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Terhörst, Philipp; Tran, Mai Ly; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Comparison-Level Mitigation of Ethnic Bias in Face Recognition

2020

IWBF 2020. Proceedings

International Workshop on Biometrics and Forensics (IWBF) <8, 2020, Porto, Portugal>

Current face recognition systems achieve high performance on several benchmark tests. Despite this progress,recent works showed that these systems are strongly biasedagainst demographic sub-groups. Previous works introducedapproaches that aim at learning less biased representations.However, applying these approaches in real applications requiresa complete replacement of the templates in the database. Thisreplacement procedure further requires that a face image ofeach enrolled individual is stored as well. In this work, wepropose the first bias-mitigating solution that works on thecomparison-level of a biometric system. We propose a fairnessdriven neural network classifier for the comparison of twobiometric templates to replace the systems similarity function.This fair classifier is trained with a novel penalization termin the loss function to introduce the criteria of group andindividual fairness to the decision process. This penalization termforces the score distributions of different ethnicities to be similar,leading to a reduction of the intra-ethnic performance differences.Experiments were conducted on two publicly available datasetsand evaluated the performance of four different ethnicities. Theresults showed that for both fairness criteria, our proposedapproach is able to significantly reduce the ethnic bias, whileit preserves a high recognition ability. Our model, build onindividual fairness, achieves bias reduction rate between 15.35%and 52.67%. In contrast to previous work, our solution is easy tointegrate into existing systems by simply replacing the systemssimilarity functions with our fair template comparison approach.

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Fu, Biying; Kirchbuchner, Florian; Kuijper, Arjan

Data augmentation for time series: traditional vs generative models on capacitive proximity time series

2020

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>

ACM International Conference Proceedings Series (ICPS)

Large labeled quantities and diversities of training data are often needed for supervised, data-based modelling. Data distribution should cover a rich representation to support the generalizability of the trained end-to-end inference model. However, this is often hindered by limited labeled data and the expensive data collection process, especially for human activity recognition tasks. Extensive manual labeling is required. Data augmentation is thus a widely used regularization method for deep learning, especially applied on image data to increase the classification accuracy. But it is less researched for time series. In this paper, we investigate the data augmentation task on continuous capacitive time series with the example on exercise recognition. We show that the traditional data augmentation can enrich the source distribution and thus make the trained inference model more generalized. This further increases the recognition performance for unseen target data around 21.4 percentage points compared to inference model without data augmentation. The generative models such as variational autoencoder or conditional variational autoencoder can further reduce the variance on the target data.

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Fang, Meiling; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

2020

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there is no works that analyse the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the the NDCLD-2013 database. The experimental results points out that female users will be significantly less protected by the PAD, in comparison to males.

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Rus, Silvia; Helfmann, Stefan; Kirchbuchner, Florian; Kuijper, Arjan

Designing smart home controls for elderly

2020

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>

ACM International Conference Proceedings Series (ICPS)

Technology is evolving by the day and with it the devices to control it. Sophisticated systems, like Smart Homes, are currently controlled in most cases via a smartphone app. While this may be acceptable for younger and middle-aged people, elders, however, have trouble keeping up with new devices and might not want to use a smartphone. Most modern-day control schemes like touch screens and menus are regarded as too complicated. However, Smart Homes provide many opportunities to reduce the every-day burden on elderly and people with special needs. Providing elderly people easy access to advanced and helpful technology via familiar interface types immensely improves their quality of life.We propose a Smart Home control designed especially for use by elderly. Our contribution ranges from evaluating existing systems to designing and building the Smart Home control for elderly based on their special requirements. Moreover, we involve elderly in the design process and evaluate the proposed prototype in a qualitative study with 10 elderly users. The results conclude that being presented with the scenario to already own the required Smart Home technology, the participants were quick to accept the cube as user friendlier when compared to smartphone controls or touchscreen controls in general.

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Fu, Biying; Jarms, Lennart; Kirchbuchner, Florian; Kuijper, Arjan

ExerTrack—Towards Smart Surfaces to Track Exercises

2020

Technologies

The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or cycling can be accurately recognized using wearable devices. However whole-body exercises such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for recognizing whole-body activities. Computer vision techniques on image data also report high recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas. Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises with a user-independent recognition accuracy of 93.5% and a user-dependent recognition accuracy of 95.1% based on a test study with 9 participants each performing 2 full sessions. We adopt a template-based approach to count repetitions and reach a user-independent counting accuracy of 93.6 %. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of our proposed system and model selection to improve the recognition accuracy and data augmentation technique to regularize the network.

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Terhörst, Philipp; Riehl, Kevin; Damer, Naser; Rot, Peter; Bortolato, Blaz; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, Arjan

PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

2020

IEEE Access

Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.

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Boutros, Fadi; Damer, Naser; Raja, Kiran; Ramachandra, Raghavendra; Kirchbuchner, Florian; Kuijper, Arjan

Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition

2020

IWBF 2020. Proceedings

International Workshop on Biometrics and Forensics (IWBF) <8, 2020, Porto, Portugal>

Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typicallyincludes an eye-facing camera and is used for eye tracking.As some of these applications require accessing or transmittinghighly sensitive private information, a trusted verification ofthe operator’s identity is needed. We investigate the use ofHMD-setup to perform verification of operator using periocularregion captured from inbuilt camera. However, the uncontrollednature of the periocular capture within the HMD results inimages with a high variation in relative eye location and eyeopening due to varied interactions. Therefore, we propose a newnormalization scheme to align the ocular images and then, a newreference sample selection protocol to achieve higher verificationaccuracy. The applicability of our proposed scheme is exemplifiedusing two handcrafted feature extraction methods and two deeplearning strategies.We conclude by stating the feasibility of sucha verification approach despite the uncontrolled nature of thecaptured ocular images, especially when proper alignment andsample selection strategy is employed.

