scholarly journals Transition Activity Recognition System Based on Standard Deviation Trend Analysis

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3117
Author(s):  
Junhao Shi ◽  
Decheng Zuo ◽  
Zhan Zhang
Keyword(s):  
Standard Deviation ◽  
Trend Analysis ◽  
Activity Recognition ◽  
Human Activity ◽  
Real Data ◽  
Recognition System ◽  
Electromechanical Systems ◽  
Transition Activities ◽  
Overall Performance ◽  
Transition Activity

With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

scholarly journals Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

2021 ◽  
Author(s):  
Muhammad Muaaz ◽  
Ali Chelli ◽  
Martin Wulf Gerdes ◽  
Matthias Pätzold
Keyword(s):  
Neural Network ◽  
Information Systems ◽  
Convolutional Neural Network ◽  
Health Information ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Information Systems ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Ratio Method

AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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.

Edge-based Human Activity Recognition System for Smart Healthcare

2021 ◽  
Author(s):  
Anirban Mukherjee ◽  
Amitrajit Bose ◽  
Debdeep Paul Chaudhuri ◽  
Akash Kumar ◽  
Aiswarya Chatterjee ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Smart Healthcare ◽  
Edge Based

scholarly journals Human Activity Recognition using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3553-3556
Author(s):  
Chaudhari Shraddha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Random Forest Classification ◽  
Medical Health Care

Activity recognition in humans is one of the active challenges that find its application in numerous fields such as, medical health care, military, manufacturing, assistive techniques and gaming. Due to the advancements in technologies the usage of smartphones in human lives has become inevitable. The sensors in the smartphones help us to measure the essential vital parameters. These measured parameters enable us to monitor the activities of humans, which we call as human activity recognition. We have applied machine learning techniques on a publicly available dataset. K-Nearest Neighbors and Random Forest classification algorithms are applied. In this paper, we have designed and implemented an automatic human activity recognition system that independently recognizes the actions of the humans. This system is able to recognize the activities such as Laying, Sitting, Standing, Walking, Walking downstairs and Walking upstairs. The results obtained show that, the KNN and Random Forest Algorithms gives 90.22% and 92.70% respectively of overall accuracy in detecting the activities.

scholarly journals Construction and Application of Indoor Video Surveillance System Based on Human Activity Recognition

2018 ◽  
Vol 232 ◽  
pp. 04024
Author(s):  
Yuchen Wang ◽  
Mantao Wang ◽  
Zhouyu Tan ◽  
Jie Zhang ◽  
Zhiyong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Activity Recognition ◽  
Human Activity ◽  
Monitoring Network ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Convolution Neural Network ◽  
Learning Technology ◽  
Building Monitoring

With the growth of building monitoring network, increasing human resource and funds have been invested into building monitoring system. Computer vision technology has been widely used in image recognition recently, and this technology has also been gradually applied to action recognition. There are still many disadvantages of traditional monitoring system. In this paper, a human activity recognition system which based on the convolution neural network is proposed. Using the 3D convolution neural network and the transfer learning technology, the human activity recognition engine is constructed. The Spring MVC framework is used to build the server end, and the system page is designed in HBuilder. The system not only enhances efficiency and functionality of building monitoring system, but also improves the level of building safety.

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