Internet of Things (IoT) devices have become prevalent, embedding intelligence into our environment. It is projected that over 75 billion IoT devices will be connected by 2025 worldwide, with the majority being operated indoors. Dye-sensitized solar cells (DSSC) have recently been optimized for ambient light, having the capabilities of providing sufficient energy for self-powered IoT devices. Interaction with digital technologies, termed Human Computer Interaction (HCI), is often achieved via physical mechanisms (e.g. remote controls, cell phones) which can hinder the natural interface between users and IoT devices, a key consideration for HCI. What if the solar cell that is powering the IoT device can also recognize hand gestures which would allow the user to naturally interact with the system? Previous attempts to achieve this have necessarily employed an array of solar cell/photodiodes to detect directionality. In this work, we demonstrate that by monitoring the photocurrent output of an asymmetrically patterned monolithic (i.e., single cell) DSSC, and using machine learning, we can recognize simple hand gestures, achieving an accuracy prediction of 97.71%. This work shows that, DSSCs are the perfect choice for self-powered interactive technologies, both in terms of powering IoT devices in ambient light conditions and having aesthetic qualities that are prioritized by users. As well as powering interactive technologies, they can also provide a means of interactive control.

翻译：物联网设备已日益普及，将智能融入我们的环境。预计到2025年，全球将有超过750亿台物联网设备实现互联，其中大部分在室内运行。染料敏化太阳能电池最近针对环境光进行了优化，能够为自供电物联网设备提供足够的能量。与数字技术的交互（即人机交互）通常通过物理机制（如遥控器、手机）实现，这可能会阻碍用户与物联网设备之间的自然交互——而这一交互恰是人机交互的关键考量。如果为物联网设备供电的太阳能电池还能识别手势，让用户自然地与系统互动，会怎样？以往实现这一目标的尝试必须使用太阳能电池/光电二极管阵列来检测方向性。本研究中，我们证明：通过监测非对称图案化单片（即单电池）染料敏化太阳能电池的光电流输出，并利用机器学习，可以识别简单手势，预测准确率达97.71%。这项工作表明，染料敏化太阳能电池是自供电交互技术的理想选择，既能在环境光条件下为物联网设备供电，又具备用户优先考虑的美学品质。除了为交互技术供电，它们还能提供交互控制的手段。