We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

中文翻译：

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用于人类活动识别和医疗保健的可穿戴物联网平台

我们建议执行基于可穿戴传感器的人体活动识别。这进一步扩展到物联网 (IoT) 平台，该平台基于集成了可穿戴传感器、智能手机和活动识别的基于 Web 的应用程序。为此，智能手机从可穿戴传感器收集数据并将其发送到服务器以进行身体活动的处理和识别。我们收集了一个包含 13 项室内和室外体育活动的新数据集。参与者来自男女，每次活动的人数各不相同。在这些活动中，可穿戴传感器通过加速度计、陀螺仪、磁力计、压力和温度测量各种身体参数。然后，这些测量值及其统计数据以特征向量表示，这些向量用于训练和测试监督式机器学习算法（分类器）以进行活动识别。在给定的数据集上，我们使用 WEKA 机器学习套件评估了许多广为人知的分类器，例如随机森林、支持向量机和许多其他分类器。使用 WEKA 中这些分类器的默认设置，我们获得了 90% 的最高总体分类准确率。因此，在所提出的平台中使用这样的识别率是令人鼓舞的、可靠的和有效的。使用 WEKA 中这些分类器的默认设置，我们获得了 90% 的最高总体分类准确率。因此，在所提出的平台中使用这样的识别率是令人鼓舞的、可靠的和有效的。使用 WEKA 中这些分类器的默认设置，我们获得了 90% 的最高总体分类准确率。因此，在所提出的平台中使用这样的识别率是令人鼓舞的、可靠的和有效的。