scholarly journals High Accuracy WiFi-Based Human Activity Classification System with Time-Frequency Diagram CNN Method for Different Places

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3797
Author(s):  
Lokesh Sharma ◽  
Chunghao Chao ◽  
Shih-Lin Wu ◽  
Mei-Chen Li
Keyword(s):  
Fall Detection ◽  
Human Action ◽  
Wearable Devices ◽  
Channel State ◽  
State Information ◽  
Time Frequency ◽  
Detection Systems ◽  
Average Accuracy ◽  
Privacy Issues ◽  
Frequency Diagram

Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitable part of aging. Many different fall detection systems have been developed to help people avoid falling. However, traditional systems based on wearable devices or image recognition-based have many disadvantages, such as user-unfriendly, privacy issues. Recently, WiFi-based fall detection systems try to solve the above problems. However, there is a common problem of reduced accuracy. Since the system is trained at the original signal collecting/training place, however, the application is at a different place. The proposed solution only extracts the features of the changed signal, which is caused by a specific human action. To implement this, we used Channel State Information (CSI) to train Convolutional Neural Networks (CNNs) and further classify the action. We have designed a prototype to test the performance of our proposed method. Our simulation results show an average accuracy of same place and different place is 93.2% and 90.3%, respectively.

scholarly journals WmFall: WiFi-based multistage fall detection with channel state information

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880571 ◽  
Author(s):  
Xu Yang ◽  
Fangyuan Xiong ◽  
Yuan Shao ◽  
Qiang Niu
Keyword(s):  
Channel State Information ◽  
Learning Algorithm ◽  
Detection System ◽  
Fall Detection ◽  
Special Method ◽  
Special Equipment ◽  
Channel State ◽  
State Information ◽  
Detection Systems ◽  
Deep Learning Algorithm

Traditional fall detection systems require to wear special equipment like sensors or cameras, which often brings the issues of inconvenience and privacy. In this article, we introduce a novel multistage fall detection system using the channel state information from WiFi devices. Our work is inspired by the fact that different actions have different effects on WiFi signals. By fully analyzing and exploring the channel state information characters, the falling actions can be distinguished from other movements. Considering that falling and sitting are very similar to each other, a special method is designed for distinguishing them with deep learning algorithm. Finally, the fall detection system is evaluated in a laboratory, which has 89% detection precision with false alarm rate of 8% on the average.

scholarly journals A Survey of User Authentication Based on Channel State Information

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhengjie Wang ◽  
Wenwen Dou ◽  
Mingjing Ma ◽  
Xiaoxue Feng ◽  
Zehua Huang ◽  
...  
Keyword(s):  
Channel State Information ◽  
User Authentication ◽  
Human Action ◽  
Fundamental Principle ◽  
Accurate Information ◽  
Channel State ◽  
Research Directions ◽  
State Information ◽  
Device Free ◽  
Open Issues

Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device-free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state-of-the-art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.

scholarly journals A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1831
Author(s):  
Armando Collado-Villaverde ◽  
Mario Cobos ◽  
Pablo Muñoz ◽  
David F. Barrero
Keyword(s):  
Machine Learning ◽  
Elderly Population ◽  
Real Data ◽  
Fall Detection ◽  
Wearable Devices ◽  
Accelerometer Data ◽  
Detection Systems ◽  
Detection Software ◽  
Health Complications ◽  
Public Datasets

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

scholarly journals Possible Life Saver: A Review on Human Fall Detection Technology

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Zhuo Wang ◽  
Vignesh Ramamoorthy ◽  
Udi Gal ◽  
Allon Guez
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Detection Systems ◽  
Detection Technology ◽  
Ambient Systems ◽  
Measure Of Performance ◽  
Privacy Issues ◽  
Current Systems

Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

scholarly journals Machine learning algorithms for fall detection using kinematic and heart rate parameters-a comprehensive analysis

2020 ◽  
Vol 9 (4) ◽  
pp. 772
Author(s):  
Anita Ramachandran ◽  
Adarsh Ramesh ◽  
Aditya Sukhlecha ◽  
Avtansh Pandey ◽  
Anupama Karuppiah
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Feature Selection ◽  
Fall Detection ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Detection Systems ◽  
Ensemble Techniques ◽  
The Impact

The application of machine learning techniques to detect and classify falls is a prominent area of research in the domain of intelligent assisted living systems. Machine learning (ML) based solutions for fall detection systems built on wearable devices use various sources of information such inertial motion units (IMU), vital signs, acoustic or channel state information parameters. Most existing research rely on only one of these sources; however, a need to do more experimenation to observe the efficiency of the ML classifiers while coupling features from diverse sources, was felt. In addition, fall detection systems based on wearable devices, require intelligent feature engineering and selection for dimensionality reduction, so as to reduce the computational complexity of the devices. In this paper we do a comprehensive performance analysis of ML classifiers for fall detection, on a dataset we collected. The analysis includes the impact of the following aspects on the performance of ML classifiers for fall detection: (i) using a combination of features from 2 sensors-an IMU sensor and a heart rate sensor, (ii) feature engineering and feature selection based on statistical methods, and (iii) using ensemble techniques for fall detection. We find that the inclusion of heart rate along with IMU sensor parameters improves the accuracy of fall detection. The conclusions from our experimentations on feature selection and ensemble analysis can serve as inputs for researchers designing wearable device-based fall detection systems.

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