Human Sensing via Passive Spectrum Monitoring

Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability

Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges

Reconfigurable intelligent surfaces (RISs) have flexible and exceptional performance in manipulating electromagnetic waves and customizing wireless channels. These capabilities enable them to provide a plethora of valuable activity-related information for promoting wireless human sensing. In this article, we present a comprehensive review of passive human sensing using radio frequency signals with the assistance of RISs. Specifically, we first introduce fundamental principles and physical platform of RISs. Subsequently, based on the specific applications, we categorize the state-of-the-art human sensing techniques into three types, including human imaging,localization, and activity recognition. Meanwhile, we would also investigate the benefits that RISs bring to these applications. Furthermore, we explore the application of RISs in human micro-motion sensing, and propose a vital signs monitoring system enhanced by RISs. Experimental results are presented to demonstrate the promising potential of RISs in sensing vital signs for manipulating individuals. Finally, we discuss the technical challenges and opportunities in this field

Human Sensing Expansion for a Rolling Robot

For the past twenty years, fans of the Star Wars movies have been building their own replica robots or “droids” that they fell in love with on the big screen. This project takes an existing design for the BB-8 droid and integrates sensors that can detect people and then interact with them. This design uses six IR sensors and one main 8x8 IR sensor grid to be able to detect the closest person and turn BB- 8’s dome towards them as though they are looking at them

Toward an Automatic Road Accessibility Information Collecting and Sharing Based on Human Behavior Sensing Technologies of Wheelchair Users

AbstractThis research proposes a methodology for digitizing street level accessibility with human sensing of wheelchair users. The dig- itization of street level accessibility is essential to develop accessibility maps or to personalize a route considering accessibility. However, current digitization methodologies are not sufficient because it requires a lot of manpower and therefore money and time cost. The proposed method makes it possible to digitize the accessibility semi-automatically. In this research, a three-axis accelerometer embedded on iPod touch sensed actions of nine wheelchair users across the range of disabilities and aged groups, in Tokyo, approximately 9hours. This paper reports out attempts to estimate both environmental factors: the status of street and subjective factors: driver's fatigue from human sensing data using machine learning

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior