TY  - JOUR
A1  - Bano, Dorina
A1  - Michael, Judith
A1  - Rumpe, Bernhard
A1  - Varga, Simon
A1  - Weske, Mathias
T1  - Process-aware digital twin cockpit synthesis from event logs
JF  - Journal of computer languages
N2  - The engineering of digital twins and their user interaction parts with explicated processes, namely processaware digital twin cockpits (PADTCs), is challenging due to the complexity of the systems and the need for information from different disciplines within the engineering process. Therefore, it is interesting to investigate how to facilitate their engineering by using already existing data, namely event logs, and reducing the number of manual steps for their engineering. Current research lacks systematic, automated approaches to derive process-aware digital twin cockpits even though some helpful techniques already exist in the areas of process mining and software engineering. Within this paper, we present a low-code development approach that reduces the amount of hand-written code needed and uses process mining techniques to derive PADTCs. We describe what models could be derived from event log data, which generative steps are needed for the engineering of PADTCs, and how process mining could be incorporated into the resulting application. This process is evaluated using the MIMIC III dataset for the creation of a PADTC prototype for an automated hospital transportation system. This approach can be used for early prototyping of PADTCs as it needs no hand-written code in the first place, but it still allows for the iterative evolvement of the application. This empowers domain experts to create their PADTC prototypes.
KW  - process-aware digital twin cockpit
KW  - low-code development approaches
KW  - sensor data
KW  - event log
KW  - process mining
KW  - process-awareness
Y1  - 2022
U6  - https://doi.org/10.1016/j.cola.2022.101121
SN  - 2590-1184
SN  - 2665-9182
VL  - 70
PB  - Elsevier
CY  - Amsterdam [u.a.]
ER  - 
TY  - JOUR
A1  - Konak, Orhan
A1  - Wegner, Pit
A1  - Arnrich, Bert
T1  - IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition
JF  - Sensors
N2  - Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
KW  - human activity recognition
KW  - image processing
KW  - machine learning
KW  - sensor data
Y1  - 2020
U6  - https://doi.org/10.3390/s20247179
SN  - 1424-8220
VL  - 20
IS  - 24
PB  - MDPI
CY  - Basel
ER  -