Fertl Elfi, Castillo Encarnación, Stettinger Georg, Cuéllar Manuel P, Morales Diego P
Infineon Technologies AG, 85579 Neubiberg, Germany.
Department of Electronics and Computer Technology, University of Granada, 18071 Granada, Spain.
Sensors (Basel). 2025 Mar 8;25(6):1687. doi: 10.3390/s25061687.
Hand gesture recognition (HGR) is a convenient and natural form of human-computer interaction. It is suitable for various applications. Much research has already focused on wearable device-based HGR. By contrast, this paper gives an overview focused on device-free HGR. That means we evaluate HGR systems that do not require the user to wear something like a data glove or hold a device. HGR systems are explored regarding technology, hardware, and algorithms. The interconnectedness of timing and power requirements with hardware, pre-processing algorithm, classification, and technology and how they permit more or less granularity, accuracy, and number of gestures is clearly demonstrated. Sensor modalities evaluated are WIFI, vision, radar, mobile networks, and ultrasound. The pre-processing technologies stereo vision, multiple-input multiple-output (MIMO), spectrogram, phased array, range-doppler-map, range-angle-map, doppler-angle-map, and multilateration are explored. Classification approaches with and without ML are studied. Among those with ML, assessed algorithms range from simple tree structures to transformers. All applications are evaluated taking into account their level of integration. This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. Our survey aims to provide a thorough understanding of the current state of the art in device-free HGR on edge devices and in general. Finally, on the basis of present-day challenges and opportunities in this field, we outline which further research we suggest for HGR improvement. Our goal is to promote the development of efficient and accurate gesture recognition systems.
手势识别（HGR）是一种便捷且自然的人机交互形式。它适用于各种应用。许多研究已经聚焦于基于可穿戴设备的手势识别。相比之下，本文给出了一个聚焦于无设备手势识别的概述。这意味着我们评估的手势识别系统不需要用户佩戴诸如数据手套之类的东西或手持设备。从技术、硬件和算法方面对手势识别系统进行了探索。清晰地展示了时间和功率要求与硬件、预处理算法、分类以及技术之间的相互联系，以及它们如何允许或多或少的粒度、准确性和手势数量。所评估的传感器模式有WIFI、视觉、雷达、移动网络和超声波。对预处理技术立体视觉、多输入多输出（MIMO）、频谱图、相控阵、距离-多普勒图、距离-角度图、多普勒-角度图和多边定位进行了探索。研究了有无机器学习的分类方法。在有机器学习的方法中，评估的算法范围从简单的树结构到变压器。所有应用都根据其集成水平进行评估。这包括确定所展示的应用是否适合边缘集成、其实时能力、是否实施了持续学习、实现了何种鲁棒性、是否应用了机器学习以及准确性水平。我们的调查旨在全面了解边缘设备上以及总体上无设备手势识别的当前技术现状。最后，基于该领域当前的挑战和机遇，我们概述了为改进手势识别建议开展的进一步研究。我们的目标是推动高效且准确的手势识别系统的发展。