978-1-7281-6232-4

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Fu, Biying; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Sensing Technology for Human Activity Recognition: a Comprehensive Survey

2020

IEEE Access

Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people’s everyday life. In order to solve many human-centered problems, such as health-care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization.

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Fu, Biying; Kirchbuchner, Florian; Kuijper, Arjan

Unconstrained workout activity recognition on unmodified commercial off-the-shelf smartphones

2020

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Corfu, Greece>

ACM International Conference Proceedings Series (ICPS)

Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone even more powerful than the computers NASA used to send astronauts to the moon. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. In this paper, we investigate the possibility of using a unmodified commercial off-the-shelf (COTS) smartphone to recognize 8 different workout exercises. App-based workout has become popular in the last few years. People do not need to go to the gym to practice. The advantage of using a mobile device is, that you can practice anywhere at anytime. In this work, we turned a COTS smartphone to an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants performing these eight exercises, we show first results for cross person evaluation and the generalization ability of our inference models on unseen participants. A bidirectional LSTM model achieved an overall F1 score of 88.86 % for the cross subject case and 79.52 % for the holdout participants evaluation. Similar good results can be achieved by a VGG16 fine-tuned model in comparison to a 2D-CNN architecture trained from scratch.

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Damer, Naser; Zienert, Steffen; Wainakh, Yaza; Kirchbuchner, Florian; Kuijper, Arjan; Moseguí Saladié, Alexandra

A Multi-detector Solution Towards an Accurate and Generalized Detection of Face Morphing Attacks

2019

FUSION 2019

International Conference on Information Fusion (FUSION) <22, 2019, Ottawa, Canada>

Face morphing attack images are built to be verifiable to multiple identities. Associating such images to identity documents leads to building faulty identity links, causing vulnerabilities in security critical processes. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work introduces a multi-detector fusion solution that aims at gaining both, accuracy and generalization over different morphing types. This is performed by fusing classification scores produced by detectors trained on databases with variations in morphing type and image pairing protocols. This work develop and evaluate the proposed solution along with baseline solutions by building a database with three different pairing protocols and two different morphing approaches. This proposed solution successfully lead to decreasing the Bona Fide Presentation Classification Error Rate at 1.0% Attack Presentation Classification Error Rate from 15.7% and 3.0% of the best performing single detector to 2.7% and 0.0%, respectively on two face morphing techniques, pointing out a highly generalized performance.

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Wilmsdorff, Julian von; Kirchbuchner, Florian; Fu, Biying; Braun, Andreas; Kuijper, Arjan

An Experimental Overview on Electric Field Sensing

2019

Journal of Ambient Intelligence and Humanized Computing

Electric fields exist everywhere. They are influenced by living beings, conductive materials, and other charged entities. Electric field sensing is a passive capacitive measurement technique that detects changes in electric fields and has a very low power consumption. We explore potential applications of this technology and compare it to other measurement approaches, such as active capacitive sensing. Five prototypes have been created that give an overview of the potential use cases and how they compare to other technologies. Our results reveal that electric field sensing can be used for indoor applications as well as outdoor applications. Even a mobile usage is possible due to the low energy consumption of this technology.

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Zimmermann, Verena; Gerber, Paul; Marky, Karola; Böck, Leon; Kirchbuchner, Florian

Assessing Users’ Privacy and Security Concerns of Smart Home Technologies

2019

i-com

Smart Home technologies have the potential to increase the quality of life, home security and facilitate elderly care. Therefore, they require access to a plethora of data about the users’ homes and private lives. Resulting security and privacy concerns form a relevant barrier to adopting this promising technology. Aiming to support end users’ informed decision-making through addressing the concerns we first conducted semi-structured interviews with 42 potential and little-experienced Smart Home users. Their diverse concerns were clustered into four themes that center around attacks on Smart Home data and devices, the perceived loss of control, the tradeoff between functionality and security, and user-centric concerns as compared to concerns on a societal level. Second, we discuss measures to address the four themes from an interdisciplinary perspective. The paper concludes with recommendations for addressing user concerns and for supporting developers in designing user-centered Smart Home technologies.

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Damer, Naser; Boutros, Fadi; Kirchbuchner, Florian; Kuijper, Arjan

D-ID-Net: Two-Stage Domain and Identity Learning for Identity-Preserving Image Generation From Semantic Segmentation

2019

2019 International Conference on Computer Vision Workshops. Proceedings

International Conference on Computer Vision (ICCV) <17, 2019, Seoul, Korea>

Training functionality-demanding AR/VR systems require accurate and robust gaze estimation and tracking solutions. Achieving such a performance requires the availability of diverse eye image data that might only be acquired by the means of image generation. Works addressing the generation of such images did not target realistic and identity-specific images, nor did they address the practicalrelevant case of generation from semantic labels. Therefore, this work proposes a solution to generate realistic and identity-specific images that correspond to semantic labels, given samples of a specific identity. Our proposed solution consists of two stages. In the first stage, a network is trained to transform the semantic label into a corresponding eye image of a generic identity. The second stage is an identity-specific network that induces identity details on the generic eye image. The results of our D-ID-Net solutions shows a high degree of identity-preservation and similarity to the ground-truth images, with an RMSE of 7.235.

