Static and Dynamic Activity Detection with Ambient Sensors in Smart Spaces.

Sagar Shelke,Baris Aksanli

doi:10.3390/s19040804

Abstract

Convergence of Machine Learning, Internet of Things, and computationally powerful single-board computers has boosted research and implementation of smart spaces. Smart spaces make predictions based on historical data to enhance user experience. In this paper, we present a low-cost, low-energy smart space implementation to detect static and dynamic human activities that require simple motions. We use low-resolution (4 × 16) and non-intrusive thermal sensors to collect data. We train six machine learning algorithms, namely logistic regression, naive Bayes, support vector machine, decision tree, random forest and artificial neural network (vanilla feed-forward) on the dataset collected in our lab. Our experiments reveal a very high static activity detection rate with all algorithms, where the feed-forward neural network method gives the best accuracy of 99.96%. We also show how data collection methods and sensor placement plays an important role in the resulting accuracy of different machine learning algorithms. To detect dynamic activities in real time, we use cross-correlation and connected components of thermal images. Our smart space implementation, with its real-time properties, can be used in various domains and applications, such as conference room automation, elderly health-care, etc.

Highlights

Smart spaces have gained significant attention over the last several years due to advancements in the sensor technology, decreasing cost of hardware and ease of deployment
We demonstrated the potential use of low resolution thermal sensors for static and dynamic human activity detection in smart environments
We compared the performance of six machine learning algorithms based on class-wise accuracy and F1 score metrics

Summary

Introduction

Smart spaces have gained significant attention over the last several years due to advancements in the sensor technology, decreasing cost of hardware and ease of deployment. These spaces combine small and efficient hardware with data management mechanisms to provide solutions in various domains including health-care, wellness, education, etc. Smart spaces differ from traditional environments because of constant interactions between the users and sensing elements. One important research topic within smart spaces is human activity detection, due to its applications in robotics and human computer interaction (HCI). There are several methods to perform activity detection: using multimedia-sources (such as audio/video), wearable devices (smart watches, wristbands, etc.), and ambient sensing. Every method has its advantage and disadvantage based on its use cases

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Conclusion