scholarly journals Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8404
Author(s):  
Zhanjun Hao ◽  
Daiyang Zhang ◽  
Xiaochao Dang ◽  
Gaoyuan Liu ◽  
Yanhong Bai
Keyword(s):  
Human Activity ◽  
Low Cost ◽  
Recognition Rate ◽  
Ensemble Classification ◽  
Contactless Method ◽  
Robust Solution ◽  
Sensing Technology ◽  
New Ideas ◽  
Cas A ◽  
Activity Sensing

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

scholarly journals Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Zhao ◽  
Rennong Yang ◽  
Guillaume Chevalier ◽  
Ximeng Xu ◽  
Zhenxing Zhang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Network Architecture ◽  
Short Term Memory ◽  
Wearable Sensors ◽  
Recognition Rate ◽  
Human Activity Recognition ◽  
Time Direction ◽  
Positive Time ◽  
New Ideas

Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate. When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.

scholarly journals HUMAN ACTIVITY RECOGNITION BASED ON SMARTPHONE SENSOR DATA USING CNN

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 263-265
Author(s):  
K. İsmail ◽  
K. Özacar
Keyword(s):  
Human Activity ◽  
Low Cost ◽  
Daily Activities ◽  
Sensor Data ◽  
Android Application ◽  
Old People ◽  
Model Accuracy ◽  
Sensing Technology ◽  
Smartphone Technology ◽  
Smartphone Sensor

Abstract. Human activity recognitions have been widely used nowadays by end users thanks to extensive usage of smartphones. Smartphones, by self-containing low-cost sensing technology, can track our daily activities for serving healthcare, sport, interactive AR/VR games and so on. However, smartphone technology is evolving and the techniques of using the data that smartphones go through are also improving. In this study, we used built-in sensing technologies (accelerometer and gyroscope) available in nearly every smartphone to detect the most common 5 daily activities of human by taking the data of these sensors and extract the features for a Convolutional Neural Network (CNN) model. We prepare a dataset and use TensorFlow to train the collected data from the sensors then filtered it to be processed. We also discuss the differences in CNN model accuracy with different optimizers. To demonstrate the model, we developed an android application that successfully predict an activity. We believe that after improving this application, it can be used for especially lonely old people to immediately warn authorities in case of any daily incidents.

scholarly journals Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1715
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Claudio Turchetti
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Low Cost ◽  
Low Complexity ◽  
Human Activity Recognition ◽  
Motion Artifacts ◽  
Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

Enhanced Sensing and Activity Recognition System Using IoT for Healthcare

2021 ◽  
Vol 13 (2) ◽  
pp. 42-49
Author(s):  
Yamini G. ◽  
Gopinath Ganapathy
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Healthcare Services ◽  
Recognition System ◽  
Physical Activities ◽  
Video Data ◽  
Large Coupling ◽  
Sensing Technology ◽  
Medical Benefits ◽  
Primary Focus

Through the integration of advanced algorithms and smart sensing technology in healthcare services, huge medical benefits could be gained by the aged and sick people in determining their activity recognition. Human activity recognition (HAR) is still in the research for the past decades that promotes recognition of physical activities automatically. The main aim of HAR is to obtain and analyze the physical activities of a person, which could be promoted through several in-built sensors examined in the form of video data. Through this technique, necessary information could be obtained that also helps in preventing significant risks and also averts or alerts unfortunate events from happening. However, there is no particular categorization for human activity, and there is no description of the particular events to occur. The objective of this paper is to propose a healthcare information system based on IoT where enhancing activity recognition is the primary focus. Human activities are supposed to be diverse; it is necessary to choose appropriate sensors and the effective placement of those sensors in recognizing specific activities. One of the major challenges here is choosing the appropriate sensor for that particular instance and gathering data under particular circumstances. Due to the large coupling of sensors and their activity monitoring functionality, the solution to promote feasibility for the HAR predicament cannot be determined. A distinguishing feature of this paper is that it includes future users' perspectives.

Lignin-based polymers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fan Qi ◽  
Zhang Chaoqun ◽  
Yang Weijun ◽  
Wang Qingwen ◽  
Ou Rongxian
Keyword(s):  
High Performance ◽  
Raw Materials ◽  
Structural Characteristics ◽  
Low Cost ◽  
Polymer Matrices ◽  
Terrestrial Plants ◽  
Polyester Resins ◽  
Sustainable Resources ◽  
Reinforced Polymer ◽  
New Ideas

Abstract On the basis of the world’s continuing consumption of raw materials, there was an urgent need to seek sustainable resources. Lignin, the second naturally abundant biomass, accounts for 15–35% of the cell walls of terrestrial plants and is considered waste for low-cost applications such as thermal and electricity generation. The impressive characteristics of lignin, such as its high abundance, low density, biodegradability, antioxidation, antibacterial capability, and its CO2 neutrality and enhancement, render it an ideal candidate for developing new polymer/composite materials. In past decades, considerable works have been conducted to effectively utilize waste lignin as a component in polymer matrices for the production of high-performance lignin-based polymers. This chapter is intended to provide an overview of the recent advances and challenges involving lignin-based polymers utilizing lignin macromonomer and its derived monolignols. These lignin-based polymers include phenol resins, polyurethane resins, polyester resins, epoxy resins, etc. The structural characteristics and functions of lignin-based polymers are discussed in each section. In addition, we also try to divide various lignin reinforced polymer composites into different polymer matrices, which can be separated into thermoplastics, rubber, and thermosets composites. This chapter is expected to increase the interest of researchers worldwide in lignin-based polymers and develop new ideas in this field.

scholarly journals Light People: Professor Aydogan Ozcan

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tingting Sun
Keyword(s):  
Deep Learning ◽  
Los Angeles ◽  
Learning Algorithm ◽  
Computational Imaging ◽  
Technological Innovations ◽  
Image Generation ◽  
Sensing Technology ◽  
Reconstruction Methods ◽  
New Ideas ◽  
The University

EditorialIn 2016, the news that Google’s artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor’s Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.

Novel low-cost vision-sensing technology with controllable of exposal time for welding

2005 ◽  
Author(s):  
Wenzeng Zhang ◽  
Bin Wang ◽  
Nian Chen ◽  
Yipeng Cao
Keyword(s):  
Low Cost ◽  
Vision Sensing ◽  
Sensing Technology

scholarly journals A Low Cost Vision-Based Road-Following System for Mobile Robots

2018 ◽  
Vol 8 (9) ◽  
pp. 1635 ◽  
Author(s):  
Haojie Zhang ◽  
David Hernandez ◽  
Zhibao Su ◽  
Bo Su
Keyword(s):  
Mobile Robots ◽  
Unscented Kalman Filter ◽  
Detection System ◽  
Tracking System ◽  
Low Cost ◽  
Recognition Rate ◽  
Road Detection ◽  
The Road ◽  
Road Following ◽  
Following Error

Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.

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