Till Riedel

Karlsruhe Institute of Technology | KIT · Institute of Telematics

Many pervasive applications deal with relative positions between interacting entities rather than global coordinates. The Relate project developed sensing methods and a modular system architecture for peer-to-peer relative positioning. It studied the methods and architecture in application case studies on mobile spatial interaction, firefighter nav...

Wireless Sensing and Radio Identification systems have undergone many innovations during the past years. This has led to short product lifetimes for both software and hardware compared to classical industries. However, especially industries dealing with long-term support of products, e.g. of industrial machinery, and product lifetime of 40+ years m...

Earphones can give access to sensitive information via voice assistants which demands security methods that prevent unauthorized use. Therefore, we developed EarCapAuth, an authentication mechanism using 48 capacitive electrodes embedded into the soft silicone eartips of two earables. For evaluation, we gathered capactive ear canal measurements fro...

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is...

In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional explanatory methods like Class Activation Mapping (CAM) and attention mechanisms, although effectiv...

Sensor-based HAR models face challenges in cross-subject generalization due to the complexities of data collection and annotation, impacting the size and representativeness of datasets. While data augmentation has been successfully employed in domains like natural language and image processing, its application in HAR remains underexplored. This stu...

In recent years, there has been a growing trend in the utilization of Artificial Intelligence (AI) technology to construct human-centered systems that are based on implicit time series information, ranging from contextual recommendations on smartwatches to human activity recognition on production workshop. Despite the advantages of these systems, t...

Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering. Despite recent advancements, challenges inherent to HAR data, such as noisy data, intra-class variability and inter-class similarity, remain. To address these challenges, w...

The performance of machine learning models depends heavily on the feature space and feature engineering. Although neural networks have made significant progress in learning latent feature spaces from data, compositional feature engineering through nested feature transformations can reduce model complexity and can be particularly desirable for inter...

Modern web technologies allow novel types of sensor networks that collect measurements from different sources ranging from citizen-collected data to official sources. In this paper, we propose a scheme to deal with measurement sources of different quality for time-series prediction of urban particulate matter. Our approach is based on a neural kern...

The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficial in guiding the development of machine learning models on the one hand and finding targeted defenses on the other. However, most of the adversarial attacks today leverag...

The enormous potential of artificial intelligence, especially artificial neural networks, when used for edge computing applications in cars, traffic lights or smart watches, has not yet been fully exploited today. The reasons for this are the computing, energy and memory requirements of modern neural networks, which typically cannot be met by embed...

To this day, a variety of approaches for providing local interpretability of black-box machine learning models have been introduced. Unfortunately, all of these methods suffer from one or more of the following deficiencies: They are either difficult to understand themselves, they work on a per-feature basis and ignore the dependencies between featu...

T he interdisciplinary „Smart Air Quality Network" (SmartAOnet) project provides an overall system for recording, visualising and predicting the spatial distribution of air pollutant concentrations in urban atmospheres. Within more than three years SmartAOnet aimed to implement an inexpensive measurement network, which is transferable to other citi...

An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method, Convolutional Neural Network (CNN) and conventional Recurrent Neural Network (RNN) approaches have produced impressive results on this matter, due to their ability to lea...

Pollution-monitoring systems (PMSs) are used worldwide to sense environmental changes, such as air quality conditions or temperature increases, and to monitor compliance with regulations. However, organizations manage the environmental data collected by such PMSs in a centralized manner, which is why recorded environmental data are vulnerable to ma...

An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method, Convolutional Neural Network (CNN) and conventional Recurrent Neural Network (RNN) approaches have produced impressive results on this matter, due to their ability to lea...

The Indoor Outdoor (IO) status of mobile devices is fundamental information for various smart city applications. In this paper we present NeuralIO, a neural network based method to deal with the Indoor Outdoor (IO) detection problem for smartphones. Multimodal data from various sensors on a smartphone are fused through neural network models to dete...

Air quality is one of the most important topics in our urban life, as it is of great significance for human health and urban planning. However, accurate assessment and prediction of air quality in urban areas are difficult. In major cities, typically only a limited number of air quality monitoring stations are available, and inferring air quality i...

Acoustic Doppler shift estimation is a cost-effective way to implement Human-Computer Interaction applications across existing smart devices such as smart phones and smart spekaers. However, due to the inherent uncertainty principle in the traditional time-frequency analysis, it remains challenging to profile motions accurately and timely. In this...

The contextual status of mobile devices is fundamental information for many smart city applications. In this paper we present AudioIO, an active sound probing based method to tackle the problem of Indoor Outdoor (IO) detection for smartphones. We utilize the embedded speaker and microphone to emit probing signal and collect reverberation of surroun...