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2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <12, 2019, Rhodes, Greece>

Smart textiles and garments promise intriguing new possibilities for the wearer. Integrated interaction can create new experiences and sensors can detect relevant information about the wearer. However, this poses an additional challenge for the designer of smart garments, about how to integrate these technologies. In this work, we want to investigate how human intuition and technical knowledge feed into the design of smart garments. Using a jacket that tracks its whereabouts as a use case, we have collected a dataset from 18 test subjects with varying technical knowledge, on what sensor patterns they would create on the garment. Using a specifically created simulation framework, we have evaluated the performance of the created sensor patterns. We observed that many participants intuitively create well-working patterns, while technical knowledge does not play a significant role in the resulting performance.

978-1-4503-6232-0

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Rus, Silvia; Braun, Andreas; Kirchbuchner, Florian; Kuijper, Arjan

E-Textile Capacitive Electrodes: Fabric or Thread

2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <12, 2019, Rhodes, Greece>

Back pain is one of the most common illnesses in Western civilizations. Office work and lack of motion can lead to deterioration over time. Many people already use seat cushions to improve their posture during work or leisure. In this work, we present an E-Textile cushion. This seat cushion is equipped with capacitive proximity sensors that track the proximity and motion of the sitting user and distinguish up to 7 postures. Giving a user immediate feedback on the posture can facilitate more healthy behavior. We evaluated a number of different electrode setups, materials, and classification methods, leading to a maximum accuracy of 97.1%.

978-1-4503-6232-0

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Boutros, Fadi; Damer, Naser; Terhörst, Philipp; Kirchbuchner, Florian; Kuijper, Arjan

Exploring the Channels of Multiple Color Spaces for Age and Gender Estimation from Face Images

2019

FUSION 2019

International Conference on Information Fusion (FUSION) <22, 2019, Ottawa, Canada>

Soft biometrics identify certain traits of individuals based on their sampled biometric characteristics. The automatic identification of traits like age and gender provides valuable information in applications ranging from forensics to service personalization. Color images are stored within a color space containing different channels. Each channel represents a different portion of the information contained in the image, including these of soft biometric patterns. The analysis of the age and gender information in the different channels and different color spaces was not previously studied. This work discusses the soft biometric performances using these channels and analyzes the sample error overlap between all possible channels to successfully prove that different information is considered in the decision making from each channel. We also present a multi-channel selection protocols and fusion solution of the selected channels. Beside the analyzes of color spaces and their channels, our proposed multi-channel fusion solution extends beyond state-of-the-art performance in age estimation on the widely used Adience dataset.

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Boutros, Fadi; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Eye-MMS: Miniature Multi-Scale Segmentation Network of Key Eye-Regions in Embedded Applications

2019

2019 International Conference on Computer Vision Workshops. Proceedings

International Conference on Computer Vision (ICCV) <17, 2019, Seoul, Korea>

Segmentation of the iris or sclera is an essential processing block in ocular biometric systems. However, humancomputer interaction, as in VR/AR applications, requires multiple region segmentation to enable smoother interaction and eye-tracking. Such application does not only demand highly accurate and generalizable segmentation, it requires such segmentation model to be appropriate for the limited computational power of embedded systems. This puts strict limits on the size of the deployed deep learning models. This work presents a miniature multi-scale segmentation network consisting of inter-connected convolutional modules. We present a baseline multi-scale segmentation network and modify it to reduce its parameters by more than 80 times, while reducing its accuracy by less than 3%, resulting in our Eye-MMS model containing only 80k parameters. This work is developed on the OpenEDS database and is conducted in preparation for the OpenEDS Semantic Segmentation Challenge.

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Terhörst, Philipp; Huber, Marco; Kolf, Jan Niklas; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images

2019

FUSION 2019

International Conference on Information Fusion (FUSION) <22, 2019, Ottawa, Canada>

Automated estimation of demographic attributes, such as gender and age, became of great importance for many potential applications ranging from forensics to social media. Although previous works reported performances that closely match human level. These solutions lack of human intuition that allows human beings to state the confidences of their predictions. While the human intuition subconsciously considers surrounding conditions or the lack of experience in a certain task, current algorithmic solutions tend to mispredict with high confidence scores. In this work, we propose a multi-algorithmic fusion approach for age and gender estimation that is able to accurately state the model’s prediction reliability. Our solution is based on stochastic forward passes through a dropout-reduced neural network ensemble. By utilizing multiple stochastic forward passes combined from the neural network ensemble, the centrality and dispersion of these predictions are used to derive a confidence statement about the prediction. Our experiments were conducted on the Adience benchmark.We showed that the proposed solution reached and exceeded state-of-the-art performance for the age and gender estimation tasks. Further, we demonstrated that the reliability statements of the predictions of our proposed solution capture challenging conditions and underrepresented training samples.

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Debiasi, Luca; Damer, Naser; Moseguí Saladié, Alexandra; Rathgeb, Christian; Scherhag, Ulrich; Busch, Christoph; Kirchbuchner, Florian; Uhl, Andreas

On the Detection of GAN-Based Face Morphs Using Established Morph Detectors

2019

Image Analysis and Processing - ICIAP 2019

International Conference of Image Analysis and Processing (ICIAP) <20, 2019, Trento, Italy>

Lecture Notes in Computer Science (LNCS), 11752

Face recognition systems (FRS) have been found to be highly vulnerable to face morphing attacks. Due to this severe security risk, morph detection systems do not only need to be robust against classical landmark-based face morphing approach (LMA), but also future attacks such as neural network based morph generation techniques. The focus of this paper lies on an experimental evaluation of the morph detection capabilities of various state-of-the-art morph detectors with respect to a recently presented novel face morphing approach, MorGAN, which is based on Generative Adversarial Networks (GANs). In this work, existing detection algorithms are confronted with different attack scenarios: known and unknown attacks comprising different morph types (LMA and MorGAN). The detectors’ performance results are highly dependent on the features used by the detection algorithms. In addition, the image quality of the morphed face images produced with the MorGAN approach is assessed using well-established no-reference image quality metrics and compared to LMA morphs. The results indicate that the image quality of MorGAN morphs is more similar to bona fide images compared to classical LMA morphs.

