Keyboards and touchscreens are widely used to control electronic devices, but these can be difficult to operate for individuals with dexterity impairments or neurological conditions. Several assistive technologies, such as voice recognition and eye tracking, have been developed to provide alternate methods of control. However, these can have problems in terms of use and maintenance. Here we report a bite-controlled optoelectronic system that uses mechanoluminescence-powered distributed-optical-fibre sensors that are integrated into mouthguards. Phosphors that are sensitive to mechanical stimulus are arranged in an array of contact pads in a flexible mouthguard; by using unique patterns of occlusal contacts in lateral positions, various forms of mechanical deformation can be distinguished by the fibre sensors via ratiometric luminescence measurements. By combining the device with machine learning algorithms, it is possible to translate complex bite patterns into specific data inputs with an accuracy of 98%. We show that interactive mouthguards can be used to operate computers, smartphones and wheelchairs.

键盘和触摸屏广泛用于控制电子设备，但对于有灵活性障碍或神经系统疾病的人来说，这些可能难以操作。已经开发了几种辅助技术，例如语音识别和眼睛跟踪，以提供替代的控制方法。然而，这些在使用和维护方面可能存在问题。在这里，我们报告了一种咬合控制光电系统，该系统使用集成到护齿器中的机械发光驱动的分布式光纤传感器。对机械刺激敏感的磷光体排列在柔性护齿中的接触垫阵列中；通过在横向位置使用独特的咬合接触模式，光纤传感器可以通过比率发光测量来区分各种形式的机械变形。通过将该设备与机器学习算法相结合，可以将复杂的咬合模式转换为特定的数据输入，准确率高达 98%。我们展示了交互式护齿器可用于操作电脑、智能手机和轮椅。