Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.

WiFall: Device-Free Fall Detection by Wireless Networks

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

/pdf/digital-twin-values-challenges-and-enablers-from-a-modeling-3hpoelaogn.pdf

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.

WiFi Sensing with Channel State Information: A Survey

Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

https://dl.acm.org/doi/pdf/10.1145/3241539.3241548

Towards Environment Independent Device Free Human Activity Recognition

With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

Wireless Sensing for Human Activity: A Survey

Most recently, gesture recognition has increasingly attracted intense academic and industrial interest due to its various applications in daily life, such as home automation, mobile games. Present approaches for gesture recognition, mainly including vision-based, sensor-based and RF-based, all have certain limitations which hinder their practical use in some scenarios. For example, the vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. To address these, we propose WiG in this paper, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. We implemented WiG in indoor environment and conducted experiments to evaluate its performance in two typical scenarios. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario.

WiG: WiFi-Based Gesture Recognition System

The invention discloses an automatic gesture recognizing method and system based on the wireless network signal transmission technology The method comprises the following steps that firstly, a wireless receiving end receives a wireless signal from a wireless transmitting end and assesses channel state information; the changing abnormality of the channel state information is recognized through an abnormality detection algorithm; the gesture action is recognized based on an SVM algorithm capable of distinguishing a plurality of target action types and with the mode that the human body gesture action causes the channel state information change as the target action type, and if recognition can be achieved, the definition of the gesture can be answered According to the recognizing method, the false alarm rate is low; an existing wireless network and an existing device are utilized, other specific detecting devices do not need to be installed, and high popularity is achieved Meanwhile, a detected person does not need to carry any additional sensing devices, and inconvenience caused when the detected person carries the detecting devices is avoided

Automatic gesture recognizing method and system

The invention relates to an information processing technology, and discloses a gesture recognition system based on RFID (radio frequency identification devices). The gesture recognition system comprises an RFID signal information acquisition module used for receiving a wireless signal from a reader by a transponder, and preprocessing an RFID signal; an anomaly detection and extraction module used for using an anomaly detection algorithm to recognize and extract abnormal changes of RFID signal state information; an action recognition module used for recognizing a gesture action by taking an abnormal segment caused by the gesture action as a target action class based on a matching algorithm; and a response recognition module used for executing a meaning that the gesture represents when recognizing a target gesture action. The gesture recognition method and system based on the RFID provided by the invention have the following beneficial effects that the universality is very high, and people subjected to detection do not need to carry any extra equipment, so that the inconvenience caused by carrying detection equipment by people subjected to detection can be avoided.

Gesture recognition method and system based on RFID (radio frequency identification devices)

The invention relates to information processing techniques. Disclosed is an RFID-based gesture recognition system, comprising: an RFID signal information acquisition module configured to enable a transponder to receive a wireless signal from a reader and to perform pre-processing on an RFID signal; an abnormality detection and extraction module configured to use an abnormality detection algorithm to identify and extract an abnormality in a variation of RFID signal state information; a motion recognition module configured to recognize a gesture by classifying an abnormal segment caused by the gesture as being a target motion category on the basis of a matching algorithm; and a response recognition module configured to execute on the basis of a meaning expressed by the gesture upon recognition of the gesture. The invention has a wide range of applications. Moreover, a person to be detected does not need to carry any additional detection apparatus, thereby saving the person from inconvenience in carrying a detection apparatus.

He Wenfeng

Papers

WiG: WiFi-Based Gesture Recognition System

Automatic gesture recognizing method and system

Gesture recognition method and system based on RFID (radio frequency identification devices)

Rfid-based gesture recognition method and system