Human-computer interaction (HCI) strategies communicate the human mind and machine intelligence based on different devices and technologies. The majority of HCI strategies assume normal physical conditions that limit accessibility for users with disabilities. Certain products, such as Braille keyboards, work fine for people with specific disabilities. However, a more general HCI strategy that can neglect users’ physical conditions would enhance the accessibility of these tools for disabled persons. Here, we report an HCI strategy that utilises triboelectricity of the human body (TEHB) for HCI. The TEHB can be generated by many parts of the human body, eliminating the obstacles imposed by physical function disabilities. Such an HCI approach has been used for text inputs, graphical inputs, and mimicked mouse functions. With the assistance of deep learning, an accuracy of approximately 98.4 % is achieved for text inputs obtained directly from handwriting. Our findings provide a new approach for HCI and demonstrate the feasibility of multiple interaction modes.

人机交互 (HCI) 策略基于不同的设备和技术交流人类思维和机器智能。大多数人机交互策略都假设正常的身体条件限制了残疾用户的可访问性。某些产品（例如盲文键盘）适用于特定残障人士。但是，可以忽略用户身体状况的更通用的 HCI 策略将增强这些工具对残疾人的可及性。在这里，我们报告了一种利用人体摩擦电 (TEHB) 进行 HCI 的 HCI 策略。TEHB可由人体的许多部位产生，消除了身体机能障碍带来的障碍。这种 HCI 方法已用于文本输入、图形输入和模拟鼠标功能。在深度学习的帮助下，直接从手写获得的文本输入的准确率约为 98.4%。我们的研究结果为 HCI 提供了一种新方法，并证明了多种交互模式的可行性。