Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions

Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3377882 ◽

2020 ◽

Vol 16 (2) ◽

pp. 1-19

Author(s):

Farzan Majeed Noori ◽

Michael Riegler ◽

Md Zia Uddin ◽

Jim Torresen

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Multiple Sensors

Deep Modular Multimodal Fusion on Multiple Sensors for Volcano Activity Recognition

Machine Learning and Knowledge Discovery in Databases - Lecture Notes in Computer Science ◽

10.1007/978-3-030-10997-4_37 ◽

2019 ◽

pp. 602-617

Author(s):

Hiep V. Le ◽

Tsuyoshi Murata ◽

Masato Iguchi

Keyword(s):

Activity Recognition ◽

Multimodal Fusion ◽

Multiple Sensors

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

Sensors ◽

10.3390/s20185114 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5114 ◽

Cited By ~ 2

Author(s):

Qin Ni ◽

Zhuo Fan ◽

Lei Zhang ◽

Chris D. Nugent ◽

Ian Cleland ◽

...

Keyword(s):

Activity Recognition ◽

Wearable Sensors ◽

Multiple Sensors ◽

Machine Learning Methods ◽

Research Fields ◽

Sit To Stand ◽

Significant Performance ◽

Conventional Machine ◽

Public Datasets ◽

Deep Learning Model

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.

Physical activity recognition using multiple sensors embedded in a wearable device

ACM Transactions on Embedded Computing Systems ◽

10.1145/2423636.2423644 ◽

2013 ◽

Vol 12 (2) ◽

pp. 1-14 ◽

Cited By ~ 22

Author(s):

Yunyoung Nam ◽

Seungmin Rho ◽

Chulung Lee

Keyword(s):

Physical Activity ◽

Activity Recognition ◽

Wearable Device ◽

Multiple Sensors ◽

Physical Activity Recognition

Multi-Sensor Fusion for Activity Recognition—A Survey

Sensors ◽

10.3390/s19173808 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3808 ◽

Cited By ~ 11

Author(s):

Antonio A. Aguileta ◽

Ramon F. Brena ◽

Oscar Mayora ◽

Erik Molino-Minero-Re ◽

Luis A. Trejo

Keyword(s):

Sensor Fusion ◽

Activity Recognition ◽

Sports Medicine ◽

Good Practice ◽

Sensor Data ◽

Personal Safety ◽

Multiple Sensors ◽

Concurrent Use ◽

Fusion Methods ◽

The Many

In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.

An overview of human activity recognition based on smartphone

Sensor Review ◽

10.1108/sr-11-2017-0245 ◽

2019 ◽

Vol 39 (2) ◽

pp. 288-306 ◽

Cited By ~ 7

Author(s):

Guan Yuan ◽

Zhaohui Wang ◽

Fanrong Meng ◽

Qiuyan Yan ◽

Shixiong Xia

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Sequence Data ◽

Ground Truth ◽

Human Activity Recognition ◽

Data Sets ◽

Ground Truth Data ◽

Content Type ◽

Multiple Sensors ◽

Model Training

Purpose Currently, ubiquitous smartphones embedded with various sensors provide a convenient way to collect raw sequence data. These data bridges the gap between human activity and multiple sensors. Human activity recognition has been widely used in quite a lot of aspects in our daily life, such as medical security, personal safety, living assistance and so on. Design/methodology/approach To provide an overview, the authors survey and summarize some important technologies and involved key issues of human activity recognition, including activity categorization, feature engineering as well as typical algorithms presented in recent years. In this paper, the authors first introduce the character of embedded sensors and dsiscuss their features, as well as survey some data labeling strategies to get ground truth label. Then, following the process of human activity recognition, the authors discuss the methods and techniques of raw data preprocessing and feature extraction, and summarize some popular algorithms used in model training and activity recognizing. Third, they introduce some interesting application scenarios of human activity recognition and provide some available data sets as ground truth data to validate proposed algorithms. Findings The authors summarize their viewpoints on human activity recognition, discuss the main challenges and point out some potential research directions. Originality/value It is hoped that this work will serve as the steppingstone for those interested in advancing human activity recognition.

Human Activity Recognition and Location Based on Temporal Analysis

Journal of Engineering ◽

10.1155/2018/4752191 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Hongjin Ding ◽

Faming Gong ◽

Wenjuan Gong ◽

Xiangbing Yuan ◽

Yuhui Ma

Keyword(s):

Real Time ◽

Activity Recognition ◽

Human Activity ◽

Temporal Analysis ◽

Human Activity Recognition ◽

Detection Accuracy ◽

Production Plant ◽

Surveillance Video ◽

Multiple Sensors ◽

Time Performance

Current methods of human activity recognition face many challenges, such as the need for multiple sensors, poor implementation, unreliable real-time performance, and lack of temporal location. In this research, we developed a method for recognizing and locating human activities based on temporal action recognition. For this work, we used a multilayer convolutional neural network (CNN) to extract features. In addition, we used refined actionness grouping to generate precise region proposals. Then, we classified the candidate regions by employing an activity classifier based on a structured segmented network and a cascade design for end-to-end training. Compared with previous methods of action classification, the proposed method adds the time boundary and effectively improves the detection accuracy. To test this method empirically, we conducted experiments utilizing surveillance video of an offshore oil production plant. Three activities were recognized and located in the untrimmed long video: standing, walking, and falling. The accuracy of the results proved the effectiveness and real-time performance of the proposed method, demonstrating that this approach has great potential for practical application.

Research on Construction Workers’ Activity Recognition Based on Smartphone

Sensors ◽

10.3390/s18082667 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2667 ◽

Cited By ~ 9

Author(s):

Mingyuan Zhang ◽

Shuo Chen ◽

Xuefeng Zhao ◽

Zhen Yang

Keyword(s):

Decision Tree ◽

Data Acquisition ◽

Activity Recognition ◽

Training Model ◽

Construction Workers ◽

Body Parts ◽

Complex Activity ◽

Multiple Sensors ◽

Angle Data ◽

Cart Algorithm

This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification.