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.

翻译：能够实现低延迟手势识别的微型设备在日常人机交互，尤其是在医疗监测领域的应用正日益普及。诸如跌倒检测、康复追踪和患者监护等嵌入式解决方案需要快速高效地追踪运动，同时避免误报。本研究提出了一种高效方案，阐述如何仅利用三轴加速度计传感器构建非常高效的运动识别模型。我们探索了AutoML流程从数据片段中提取最重要特征的能力。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。我们使用了WeBe Band——一款配备有足够强大微控制器（MCU）的多传感器可穿戴设备，能够完全在设备端有效执行手势识别。在所探索的模型中，我们发现神经网络在准确率、延迟和内存使用之间提供了最佳平衡。我们的结果还表明，WeBe Band能够实现可靠的实时手势识别，这对于需要安全快速响应时间的实时医疗监测解决方案具有巨大潜力。