On the lap of this modern era, human activity recognition (HAR) has been of great help in case of health monitoring and rehabilitation. Existing works mostly use one or more specific devices (with embedded sensors) including smartphones for activity recognition and most of the time the detected activities are coarse grained like sit or walk rather than detailed and intensive like sit carrying weight or walk carrying weight. But, intensity of activities reflects valuable insight about a person’s health and more importantly, physical exertion for performing those activities. Consequently, in this paper, we propose an intense activity recognition framework that combines features from smartphone accelerometer (available in almost every smartphone) and that from wearable heartrate sensor. We introduce a set of novel heartrate features that takes into consideration finer variation of heartrate as compared to the resting heartrate of an individual. The proposed framework forms an ensemble model based on different classifiers to address the challenge of usage behavior in terms of how the smartphone is carried. The stack generalization based ensemble model predicts the intensity of activity. We have implemented the framework and tested for a real dataset collected from four users. We have observed that our work is able to identify both static and dynamic intense activities with 96% accuracy, and even found to be better than state of the art techniques.

在这个现代时代，人类活动识别（HAR）在健康监测和康复方面大有帮助。现有作品大多使用一种或多种特定设备（带有嵌入式传感器）（包括智能手机）来进行活动识别，并且大多数情况下，检测到的活动像坐着或走路那样粗糙，而不是像坐着重量或步行着重量那样详尽而密集。但是，活动的强度反映出有关一个人的健康的宝贵见解，更重要的是，反映出进行这些活动的体力消耗。因此，在本文中，我们提出了一个激烈的活动识别框架，该框架结合了智能手机加速度计（几乎每个智能手机都可用）和可穿戴心率传感器的功能。我们介绍了一组新颖的心率特征，这些特征考虑了与个人静息心率相比更好的心率变化。所提出的框架基于不同的分类器形成一个整体模型，以解决智能手机如何携带方面的使用行为挑战。基于堆栈概括的集成模型可预测活动的强度。我们已经实施了该框架，并测试了从四个用户那里收集的真实数据集。我们观察到，我们的工作能够以96％的准确度识别静态和动态激烈的活动，甚至发现它比最新技术要好。