Van Laerhoven, Kristof
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Van Laerhoven, Kristof
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Publication Access status: Metadata only , A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensorsResearch into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels. Our study illustrates that different labeling methodologies directly impact the annotations' quality, as well as the capabilities of a deep learning classifier trained with the data. We noticed that in situ methods produce less but more precise labels than recall methods. Furthermore, we combined an activity diary with a visualization tool that enables the participant to inspect and label their activity data. Due to the introduction of such a tool were able to decrease missing annotations and increase the annotation consistency, and therefore the F1-Score of the deep learning model by up to 8% (ranging between 82.1 and 90.4% F1-Score). Furthermore, we discuss the advantages and disadvantages of the methods compared in our study, the biases they could introduce, and the consequences of their usage on human activity recognition studies as well as possible solutions.Source Type:Article1 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensorsResearch into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-thewild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels. Our study illustrates that different labeling methodologies directly impact the annotations' quality, as well as the capabilities of a deep learning classifier trained with the data. We noticed that in situ methods produce less but more precise labels than recall methods. Furthermore, we combined an activity diary with a visualization tool that enables the participant to inspect and label their activity data. Due to the introduction of such a tool were able to decrease missing annotations and increase the annotation consistency, and therefore the F1-Score of the deep learning model by up to 8% (ranging between 82.1 and 90.4 % F1-Score). Furthermore, we discuss the advantages and disadvantages of the methods compared in our study, the biases they could introduce, and the consequences of their usage on human activity recognition studies as well as possible solutions.Source Type:Article2 9 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , A Survey of LoRaWAN-Integrated Wearable Sensor Networks for Human Activity Recognition: Applications, Challenges and Possible SolutionsLong-Range Wide Area Networks (LoRaWAN), a prominent technology within Low-Power Wide Area Networks (LPWANs), have gained traction in remote monitoring due to their long-range communication, scalability, and low energy consumption. Compared to other LPWANs like Sigfox, Ingenu Random Phase Multiple Access (Ingenu-RPMA), Long-Term Evolution for Machines (LTE-M), and Narrowband Internet of Things (NB-IoT), LoRaWAN offers superior adaptability in diverse environments. This adaptability makes it particularly effective for Human Activity Recognition (HAR) systems. These systems utilize wearable sensors to collect data for applications in healthcare, elderly care, sports, and environmental monitoring. Integrating LoRaWAN with edge computing and Internet of Things (IoT) frameworks enhances data processing and transmission efficiency. However, challenges such as sensor wearability, data payload constraints, energy efficiency, and security must be addressed to deploy LoRaWAN-based HAR systems in real-world applications effectively. This survey explores the integration of LoRaWAN technology with wearable sensors for HAR, highlighting its suitability for remote monitoring applications such as Activities of Daily Living (ADL), tracking and localization, healthcare, and safety. We categorize state-of-the-art LoRaWAN-integrated wearable systems into body-worn, hybrid, objectmounted, and ambient sensors. We then discuss their applications and challenges, including energy efficiency, sensor scalability, data constraints, and security. Potential solutions such as advanced edge processing algorithms and secure communication protocols are proposed to enhance system performance and user comfort. The survey also outlines specific future research directions to advance this evolving field.Source Type:Article1 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Datengestützte Umformtechnik: Künstliche Intelligenz zwischen Prozesswissen, Regelung und ProduktionsrealitätWith the 7th Biegeforum, the Chair of Forming Technology at the University of Siegen once again offers researchers the opportunity to present their topics and findings to a specialist audience from industry and academia. This conference volume brings together current research findings and innovative developments in forming technology, highlighting the broad scope of the research field. The Biegeforum thus underscores its role as a key platform for exchange between research and industry — a role it has fulfilled since its first edition in 2011.Source Type:Conference ProceedingsSeries:Forschungsberichte des Lehrstuhls für Umformtechnik3 10 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial SensorsWe present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.Source Type:Article2 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , IBSync: Intra-body synchronization and implicit contextualization of wearable devices using artificial ECG landmarksWith a smaller form factor and a larger set of applications, body-worn devices have evolved into a collection of simultaneously deployed hardware units, rather than into a single all-round wearable. The sensor data, logged by such devices across the user's body, contains a wealth of information but is often difficult to synchronize. Especially the application of machine learning techniques, e.g., for activity recognition, suffers from the inaccuracy of the devices' internal clocks. In recent years, intra-body communication emerged as a promising alternative to the traditional wired and wireless communication techniques. Distributed wearable systems will notably benefit from its advantages, such as a superior energy efficiency. However, due to the absence of commercially available platforms, applications using this innovative technique remain rare and underinvestigated. With IBSync, we present a novel concept in which artificial landmark signals are received by body-worn devices on touching, approaching, or passing certain areas, surfaces, or objects with embedded transmitter beacons. The landmark signals enable both the wearables' intentional or incidental synchronization as well as the implicit contextualization using supplementary information about the beacons' situational context. For the detection of the landmarks, we propose to repurpose the on-board ECG sensor front-end available in recent commercial wearable devices. Evaluated on a total of 215 min of recordings from two devices, we demonstrate the concept's feasibility and a promising synchronization error of 0.80±1.79 samples or 6.25±14.00 ms at a device's sampling rate of 128 Hz.Source Type:Article3 7 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , TubeBEND: A Real-World Dataset for Geometry Prediction in Rotary Draw BendingThis paper presents TubeBEND, a real-world dataset comprising 318 rotary tube bending processes, which were collected and sorted by experts from various fields to evaluate machine learning and signal analysis methods. The dataset addresses the industrial challenge of predicting the geometry of a first-stage bend, which can be beneficial for designing machine clamping molds for the second-stage bend in two-stage rotary draw bending. Some geometry criteria, such as the tube's final bent angle (or springback) and its cross-sectional deformation, are being recorded in this dataset. This dataset enables the development of machine learning models for geometry prediction and improved machine setup, supporting operators in reducing springback and deformation. Moreover, by recording some process parameters, such as tool movements and forces or torques applied to them, we deliver detailed information about their impacts on the final tube geometry. The focus of our work is to discover solutions that can replace traditional methods, such as trial-and-error or simulation-based predictions, by including experimental process variables in ML algorithms. Our dataset is publicly available as a benchmark to improve data-driven methods in this field at: https://github.com/zeyneddinoz/tubebend and https://zenodo.org/records/18458823.Source Type:InProceedings2 10