978-3-030-30644-1

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Fu, Biying; Kirchbuchner, Florian; Wilmsdorff, Julian von; Große-Puppendahl, Tobias; Braun, Andreas; Kuijper, Arjan

Performing Indoor Localization with Electric Potential Sensing

2019

Journal of Ambient Intelligence and Humanized Computing

Location-based services or smart home applications all depend on an accurate indoor positioning system. Basically one divides these systems into token-based and token-free localization systems. In this work, we focus on the token-free system based on smart floor technology. Smart floors can typically be built using pressure sensors or capacitive sensors. However, these set-ups are often hard to deploy as mechanical or electrical features are required below the surface and even harder to replace when detected a sensor malfunctioning. Therefore we present a novel indoor positioning system using an uncommon form of passive electric field sensing (EPS), which detects the electric potential variation caused by body movement. The EPS-based smart floor set-up is easy to install by deploying a grid of passive electrode wires underneath any non-conductive surfaces. Easy maintenance is also ensured by the fact that the sensors are not placed underneath the surface, but on the side. Due to the passive measuring nature, low power consumption is achieved as opposed to active capacitive measurement. Since we do not collect image data as in visual-based systems and all sensor data is processed locally, we preserve the user’s privacy. The proposed architecture achieves a high position accuracy and an excellent spatial resolution. Based on our evaluation conducted in our living lab, we measure a mean positioning error of only 12.7 cm.

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Siegmund, Dirk; Tran, Vinh Phuc; Wilmsdorff, Julian von; Kirchbuchner, Florian; Kuijper, Arjan

Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control

2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Iberoamerican Conference on Pattern Recognition (CIARP) <24, 2019, Havana, Cuba>

Lecture Notes in Computer Science (LNCS), 11896

A major risk of an automated high-security entrance control is that an authorized person takes an unauthorized person into the secured area. This practice is called “piggybacking”. Known systems try to prevent it by using physical barriers combined with sensory or camera based algorithms. In this paper we present a multi-sensor solution for verifying the number of persons that stand within a defined transit area. We use sensors that are installed in the floor to detect feet as well as camera shots taken from above. We propose an image-based approach that uses change detection to extract motion from a sequence of images and classify it by using a convolutional neural network. Our sensor-based approach shows how user interactions can be used to facilitate safe separation. Both methods are computationally efficient so they can be used in embedded systems. In the evaluation, we were able to achieve state-ofthe- art results for both approaches individually. Merging both methods sustainably prevents piggybacking, at a BPCER of 7.1%, where bona fide presentations are incorrectly classified as presentation attacks.

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Terhörst, Philipp; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination

2019

The 12th IAPR International Conference On Biometrics

IAPR International Conference on Biometrics (ICB) <12, 2019, Crete, Greece>

Recent research on soft-biometrics showed that more information than just the person’s identity can be deduced from biometric data. Using face templates only, information about gender, age, ethnicity, health state of the person, and even the sexual orientation can be automatically obtained. Since for most applications these templates are expected to be used for recognition purposes only, this raises major privacy issues. Previous work addressed this problem purely on image level regarding function creep attackers without knowledge about the systems privacy mechanism. In this work, we propose a soft-biometric privacy enhancing approach that reduces a given biometric template by eliminating its most important variables for predicting soft-biometric attributes. Training a decision tree ensemble allows deriving a variable importance measure that is used to incrementally eliminate variables that allow predicting sensitive attributes. Unlike previous work, we consider a scenario of function creep attackers with explicit knowledge about the privacy mechanism and evaluated our approach on a publicly available database. The experiments were conducted to eight baseline solutions. The results showed that in many cases IVE is able to suppress gender and age to a high degree with a negligible loss of the templates recognition ability. Contrary to previous work, which is limited to the suppression of binary (gender) attributes, IVE is able, by design, to suppress binary, categorical, and continuous attributes.

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Damer, Naser; Saladie, Alexandra Moseguí; Zienert, Steffen; Wainakh, Yaza; Kirchbuchner, Florian; Kuijper, Arjan; Terhörst, Philipp

To Detect or not to Detect: The Right Faces to Morph

2019

The 12th IAPR International Conference On Biometrics

IAPR International Conference on Biometrics (ICB) <12, 2019, Crete, Greece>

Recent works have studied the face morphing attack detection performance generalization over variations in morphing approaches, image re-digitization, and image source variations. However, these works assumed a constant approach for selecting the images to be morphed (pairing) across their training and testing data. A realistic variation in the pairing protocol in the training data can result in challenges and opportunities for a stable attack detector. This work extensively study this issue by building a novel database with three different pairing protocols and two different morphing approaches. We study the detection generalization over these variations for single image and differential attack detection, along with handcrafted and CNNbased features. Our observations included that training an attack detection solution on attacks created from dissimilar face images, in contrary to the common practice, can result in an overall more generalized detection performance. Moreover, we found that differential attack detection is very sensitive to variations in morphing and pairing protocols.

