A Multi-media Exchange Format for Time-Series Dataset Curation

Scholl, Philipp M.; Völker, Benjamin; Becker, Bernd W.; Laerhoven, Kristof Van

doi:10.1007/978-3-030-13001-5_8

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The data are stored as multiple MKV container into sub-folders named powermeter<ID> and smartmeter001, respectively. We used multimedia containers to store the data, as we previously explored their benefits in [30]. While being optimized for audio or video streams, these containers allow for storing regularly sampled sensor data as time synced audio streams.…”

Section: Data Recordsmentioning

confidence: 99%

“…Each stream has multiple channels for the voltage and current signals. As stated in [30], WavPack allows for a lossless compression while maintaining high compression rates for time series data. In particular, we achieved a compression ratio of 1.46 for the voltage and current measurements using WavPack (only 1.42 could be achieved with hdf5 [32], which has been used e.g., for UK-DALE [11] and BLOND [12]).…”

Section: Data Recordsmentioning

confidence: 99%

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A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

et al. 2020

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In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Monitoring. The framework is comprised of the hardware and software required to record and (semi-automatically) label the data. The hardware setup includes a smart-meter device to record aggregated consumption data and multiple socket meters to record appliance level data. Labeling is performed in a semi-automatic post-processing step guided by a graphical user interface, which reduced the labeling effort by 72% compared to a manual approach. We evaluated our framework and present the FIRED dataset. The dataset features uninterrupted, time synced aggregated, and individual device voltage and current waveforms with distinct state transition labels for a total of 101 days.

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Section: Data Recordsmentioning

confidence: 99%

Section: Data Recordsmentioning

confidence: 99%

A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

et al. 2020

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A Versatile High Frequency Electricity Monitoring Framework for Our Future Connected Home

Völker

Pfeifer

Scholl

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition

Bock,

Kuehne,

Van Laerhoven

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Research has shown the complementarity of camera- and inertial-based data for modeling human activities, yet datasets with both egocentric video and inertial-based sensor data remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). Data from 22 participants performing a total of 18 different workout activities was collected with synchronized inertial (acceleration) and camera (egocentric video) data recorded at 11 different outside locations. WEAR provides a challenging prediction scenario in changing outdoor environments using a sensor placement, in line with recent trends in real-world applications. Benchmark results show that through our sensor placement, each modality interestingly offers complementary strengths and weaknesses in their prediction performance. Further, in light of the recent success of single-stage Temporal Action Localization (TAL) models, we demonstrate their versatility of not only being trained using visual data, but also using raw inertial data and being capable to fuse both modalities by means of simple concatenation. The dataset and code to reproduce experiments is publicly available via: mariusbock.github.io/wear/.

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A Multi-media Exchange Format for Time-Series Dataset Curation

Cited by 3 publications

References 8 publications

A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

A Versatile High Frequency Electricity Monitoring Framework for Our Future Connected Home

WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition

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