Feasibility analysis of unsupervised industrial activity recognition based on a frequent micro action

Azadi, Behrooz; Haslgrübler, Michael; Sopidis, Georgios; Murauer, Michaela; Anzengruber, Bernhard; Ferscha, Alois

doi:10.1145/3316782.3322749

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…We follow a windowing strategy to divide signals into smaller segments and extract several features so that, from each window, various features will be calculated. These features have been studied and used in the literature for HAR purposes [ 21 , 22 , 23 ]. The list of features is as follows: mean, standard deviation, root mean square, minimum, maximum, median, variance, median absolute deviation, the energy of the window and its auto-correlation mean crossing, 50 percent crossing, 25 percent crossing, 75 percent crossing of the window and its auto-correlation mean, the median of Power Spectrum of the window SMA: Signal Magnitude Area …”

Section: Methodsmentioning

confidence: 99%

“…Since we have only two classes, clustering algorithms will be initialized to detect two clusters of activities. In this experiment, we employ three different unsupervised learning algorithms: Gaussian Mixture Models (GMM), KMeans, and hierarchical clustering Ward’s method which is widely known and studied by other scholars [ 23 , 25 , 26 ]. In combination with two datasets from feature extraction (NFS and PCA), we will have six different models as the output of cluster analysis.…”

Section: Methodsmentioning

confidence: 99%

“…The window size and sliding rate selection have been discussed a lot in the HAR domain [ 21 , 23 ]. Choosing the right window size and sliding rate plays a critical role in the feature extraction to ensure that all the patterns are covered throughout an incoming signal.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Alpine Skiing Activity Recognition Using Smartphone’s IMUs

Azadi

Haslgrübler

Anzengruber-Tanase

et al. 2022

Sensors

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Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine skiing activities for further analysis. This study aims to detect alpine skiing activities via smartphone inertial measurement units (IMU) in an unsupervised manner that is feasible for daily use. Data of full skiing sessions from novice to expert skiers were collected in varied conditions using smartphone IMU. The recorded data is preprocessed and analyzed using unsupervised algorithms to distinguish skiing activities from the other possible activities during a day of skiing. We employed a windowing strategy to extract features from different combinations of window size and sliding rate. To reduce the dimensionality of extracted features, we used Principal Component Analysis. Three unsupervised techniques were examined and compared: KMeans, Ward’s methods, and Gaussian Mixture Model. The results show that unsupervised learning can detect alpine skiing activities accurately independent of skiers’ skill level in any condition. Among the studied methods and settings, the best model had 99.25% accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Alpine Skiing Activity Recognition Using Smartphone’s IMUs

Azadi

Haslgrübler

Anzengruber-Tanase

et al. 2022

Sensors

Self Cite

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“…Obtaining data from IMU sensors was unidirectional, since it was a requirement that the device be attached to the wrist of the user, similar to the approaches in [48,49]. This is due to the fact that this position of the sensor is generally accepted by employees in manufacturing, as less disturbing and intrusive and providing descriptive data for complex small-scale activities of hands.…”

Section: Sensingmentioning

confidence: 99%

Towards Flexible and Cognitive Production—Addressing the Production Challenges

et al. 2022

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Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity.

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“…HAR is an active field of research in pervasive computing that aims to detect human physical activities through machine learning models. HAR has various applications in healthcare [ 1 , 2 ], sports [ 3 , 4 ], industry [ 5 , 6 , 7 ], and other fields. Commonly, HAR models utilize activity signals recorded by wearable or visual sensors.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition

Azadi,

Haslgrübler,

Anzengruber-Tanase

et al. 2024

Sensors

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Learning underlying patterns from sensory data is crucial in the Human Activity Recognition (HAR) task to avoid poor generalization when coping with unseen data. A key solution to such an issue is representation learning, which becomes essential when input signals contain activities with similar patterns or when patterns generated by different subjects for the same activity vary. To address these issues, we seek a solution to increase generalization by learning the underlying factors of each sensor signal. We develop a novel multi-channel asymmetric auto-encoder to recreate input signals precisely and extract indicative unsupervised futures. Further, we investigate the role of various activation functions in signal reconstruction to ensure the model preserves the patterns of each activity in the output. Our main contribution is that we propose a multi-task learning model to enhance representation learning through shared layers between signal reconstruction and the HAR task to improve the robustness of the model in coping with users not included in the training phase. The proposed model learns shared features between different tasks that are indeed the underlying factors of each input signal. We validate our multi-task learning model using several publicly available HAR datasets, UCI-HAR, MHealth, PAMAP2, and USC-HAD, and an in-house alpine skiing dataset collected in the wild, where our model achieved 99%, 99%, 95%, 88%, and 92% accuracy. Our proposed method shows consistent performance and good generalization on all the datasets compared to the state of the art.

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Feasibility analysis of unsupervised industrial activity recognition based on a frequent micro action

Cited by 9 publications

References 33 publications

Alpine Skiing Activity Recognition Using Smartphone’s IMUs

Alpine Skiing Activity Recognition Using Smartphone’s IMUs

Towards Flexible and Cognitive Production—Addressing the Production Challenges

Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition

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