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Terhörst, Philipp; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

Unsupervised Privacy-enhancement of Face Representations Using Similarity-sensitive Noise Transformations

2019

Applied Intelligence

Face images processed by a biometric system are expected to be used for recognition purposes only. However, recent work presented possibilities for automatically deducing additional information about an individual from their face data. By using soft-biometric estimators, information about gender, age, ethnicity, sexual orientation or the health state of a person can be obtained. This raises a major privacy issue. Previous works presented supervised solutions that require large amount of private data in order to suppress a single attribute. In this work, we propose a privacy-preserving solution that does not require these sensitive information and thus, works in an unsupervised manner. Further, our approach offers privacy protection that is not limited to a single known binary attribute or classifier. We do that by proposing similarity-sensitive noise transformations and investigate their effect and the effect of dimensionality reduction methods on the task of privacy preservation. Experiments are done on a publicly available database and contain analyses of the recognition performance, as well as investigations of the estimation performance of the binary attribute of gender and the continuous attribute of age. We further investigated the estimation performance of these attributes when the prior knowledge about the used privacy mechanism is explicitly utilized. The results show that using this information leads to significantly enhancement of the estimation quality. Finally, we proposed a metric to evaluate the trade-off between the privacy gain and the recognition loss for privacy-preservation techniques. Our experiments showed that the proposed cosine-sensitive noise transformation was successful in reducing the possibility of estimating the soft private information in the data, while having significantly smaller effect on the intended recognition performance.

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Siegmund, Dirk; Prajapati, Ashok; Kirchbuchner, Florian; Kuijper, Arjan

An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures

2018

Progress in Artificial Intelligence and Pattern Recognition

International Workshop on Artificial Intelligence and Pattern Recognition (IWAIPR) <6, 2018, Havana, Cuba>

Lecture Notes in Computer Science (LNCS), 11047

This paper presents a comprehensive defect detection method for two common fabric defects groups. Most existing systems require textiles to be spread out in order to detect defects. This method can be applied when the textiles are not spread out and does not require any pre- processing. The deep learning architecture we present is based on transfer learning and localizes and recognizes cuts, holes and stain defects. Classification and localization is combined into a single system combining two different networks. The experiments this paper presents show that even without adding depth information, the network was able to distinguish between stain and shadow. This method has been successful even for textiles in voluminous shape and is less computationally intensive than other state-of-the-art methods.

978-3-030-01131-4

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Ivanov, Ivelin; Kuijper, Arjan [Advisor]; Wilmsdorff, Julian von [1. Supervisor]; Kirchbuchner, Florian [2.. Supervisor]

CapBed - Preventive Assistance System for the Bed Area Based on Capacitive Sensing

2018

Darmstadt, TU, Master Thesis, 2018

Over the past decades, human activity recognition systems have become a major input modality for building automation. However, those systems also found recent applications in emergency detection, such as recognizing patient activities that may lead to life-threatening situations like falls or heart attacks. The aim of this thesis is to develop a sensor that recognizes whether a person wants to get out of bed. This is to prevent falls by illuminating the path or calling a nurse in time. In addition, such a system can also provide insights into the behavior of the user in the long term. Therefore, a concept of preventive assistance system for the bed area based on capacitive sensing is developed within the scope of this work. To this end, a comparison to other sensor technologies will be established, followed by a detailed overview of the technical background of capacitive proximity sensing. An innovative concept of a device that offers decent performance at an affordable price is proposed. Based on this concept a prototype system was developed and evaluated to investigate its sensing performance and identify possible limitations. As a future outlook, this thesis summarizes the occurred problems and suggests possible modifications that might improve the overall performance of the system.

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Wilmsdorff, Julian von; Kirchbuchner, Florian; Braun, Andreas; Kuijper, Arjan

Eliminating the Ground Reference for Wireless Electric Field Sensing

2018

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <14, 2018, Larnaca, Cyprus>

Capacitive systems are getting more and more attention these days. But many systems today like smart-phone screens, laptops, and non-mechanical buttons use capacitive techniques to measure events within several centimeters of distance. The reason that battery-powered devices don’t have high measurement ranges lies in the principle of capacitive measurement itself - the electrical ground is an inherent part of the measurement. In this paper, we present a method for passive and wireless capacitive systems to eliminate the reference to ground. This bears a couple of advantages for mobile, battery-powered capacitive sensor designs in the field of ambient intelligence. We compare the detection range of normal passive capacitive systems with our new approach. The results show that our improvements result in a higher detection range and higher power efficiency.

978-3-030-03061-2

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Fu, Biying; Kirchbuchner, Florian; Kuijper, Arjan; Braun, Andreas; Gangatharan, Dinesh Vaithyalingam

Fitness Activity Recognition on Smartphones Using Doppler Measurements

2018

Informatics

Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps that use gamification aspects to motivate the user to perform fitness activities, or increase the amount of sports exercise. Thus far, most applications rely on accelerometers or gyroscopes that are integrated into the devices. They have to be worn on the body to track activities. In this work, we investigated the use of a speaker and a microphone that are integrated into a smartphone to track exercises performed close to it. We combined active sonar and Doppler signal analysis in the ultrasound spectrum that is not perceivable by humans. We wanted to measure the body weight exercises bicycles, toe touches, and squats, as these consist of challenging radial movements towards the measuring device. We have tested several classification methods, ranging from support vector machines to convolutional neural networks. We achieved an accuracy of 88% for bicycles, 97% for toe-touches and 91% for squats on our test set.

