The world is getting older by the minute due to rising life expectancy, leading to an urgent need for the continuous monitoring of patients. Tracking human activities without hospitalization has been tackled in the past decade thanks to the advancement of sensing technologies and wearable devices. However, the limited power resources of microcontrollers and the power consumption due to the use of different sensors are two issues that make the recognition process via embedded systems an open research topic to date. Consequently, this paper proposes a low-cost machine learning-based human activity recognition algorithm. It detects cyclic activities from wrist-worn tri-axial accelerometer data and classifies them into four classes. Specifically, a novel and smart peak detection technique is proposed, followed by the extraction of small handcrafted feature vectors, representing the input of a novel machine-learning architecture. These three contributions are approved by experimental results on a 3300-file dataset, showing an accuracy of 99.21% with a low computational cost thanks to an efficient implementation for feature extraction. The effectiveness of the proposed recognition process is validated in real world conditions.

中文翻译：

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表征加速度信号中的峰值——使用可穿戴传感器进行身体活动检测的应用

由于预期寿命的延长，世界每分钟都在变老，因此迫切需要对患者进行持续监测。由于传感技术和可穿戴设备的进步，在过去十年中已经解决了无需住院即可跟踪人类活动的问题。然而，微控制器有限的电源资源和由于使用不同传感器而导致的功耗是两个问题，这两个问题使得通过嵌入式系统的识别过程成为迄今为止一个开放的研究课题。因此，本文提出了一种低成本的基于机器学习的人体活动识别算法。它从腕戴式三轴加速度计数据中检测循环活动，并将它们分为四类。具体而言，提出了一种新颖且智能的峰值检测技术，然后提取手工制作的小特征向量，代表新机器学习架构的输入。这三个贡献得到了在 3300 个文件数据集上的实验结果的认可，由于特征提取的高效实现，显示了 99.21% 的准确率和低计算成本。提议的识别过程的有效性在现实世界条件下得到验证。