scholarly journals Introducing VTT-ConIot: A Realistic Dataset for Activity Recognition of Construction Workers Using IMU Devices

2021 ◽  
Vol 14 (1) ◽  
pp. 220
Author(s):  
Satu-Marja Mäkelä ◽  
Arttu Lämsä ◽  
Janne S. Keränen ◽  
Jussi Liikka ◽  
Jussi Ronkainen ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Occupational Safety ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Well Being ◽  
Work Conditions ◽  
Human Activity Recognition ◽  
Wearable Sensor ◽  
Made In

Sustainable work aims at improving working conditions to allow workers to effectively extend their working life. In this context, occupational safety and well-being are major concerns, especially in labor-intensive fields, such as construction-related work. Internet of Things and wearable sensors provide for unobtrusive technology that could enhance safety using human activity recognition techniques, and has the potential of improving work conditions and health. However, the research community lacks commonly used standard datasets that provide for realistic and variating activities from multiple users. In this article, our contributions are threefold. First, we present VTT-ConIoT, a new publicly available dataset for the evaluation of HAR from inertial sensors in professional construction settings. The dataset, which contains data from 13 users and 16 different activities, is collected from three different wearable sensor locations.Second, we provide a benchmark baseline for human activity recognition that shows a classification accuracy of up to 89% for a six class setup and up to 78% for a sixteen class more granular one. Finally, we show an analysis of the representativity and usefulness of the dataset by comparing it with data collected in a pilot study made in a real construction environment with real workers.

scholarly journals Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6434
Author(s):  
Changjun Fan ◽  
Fei Gao
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Feature Selection Method ◽  
Feature Space ◽  
Human Activity Recognition ◽  
Wearable Electronics ◽  
Benchmark Datasets

The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.

scholarly journals Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 885 ◽  
Author(s):  
Zhongzheng Fu ◽  
Xinrun He ◽  
Enkai Wang ◽  
Jun Huo ◽  
Jian Huang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Joint Probability ◽  
Human Activity Recognition ◽  
Sensor Technology ◽  
Measurement Unit ◽  
Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

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 HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6927
Author(s):  
Xiaojuan Wang ◽  
Xinlei Wang ◽  
Tianqi Lv ◽  
Lei Jin ◽  
Mingshu He
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Search Algorithm ◽  
Wearable Sensors ◽  
Research Direction ◽  
Human Activity Recognition ◽  
Nsga Ii ◽  
Neural Architecture ◽  
Access Cost ◽  
Computation Speed

Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Well Being ◽  
Human Activity Recognition ◽  
Research Field ◽  
Daily Lives ◽  
Video Cameras ◽  
Advantages And Disadvantages

Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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