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Rus, Silvia; Hammacher, Felix; Wilmsdorff, Julian von; Braun, Andreas; Große-Puppendahl, Tobias; Kirchbuchner, Florian; Kuijper, Arjan

Prototyping Shape-Sensing Fabrics Through Physical Simulation

2018

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <14, 2018, Larnaca, Cyprus>

Lecture Notes in Computer Science (LNCS), 11249

Embedding sensors into fabrics can leverage substantial improvements in application areas like working safety, 3D modeling or health-care, for example to recognize the risk of developing skin ulcers. Finding a suitable setup and sensor combination for a shape-sensing fabric currently relies on the intuition of an application engineer. We introduce a novel approach: Simulating the shape-sensing fabric first and optimize the design to achieve better real-world implementations. In order to enable developers to easily prototype their shape-sensing scenario, we have implemented a framework that enables soft body simulation and virtual prototyping. To evaluate our approach, we investigate the design of a system detecting sleeping postures. We simulate potential designs first, and implement a bed cover consisting of 40 distributed acceleration sensors. The validity of our framework is confirmed by comparing the simulated and real evaluation results. We show that both approaches achieve similar performances, with an F-measure of 85% for the virtual prototype and 89% for the real-world implementation.

978-3-030-03061-2

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Scherf, Lisa; Kirchbuchner, Florian; Wilmsdorff, Julian von; Fu, Biying; Braun, Andreas; Kuijper, Arjan

Step by Step: Early Detection of Diseases Using an Intelligent Floor

2018

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <14, 2018, Larnaca, Cyprus>

Lecture Notes in Computer Science (LNCS), 11249

The development of sensor technologies in smart homes helps to increase user comfort or to create safety through the recognition of emergency situations. For example, lighting in the home can be controlled or an emergency call can be triggered if sensors hidden in the floor detect a fall of a person. It makes sense to also use these technologies regarding prevention and early detection of diseases. By detecting deviations and behavioral changes through long-term monitoring of daily life activities it is possible to identify physical or cognitive diseases. In this work, we first examine in detail the existing possibilities to recognize the activities of daily life and the capability of such a system to conclude from the given data on illnesses. Then we propose a model for the use of floor-based sensor technology to help diagnose diseases and behavioral changes by analyzing the time spent in bed as well as the walking speed of users. Finally, we show that the system can be used in a real environment.

978-3-030-03061-2

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Fu, Biying; Mettel, Matthias Ruben; Kirchbuchner, Florian; Braun, Andreas; Kuijper, Arjan

Surface Acoustic Arrays to Analyze Human Activities in Smart Environments

2018

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <14, 2018, Larnaca, Cyprus>

Smart Environments should be able to understand a user’s need without explicit interaction. In order to do that, one step is to build a system that is able to recognize and track some common activities of the user. This way, we can provide a system that provides various services for controlling installed appliances and offering help for every day activities. Applying these services in the users’ environment should make his life more comfortable, easier, and safer. In this paper, we will introduce an embedded sensor system using surface acoustic arrays to analyze human activities in a smart environment. We divided basic activity groups ranging from walking, cupboard closing to falling, including their extended sub-activity groups. We expanded walking into walking barefoot, with shoes and with high heels and further extended closing cupboard with three cupboards locating on different positions. We further investigated the usage of single pickup or a combination of 4 pickups with their effect on the recognition precision. We achieved an overall precision of 97.23% with 10-fold cross validation using support vector machine (SVM) for all sub-activity group combined. Even using one pickup only, we can achieve an overall precision of more than 93%, but we can further increase the precision by using a combination of pickups up to 97.23%.

978-3-030-03061-2

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Braun, Andreas; Majewski, Martin; Loge, Joachim; Kirchbuchner, Florian; Kuijper, Arjan

SurfaceVox - Exploring Sound Control for Gesture-Tracking Interactive Surfaces

2018

14th International Conference on Signal-Image Technology & Internet-Based Systems

International Conference on Signal Image Technology & Internet-Based Systems (SITIS) <14, 2018, Las Palmas de Gran Canaria, Spain>

Almost 100 years ago, the thereminvox was the first electronic musical instrument that could be controlled without contact. With precise positioning of two hands, the player controls pitch and volume of a sine sound, by changing the distance from two antennas. We present SurfaceVox, which combines the technology behind the thereminvox with an additional acoustic sensor to create a musical instrument that combines mid-air and touch gesture control. We explore various scenarios of sound synthesis and combine the system with an augmented reality application. SurfaceVox has been evaluated in a study with thirteen users for input precision, perceived workload, as well as pragmatic and hedonistic qualities of the application.

978-1-5386-9385-8

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Wilmsdorff, Julian von; Kirchbuchner, Florian; Fu, Biying; Braun, Andreas; Kuijper, Arjan

An Exploratory Study on Electric Field Sensing

2017

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <13, 2017, Malaga, Spain>

Electric fields are influenced by the human body and other conducting materials. Capacitive measurement techniques are used in touch-screens, in the automobile industry, and for presence and activity recognition in Ubiquitous Computing. However, a drawback of the capacitive technology is the energy consumption, which is an important aspect for mobile devices. In this paper we explore possible applications of electric field sensing, a purely passive capacitive measurement technique, which can be implemented with an extremely low power consumption. To cover a wide range of applications, we examine five possible use cases in more detail. The results show that the application is feasible both in interior spaces and outdoors. Moreover, due to the low energy consumption, mobile usage is also possible.

