Bremen Big Data Challenge 2017: Predicting University Cafeteria Load

Weiner, Jochen; Diener, Lorenz; Stelter, Simon; Externest, Eike; Kuhl, Sebastian; Putze, Felix; Schulze, Timo; Salous, Mazen; Liu, Hui; Küster, Dennis; Schultz, Tanja

doi:10.1007/978-3-319-67190-1_35

Cited by 1 publication

(1 citation statement)

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“…As mentioned in Section 1 , wearable sensors are currently available on a large scale, promoting the acquisition of massive amounts of data. Datasets of this size can no longer be handled by trivial means and call for engineers and data scientists with expertise in data mining, machine learning, and data analysis [ 10 ]. This increase in wearable usage has also been seen in industrial environments, which is motivated by the current trend of Industry 4.0 [ 11 ], promoting the use of sensors to monitor in real-time their machines for damage prevention, and their workers for occupational-related disorder prevention and productivity improvements [ 12 , 13 ].…”

Section: Related Literaturementioning

confidence: 99%

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

et al. 2022

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Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

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