Die naive Fusion von Messdaten unbekannter Qualität führt häufig zu keiner signifikanten Verbesserung von Vorhersagemodellen. Stochastische Regressionsmodelle bieten hier einen interessanten Ansatz, der es erlaubt, komplexe Zusammenhänge in vergangenen Daten zu erkennen, diese auf zukünftige Daten zu übertragen und somit Vorhersagen zu verbessern....

Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we...

In Particulate Matter (PM) monitoring, current laser scattering low-cost sensor generations exhibit better stability than early sensor generations and feature internal digital processing to achieve more accurate results. As a representative of this class of sensors, we examine the popular SDS011 PM sensor. Previous work about co-location measuremen...

Acoustic Doppler shift estimation is a cost-effective way to implement Human-Computer Interaction applications across existing smart devices such as smart phones and smart spekaers. However, due to the inherent uncertainty principle in the traditional time-frequency analysis, it remains challenging to profile motions accurately and timely. In this...

The rise of the smartphone opens up new possibilities for researchers to observe users in everyday life situations. Researchers from diverse disciplines use in-field studies to gain new insights into user behavior and experiences. However, the collected datasets are mostly not available to the public and thus results are neither falsifiable nor rep...

Classic measurement grids with their static and expensive infrastructure are unfit to realize modern air quality monitoring needs, such as source appointment, pollution tracking or the assessment of personal exposure. Fine grained air quality assessment (both in time and space) is the future. Different approaches, ranging from measurement with low-...

The SmartAQnet research project is investigating a smart way of determining the spatial distribution of fine dust, prototyped at a demonstration site in Augsburg, Germany. The research approach is the collection of different qualities of fine dust measurement data and its combination with remote sensing data. Measured fine dust data can be fed into...

In Particulate Matter (PM) monitoring, a paradigm shift towards incorporating distributed sensing approaches using low-cost sensors has begun [1]. In past research, early generations of low-cost particle sensors based on IR light scattering have been compared with official measurement stations, showing that these sensors can in principle capture th...

Reading and understanding source code is a major task in software development. Code comprehension depends on the quality of code, which is impacted by code structure and identifier naming. In this paper we empirically investigated whether longer but more descriptive identifier names improve code comprehension compared to short names, as they repres...

Air quality and the associated subjective and health-related quality of life are among the important topics of urban life in our time. In the past years, a paradigm shift towards integrating mobile PM monitors to form distributed sensing networks has begun in air quality sensing [1]. In addition to new and promising measurement approaches, large-sc...

Die räumliche und zeitliche Verteilung von Luftschadstoffen in Städten ist sehr variabel, da sie von unterschiedlichen Einflüssen abhängt, die von den Emittenten (räumliche Anordnung, zeitliche Aktivität) als auch den meteorologischen Bedingungen, der Bebauung und den chemischen Prozessen kommen. Zurzeit werden Verunreinigung der Luft mit relativ w...

Today we have access to a vast amount of weather, air quality, noise or radioactivity data collected by individual around the globe. This volunteered geographic information often contains data of uncertain and of heterogeneous quality, in particular when compared to official in-situ measurements. This limits their application, as rigorous, work-int...

Today we have access to a vast amount of weather, air quality, noise or radioactivity data collected by individual around the globe. This volunteered geographic information often contains data of uncertain and of heterogeneous quality, in particular when compared to official in-situ measurements. This limits their application, as rigorous, work-int...

Cyber-physical systems (CPS) require a new level of dynamics in information processing. Databases and query approaches need to be extended towards dynamic stream aggregation and analysis systems. In this paper, we designed ECQELS, a semantic stream processing engine, to support CPS applications by adding essential features like dynamic sensor selec...

User studies are an important part in the design of socio-technical systems. However, Computer Science students and practitioners in Ubicomp often have not been trained in designing and conducting experimental studies. To help bridging this gap, this paper presents a (very) short tutorial on how (not) to design and conduct experimental studies and...

Citizen Science with mobile and wearable technology holds the possibility of unprecedented observation systems. Experts and policy makers are torn between enthusiasm and scepticism regarding the value of the resulting data, as their decision making traditionally relies on high-quality instrumentation and trained personnel measuring in a standardize...

Components in the power grid require security, high availability and real-time communications for reliable operation. But these components are based on software that contains issues that need to be fixed. Timely installation of software updates allows securing vulnerable software quickly but conventionally disrupts availability and communications....

