Abstract
The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded solutions such as fall detection, rehabilitation tracking, and patient supervision require fast and efficient tracking of movements while avoiding unwanted false alarms. This study presents an efficient solution on how to build very efficient motion-based models only using triaxial accelerometer sensors. We explore the capability of the AutoML pipelines to extract the most important features from the data segments. This approach also involves training multiple lightweight machine learning algorithms using the extracted features. We use WeBe Band, a multi-sensor wearable device that is equipped with a powerful enough MCU to effectively perform gesture recognition entirely on the device. Of the models explored, we found that the neural network provided the best balance between accuracy, latency, and memory use. Our results also demonstrate that reliable real-time gesture recognition can be achieved in WeBe Band, with great potential for real-time medical monitoring solutions that require a secure and fast response time.
Abstract (translated)
使用能够进行低延迟手势识别的微型设备在日常人机交互中，尤其是在医疗监测领域中正变得越来越受欢迎。嵌入式解决方案如跌倒检测、康复跟踪和患者监护需要快速且高效的运动追踪，并同时避免不必要的误报。本研究提出了一种有效的方法来构建仅利用三轴加速度计传感器的高度高效的动作模型。我们探索了自动机器学习（AutoML）管道从数据段中提取最重要的特征的能力。该方法还涉及使用提取的特征训练多个轻量级机器学习算法。 我们采用WeBe Band，这是一种配备了足够强大的MCU以完全在设备上执行手势识别的多传感器可穿戴设备。在研究的各种模型中，我们发现神经网络提供了准确度、延迟和内存使用的最佳平衡。我们的结果还表明，在WeBe Band中可以实现可靠且实时的手势识别，这为需要安全快速响应时间的实时医疗监测解决方案带来了巨大潜力。
URL
https://arxiv.org/abs/2512.00008
