分析。 实验流程要求受试者以自然步速与步频赤脚在平地上行走，沿包含直线斜向段、曲线段以及加减速环节的八字形路径行进，总时长约5分钟。 本研究针对该数据集的sEMG信号进行了进阶分析，以验证其可靠性与可用性。该数据集的信号时长充足，尤其适用于对数据量要求较高的研究场景，例如机器学习/深度学习方法研究、生理性行走过程中肌肉募集变异性的相关分析、新型基于sEMG的算法的可靠性验证，以及构建用于病理状态特征表征的参考数据集等方向。