Smart watch activity recognition using plot image analysis

A. Alexan, Anca Alexan, S. Oniga
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Abstract

Nowadays, many of us wear multiple devices capable of acquiring and storing data related to our everyday activities. Since the computing power of mobile battery-operated devices slowly increases and the power optimizations allow for more and more continuous use, these devices are capable of not only monitoring our activity but analyzing the activity as well. Of these devices, the smartwatch is probably the most inconspicuous, and due to its widespread use, we have used accelerometer data gathered from a smartwatch to identify common user activities by using image generated plots and image recognition machine learning. By leveraging the.Net ML.NET machine learning framework we have managed to obtain a decent recognition rate.
基于情节图像分析的智能手表活动识别
如今,我们中的许多人都佩戴着多种设备,能够获取和存储与我们日常活动相关的数据。由于移动电池供电设备的计算能力缓慢增加,并且功率优化允许越来越多的连续使用,这些设备不仅能够监控我们的活动,还能够分析活动。在这些设备中,智能手表可能是最不显眼的,由于它的广泛使用,我们使用从智能手表收集的加速度计数据,通过使用图像生成图和图像识别机器学习来识别常见的用户活动。通过杠杆作用。Net ML.NET机器学习框架,我们已经设法获得了一个体面的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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