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Accurate detection of sitting posture activities in a secure IoT based assisted living environment

机译：在基于安全物联网的辅助生活环境中准确检测坐姿活动

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In this work, we present a technique as well as a dataset for improving daily life assistive activities in a smart Internet of Things (IoT) driven environment. We propose that augmenting data from multiple sensing devices such as Microsoft Kinect and Smartwatches can significantly improve the detection performance once incorporated in the context of an IoT framework. Kinect, being the feature-wise richest input device in the IoT world, is a favorite pick of most of the researchers for detecting postural activities. However, there are certain activity classes in IoT based smart environments on which Kinect based solutions result in high misclassifications. This is due to the similarities in 3D joint position space. For such scenarios, Kinect must be augmented with additional sensor(s) to achieve the desired level of accuracy. In this work, we improve the detection of the assistive activities related to sitting posture in general and dining-related activities in particular. Our research focus is to enable a robot to understand the activities of a human at the dining table and plan the assistive tasks accordingly. This is a two-step process; in the first step, Kinect sensor data is augmented with a collection of motion sensors’ data. Then, this data is analyzed for discrimination power through cross-validation of the Hidden Markov Model (HMM). In addition, we propose a two-level security scheme consisting of key establishment and two-factor authentication for the IoT based activity recognition environment. Our experiments show that Kinect, when complemented by motion sensors’ data, reduces the confusion instances by up to 12% on average. Moreover, we demonstrate the data quality through clustering properties of the data using an unsupervised neural network.
机译：在这项工作中，我们提出了一种技术和数据集，用于在智能物联网（IoT）驱动的环境中改善日常生活辅助活动。我们建议，一旦结合到IoT框架的上下文中，从Microsoft Kinect和Smartwatches等多个传感设备中扩充数据可以显着提高检测性能。 Kinect是物联网世界中功能最丰富的输入设备，是大多数用于检测姿势活动的研究人员的最爱选择。但是，基于IoT的智能环境中存在某些活动类别，基于Kinect的解决方案会导致高度错误分类。这是由于3D关节位置空间的相似性。对于此类情况，必须使用其他传感器来增强Kinect，以实现所需的精度水平。在这项工作中，我们改善了对与坐姿有关的辅助活动的检测，尤其是在餐饮方面的活动。我们的研究重点是使机器人能够了解人在餐桌上的活动并相应地计划辅助任务。这是一个两步过程。第一步，通过运动传感器数据的集合来扩充Kinect传感器数据。然后，通过隐马尔可夫模型（HMM）的交叉验证来分析此数据的判别力。此外，针对基于IoT的活动识别环境，我们提出了一个由密钥建立和两因素身份验证组成的两级安全方案。我们的实验表明，通过运动传感器的数据进行补充，Kinect可以将混乱情况平均减少多达12％。此外，我们通过使用无监督神经网络对数据进行聚类来证明数据质量。

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