Manni Andrea, Rescio Gabriele, Carluccio Anna Maria, Caroppo Andrea, Leone Alessandro
National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy.
Sensors (Basel). 2025 Feb 20;25(5):1305. doi: 10.3390/s25051305.
Gait is a complex motor process that involves the coordination and synchronization of various body parts through continuous interaction with the environment. Monitoring gait is crucial for the early detection of abnormalities, such as toe walking, which is characterized by limited or absent heel contact with the floor during walking. Persistent toe walking can cause severe foot, ankle, and musculature conditions; poor balance; increased risk of falling or tripping; and can affect overall quality of life, making it difficult, for example, to participate in sports or social activities. This study proposes a new approach to detect toe walking using surface Electromyography (sEMG) on lower limbs. sEMG sensors, by measuring the electrical activity of muscles, can see signals before the movement corresponding to muscle activation, contributing to an early detection of a possible problem. The sEMG signal presents significant complexity due to its noisy nature and the challenge of extracting meaningful features for classification. To address this issue and enhance the model's robustness across different devices and configurations, a Transfer Learning (TL) approach is introduced. This method leverages pre-trained models to effectively handle the variability of sEMG data and improve classification accuracy. In particular, Continuous Wavelet Transform (CWT) is applied to sEMG-filtered signals (with time windows of 1 s) to convert them into 2D images (scalograms). Preliminary tests were performed on a public dataset using some of the most well-known pre-trained architectures, obtaining an accuracy of about 95% on InceptionResNetV2.
步态是一个复杂的运动过程，它通过与环境的持续交互来协调和同步身体的各个部位。监测步态对于早期发现异常情况至关重要，例如足尖行走，其特征是行走时脚跟与地面接触受限或没有接触。持续的足尖行走会导致严重的足部、踝关节和肌肉问题；平衡能力差；跌倒或绊倒的风险增加；并会影响整体生活质量，例如难以参与体育或社交活动。本研究提出了一种利用下肢表面肌电图（sEMG）检测足尖行走的新方法。sEMG传感器通过测量肌肉的电活动，可以在与肌肉激活相对应的运动之前看到信号，有助于早期发现可能的问题。由于其噪声特性以及提取有意义特征进行分类的挑战，sEMG信号呈现出显著的复杂性。为了解决这个问题并提高模型在不同设备和配置上的鲁棒性，引入了迁移学习（TL）方法。该方法利用预训练模型有效地处理sEMG数据的变异性并提高分类准确率。具体而言，将连续小波变换（CWT）应用于经过滤波的sEMG信号（时间窗口为1秒），将其转换为二维图像（尺度图）。使用一些最著名的预训练架构在一个公共数据集上进行了初步测试，在InceptionResNetV2上获得了约95%的准确率。