In industrial environments, machine faults have a high impact on productivity due to the high costs it can cause. Machine generated event logs are a abundant source of information for understanding the causes and events that led to a critical event in the machine. In this work, we present a Sequence-Mining based technique to automatically extract s...

High quality of master data is crucial for almost every company and it has become increasingly difficult for domain experts to validate the quality and extract useful information out of master data sets. However, experts are rare and expensive for companies and cannot be aware of all dependencies in the master data sets. In this paper, we introduce...

The DeveloperSpace, one of the core components of GPII, is a self-sustainable infrastructure and collaborative environment, where developers, implementers, consumers, prosumers and other directly and indirectly involved actors (e.g. teachers, caregivers, clinicians) may interact with and play a role in its viability and the development of new acces...

The great increase of information and communication technology functionality in every-day living environments, from home appliances to public services, introduces complexities and inter-dependencies between heterogeneous devices and services, and imposes higher demands with respect to our digital literacy and technical knowledge. While young people...

Applications in the Internet of Things require security, high availability and real-time communications for reliable operation. But their software contains issues that need to be fixed. Timely installation of software updates allows securing vulnerable software quickly but conventionally disrupts availability and communications. Rolling update sche...

Any upcoming industrial revolution will rely on the ability to harness software as the nervous system of future production environments. This paper proposes an app ecosystem as the potential key enabler of industry digitization and argues for the need of semantic web technologies as primary enablers for app interoperability. We shortly discuss how...

Fueled by the increasing proliferation of citizen generated spatio-temporal data -- especially in participatory urban infrastructure monitoring -- municipal authorities are in need for ways to process and understand increasingly overwhelming amounts of data. However, duplicate issue reporting by citizens such as broken traffic lights, potholes or g...

Internet-enabled, location aware smart phones with sensor inputs have led to novel urban infra-structure monitoring applications exploiting unprecedented high levels of citizen participation in dense metropolitan areas. For policy makers, it is a key task to keep track of trends and developments of reported infra-structure issues for understanding...

While almost any device today may have a virtual representation, the web itself is not yet a very physical experience. Bringing proven spatial interaction and ubiquitous computing paradigms to life using current web technology, we designed IndianaJS, a JavaScript framework to add a physical browsing experience to any Web of Things content. The eval...

In ambulatory assessment, psychologists apply experience sampling methods (ESM) on mobile devices to assess self-reports from subjects. One major challenge is to support domain experts to create ESM apps themselves without prior programming knowledge. When running ESM apps, subjects are prompted to answer self-reports time-triggered at fixed points...

The experience sampling method (ESM) is applied in ambulatory assessment to prompt subject self-reporting. Existing mobile apps provide time-triggered prompts but lack event-triggers. Hence, the sampling might not occur in moments that are of interest for a psychologist. To identify relevant sensor sources and contexts we conducted an online survey...

In this paper we present the iterative design process of our wearable sensory substitution system ProximityHat, which uses pressure actuators around the head to convey spatial information. It was already shown that the sense of touch can be used to augment our perception of reality. Existing systems focus on vibration signals for information transf...

We present MoA², a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable sensing. In a...

In ambulatory assessment, subjects are monitored in everyday life. Though, it is diffcult to unobtrusively assess information - e.g. about their context and affective state - which results in an increased burden for the subjects. This burden is caused by a complex self-report that they need to provide or by additional wearables that need to be carr...

Prosperity4All is a continuous and dynamic paradigm shift towards an e-inclusion framework building on the architectural and technical foundations of other Global Public Inclusive Infrastructure (GPII) projects aiming to create a self-sustainable and growing ecosystem where developers, implementers, consumers, prosumers and other directly and indir...

This work presents the bPart, a highly integrated autonomous sensor platform for use with mobile phones and devices. It consists of a Bluetooth Low Energy (BLE) radio and several MEMS sensors, all integrated in a volume of less than 1cm³, including the battery. Aside from the wireless transceiver, the bPart features sensors for ambient illumination...

Cost effective, intelligent and network capable devices allow the proliferation of smart home applications in common households. However, user documentation has not evolved to handle end-user installation as complex interaction patterns become increasingly difficult for a human to understand. Our system allows the set-up of a complex home automatio...

Displaying three-dimensional content on a flat display is bound to reduce the impression of depth, particularly for mobile video see-trough augmented reality. Several applications in this domain can benefit from accurate depth perception, especially if there are contradictory depth cues, like occlusion in a x-ray visualization. The use of stereosco...

As users and developers have started to put the Internet of Things to good use, the approach of documenting applications has not evolved to handle the created complexity. As items, devices and systems become more customizable and adapted to their users, their documentation still lags behind. In particular, documentation covering the contextual beha...