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Fu, Biying; Gangatharan, Dinesh Vaithyalingam; Kuijper, Arjan; Kirchbuchner, Florian; Braun, Andreas

Exercise Monitoring On Consumer Smart Phones Using Ultrasonic Sensing

2017

iWOAR 2017

International Workshop on Sensor-based Activity Recognition (iWOAR) <4, 2017, Rostock, Germany>

Quantified self has been a trend over the last several years. An increasing number of people use devices, such as smartwatches or smartphones to log activities of daily life, including step count or vital information. However, most of these devices have to be worn by the user during the activities, as they rely on integrated motion sensors. Our goal is to create a technology that enables similar precision with remote sensing, based on common sensors installed in every smartphone, in order to enable ubiquitous application. We have created a system that uses the Doppler effect in ultrasound frequencies to detect motion around the smartphone. We propose a novel use case to track exercises, based on several feature extraction methods and machine learning classification. We conducted a study with 14 users, achieving an accuracy between 73% and 92% for the different exercises.

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Scherf, Lisa; Kuijper, Arjan [1. Gutachten]; Kirchbuchner, Florian [2. Gutachten]

Human Behavior Analysis and Prediction Based on a Smart Floor

2017

Darmstadt, TU, Bachelor Thesis, 2017

Older adults have the desire to live independently in their own homes for as long as possible. The development of sensor technologies in Smart Homes support this aim by providing sufficient security standards in case of emergencies. For example, a call of emergency can be triggered if a fall of a person is detected by sensors hidden in the floor. However, it is often not only about urgent situations, but also about gradual changes in behavior. Especially when a user is not able to follow his or her daily routine, long-term activity recognition based on location tracking allows for early detection of diseases such as Alzheimer's and dementia and can generally reveal a decrease in the ability to live independently. The focus of this work was the investigation of health related activities and their most accurate measurement only using an intelligent floor based system. Based on these considerations, a method to extrapolate from the collected sensor data to the chosen values is proposed. In addition, a model to detect gradual changes in these health indicators is developed and tested on the smart floor in the Living Lab of Fraunhofer IGD as well as in two apartments in everyday life. The findings of these thesis show a way of using smart floors for health monitoring. The applicability in everyday life could not be shown due to independent problems with the location tracking of the floor during the evaluation period and the lack of additional data for the validation. However, the evaluation under testing conditions showed promising results and an untapped potential of smart floors in health monitoring.

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Fu, Biying; Kirchbuchner, Florian; Wilmsdorff, Julian von; Große-Puppendahl, Tobias; Braun, Andreas; Kuijper, Arjan

Indoor Localization Based on Passive Electric Field Sensing

2017

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <13, 2017, Malaga, Spain>

The ability to perform accurate indoor positioning opens a wide range of opportunities, including smart home applications and location-based services. Smart floors are a well-established technology to enable marker-free indoor localization within an instrumented environment. Typically, they are based on pressure sensors or varieties of capacitive sensing. These systems, however, are often hard to deploy as mechanical or electrical features are required below the surface. They might also have a limited range or not be compatible with different floor materials. In this paper, we present a novel indoor positioning system using an uncommon form of passive electric field sensing, which detects the change in body electric potential during movement. It is easy to install by deploying a grid of passive wires underneath any non-conductive floor surface. The proposed architecture achieves a high position accuracy and an excellent spatial resolution. In our evaluation, we measure a mean positioning error of only 12.7 cm. The proposed system also combines the advantages of very low power consumption, easy installation, easy maintenance, and the preservation of privacy.

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Kirchbuchner, Florian; Fu, Biying; Braun, Andreas; Wilmsdorff, Julian von

New Approaches for Localization and Activity Sensing in Smart Environments

2017

Ambient Assisted Living

Ambient Assisted Living (AAL) <9, 2016, Frankfurt, Germany>

Smart environments need to be able to fulfill the wishes of its occupants unobtrusively. To achieve this goal, it has to be guaranteed that the current state environment is perceived at all times. One of the most important aspects is to find the current position of the in- habitants and to perceive how they move in this environment. Numerous technologies enable such supervision. Particularly challenging are marker-free systems that are also privacy-preserving. In this paper, we present two such systems for localizing inhabitants in a Smart Environment using - electrical potential sensing and ultrasonic Doppler sensing. We present methods that infer location and track the user, based on the acquired sensor data. Finally, we discuss the advantages and challenges of these sensing technologies and provide an overview of future research directions.

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Braun, Andreas; Kirchbuchner, Florian; Wichert, Reiner

Ambient Assisted Living

2016

eHealth in Deutschland

Das Anwendungsfeld Ambient Assisted Living (AAL) beschreibt technische Systeme zur Unterstützung hilfsbedürftiger Personen im Alltag. In den vergangenen Jahren wurde in Deutschland und Europa viel in die Entwicklung und Erprobung von Technologien zur Unterstützung in der häuslichen Umgebung investiert, jedoch häufig ohne nachhaltige Effekte am Markt. Ein fehlender Aspekt war häufig die mangelnde Involvierung aller notwendigen Parteien. In diesem Kapitel werden die Potenziale assistiver Technologien beleuchtet, eine Studie zur Akzeptanz derartiger Technologien bei Senioren vorgestellt sowie ein Ausblick auf zukünftige Entwicklungen in diesem Bereich präsentiert.

