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

Mäkelä, Satu-Marja; Lämsä, Arttu; Keränen, Janne S.; Liikka, Jussi; Ronkainen, Jussi; Peltola, Johannes; Häikiö, Juha; Järvinen, Sari; López, Miguel Bordallo

doi:10.3390/su14010220

Cited by 15 publications

(12 citation statements)

References 36 publications

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“…The boundary between these ranges is around 6 Hz for both ankle and hip. Commonly for this application, it is agreed that most discriminative components are contained in the frequency range below 6 Hz ( Mäkela et al, 2021 ). For that reason, many researchers now focus on the 0—6 Hz frequency range when pre-processing biomechanical gait data ( Luo et al, 2020 ; Renani et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Ding

Plummer²,

Georgilas³

2022

Front. Bioeng. Biotechnol.

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Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact moment/force) for use in sports injury science. Recognising that human locomotion is an inherently time-sequential and limb-synergetic behaviour, this study investigates models and learning algorithms for predicting the motion of a subject’s leg from the motion of complementary limbs. The novel deep learning model architectures proposed are based on the Long Short-Term Memory approach with the addition of an attention mechanism. A dataset comprising Inertial Measurement Unit signals from 21 subjects traversing varied terrains was used, including stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and the transitions between these conditions. Fourier Analysis is deployed to evaluate the model robustness, in addition to assessing time-based prediction errors. The experiment on three unseen test participants suggests that the branched neural network structure is preferred to tackle the multioutput problem, and the inclusion of an attention mechanism demonstrates improved performance in terms of accuracy, robustness and network size. An experimental comparison found that 57% of the model parameters were not needed after adding attention layers meanwhile the prediction error is lower than the LSTM model without attention mechanism. The attention model has errors of 9.06% and 7.64% (normalised root mean square error) for ankle and hip acceleration prediction respectively. Also, less high-frequency noise is present in the attention model predictions. We conclude that the internal structure of the proposed deep learning model is justified, principally the benefit of using an attention mechanism. Experimental results for biomechanical motion estimation are obtained, showing greater accuracy than only with LSTM. The trained attention model can be used throughout despite transitioning between terrain types. Such a model will be useful in, for example, the control of lower-limb prostheses, instead of the need to identify and switch between different trajectory generators for different walking modes.

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Section: Resultsmentioning

confidence: 99%

Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Ding

Plummer²,

Georgilas³

2022

Front. Bioeng. Biotechnol.

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“…It may be used to develop and evaluate HAR classifiers. Mäkela et al [48] introduced the VTT-ConIoT dataset, a publicly accessible dataset for evaluating HAR from inertial sensors in professional construction contexts. The collection comprises data from 13 people and 16 distinct actions and is gathered from three particular sensor sites on wearable devices.…”

Section: Available Dataset For Construction Worker Activitiesmentioning

confidence: 99%

“…The collection comprises data from 13 people and 16 distinct actions and is gathered from three particular sensor sites on wearable devices. The dataset is provisionally accessible on the Zenodo website [48]. It was compiled under controlled settings to portray actual construction operations and was built in collaboration with important building construction stakeholders.…”

Section: Available Dataset For Construction Worker Activitiesmentioning

confidence: 99%

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Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

Jitpattanakul¹

2023

Intelligent Automation &Amp; Soft Computing

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The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturing firm are vital for the rapid and accurate diagnosis of work performance, particularly during the training of a new worker. Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques. Despite widespread computer vision-based approaches, it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where camera deployment is problematic. Through the use of wearable inertial sensors, we propose a deep learning method for automatically recognizing the activities of construction workers. The suggested method incorporates a convolutional neural network, residual connection blocks, and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as construction worker tasks. The proposed approach has been evaluated using standard performance measures, such as precision, F1-score, and AUC, using a publicly available benchmark dataset known as VTT-ConIoT, which contains genuine construction work activities. In addition, standard deep learning models (CNNs, RNNs, and hybrid models) were developed in different empirical circumstances to compare them to the proposed model. With an average accuracy of 99.71% and an average F1-score of 99.71%, the experimental findings revealed that the suggested model could accurately recognize the actions of construction workers. Furthermore, we examined the impact of window size and sensor position on the identification efficiency of the proposed method.

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“…However, their system did not filter the raw data correctly. In [38], Mäkela et al established threefold contributions. First, they offered a publicly available dataset named VTT-ConloT.…”

Section: B Imu Sensors For Outdoor-environmentsmentioning

confidence: 99%

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.

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

Cited by 15 publications

References 36 publications

Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

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