The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.

可穿戴电子设备的最新发展迅速推动了人工智能和物联网的时代。步态揭示了日常生活中的感官信息，其中包含有关身份和医疗保健的个人信息。当前的步态分析可穿戴电子设备主要受制于高制造成本，操作能耗或劣等分析方法，这些方法几乎不涉及机器学习或实现需要大量数据集进行训练的非最优模型。在这里，我们开发了低成本的摩擦智能袜，用于从低频人体运动中收集能量以传输无线传感数据。配备有自供电功能的袜子也可用作可穿戴传感器，以传递有关用户的身份，健康状况和活动的信息。为了进一步解决无效的分析方法的问题，提出了一种优化的深度学习模型，该模型在步态分析的袜子信号上具有端到端结构，可产生13.个参与者的93.54％的识别准确率，并检测出五种不同的人类活动准确率为96.67％。为了实现实际应用，我们在虚拟空间中映射通过袜子收集的物理信号，以建立用于运动监控，医疗保健，身份识别和未来智能家居应用的数字人系统。