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Kutlucan, Osman; Kuijper, Arjan [Prüfer]; Kirchbuchner, Florian [Betreuer]

Barrierefreies Lagersystem zur Unterstützung von Menschen mit eingeschränkter visueller Wahrnehmungsfähigkeit

2016

Darmstadt, TU, Master Thesis, 2016

Im Mittelpunkt dieser Masterarbeit steht die Realisierung eines Lagersystems für Menschen mit eingeschränkter visueller Wahrnehmungsfähigkeit. Das in diesem Rahmen entwickelte Lagersystem verwendet Hand- und Gestenerkennung, Spracherkennung, Sprachsynthese und Sonifikation. Das Ziel des Systems ist es, sowohl eine pervasive Benutzerschnittstelle anzubieten, welche es blinden Benutzern ermöglicht, mit dem Lagerbereich auf eine natürliche Weise zu interagieren, und ein Lagersystem zu haben, welches mit geringem Aufwand konfiguriert werden kann. Daher werden in der Thesis in erster Linie verschiedene verwandte Ansätze und Konzepte betrachtet, woraufhin beschrieben wird, wie das vorgeschlagene Konzept der Thesis entwickelt wurde und warum dieses Konzept sich besser für blinde Benutzer eignet. Um dies zu analysieren, wird das vorgeschlagene System iterativ evaluiert. Im Rahmen der Evaluation wurden Verbesserungsvorschläge aufgenommen, und die Präzision und Effizienz der Implementierung gemessen. Die Ergebnisse der Evaluation dieser Thesis zeigen, dass das Konzept des vorgeschlagenen Systems angemessen ist und von blinden Benutzern gut aufgenommen wird, was z.B. durch einen Teilnehmer der Evaluation bei der Beurteilung eines Subworkflows des Systems mit folgenden Worten bestätigt wurde: "Die Interaktion fühlt sich so an, als wäre man nicht blind". Aber die Implementierung des Systems weist einige Probleme auf, welche sich vorwiegend in der Hand- und Gestenerkennung des Systems zeigen. Mit Behebung dieser Probleme könnte die User Experience einen höheren Grad erreichen, wodurch das System im alltäglichen Leben einsetzbar werden könnte.

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Kirchbuchner, Florian; Große-Puppendahl, Tobias; Hastall, Matthias R.; Distler, Martin; Kuijper, Arjan

Ambient Intelligence from Senior Citizens' Perspectives: Understanding Privacy Concerns, Technology Acceptance, and Expectations

2015

Ambient Intelligence

European Conference on Ambient Intelligence (AmI) <12, 2015, Athens, Greece>

Especially for seniors, Ambient Intelligence can provide assistance in daily living and emergency situations, for example by automatically recognizing critical situations. The use of such systems may involve trade-offs with regard to privacy, social stigmatization, and changes of the well-known living environment. This raises the question of how older adults perceive restrictions of privacy, accept technology, and which requirements are placed on Ambient Intelligent systems. In order to better understand the related concerns and expectations, we surveyed 60 senior citizens. The results show that experience with Ambient Intelligence increases technology acceptance and reduces fears regarding privacy violations and insufficient system reliability. While participants generally tolerate a monitoring of activities in their home, including bathrooms, they do not accept commercial service providers as data recipients. A comparison between four exemplary systems shows that camera-based solutions are perceived with much greater fears than wearable emergency solutions. Burglary detection was rated as similarly important assigned as health features, whereas living comfort features were considered less useful.

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Pavlov, Alexander; Große-Puppendahl, Tobias [Betreuer]; Kirchbuchner, Florian [Betreuer]

Erkennung von Aufstehsituationen mit multimodaler Sensorik

2015

Darmstadt, TU, Master Thesis, 2015

In dieser Arbeit wurde die Erkennung von sechs ausgewählten Aktivitäten mit dem multimodalen Bettsystem untersucht. Bei der multimodalen Sensorik handelt es sich um eine Kombination aus einem Elektropotentialsensor, einem kapazitiven Sensor, einem Passiv-Infrarot-Sensor und einem 3-Achsen-Beschleunigungssensor. Es wurde ein prototypisches System im Multimedia Appliances Lab am Fraunhofer IGD aufgebaut. Anschließend wurden Aufzeichnungen mit zehn Personen durchgeführt und die Trainings- und Testdaten für den Machine-Learning-Algorithmus gesammelt. Aus den aufgenommenen Daten wurden die Merkmale im Zeitbereich, im Zeit-Frequenzbereich und im Frequenzbereich extrahiert. Die beste Klassifikationsgenauigkeit des entwickelten Systems, abgeschätzt mit dem F-Maß, berechnet sich zu 0,999 und wurde bei der Aktivität "Epilepsie" beobachtet. Das über alle Aktivitäten gemittelte F-Maß ist zufriedenstellend und erreicht einen Wert von 0,870.

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Kirchbuchner, Florian; Kuijper, Arjan [Prüfer]; Große-Puppendahl, Tobias [Betreuer]

User Tracking and Behavior Recognition Based on a Capacitive Indoor Localization System

2014

Darmstadt, TU, Master Thesis, 2014

This thesis focused on tracking and analyzing the behavior of elderly people by using in-home monitoring systems. The goal was to add a localization component to an existing system for fall detection as well as to assess the elderly's acceptance of such systems and the corresponding loss of privacy. Therefore, this thesis analyzed the demands on monitoring and technical assistance systems and discussed different models of acceptance measuring. Furthermore, existing approaches to user tracking and behavior analysis are examined. On this basis, an implementation of the tracking functionality, based on a capacitive sensor system, was proposed and evaluated. In addition, a survey among elderly people was conducted by the author and the results are presented in detail. The findings of this thesis showed that capacitive sensing in combination with particle filtering was suitable for user tracking. Moreover, the results of the study emphasized that senior citizens were indeed willing to accept a certain loss of privacy but distinguished between different services and systems. It was also shown that the capacitive system used for this study was received positively in comparison to other systems.