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Detailed estimation of cognitive workload with reference to a modern working environment 9

Detailed estimation of cognitive workload with reference to ...

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Doctoral Consortium - 9th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2016

作者： Hörmann, Timm Hesse, Marc Christ, Peter Adams, Michael Menßen, Christian Rückert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Universitätsstr. 21-23 Bielefeld33615 Germany

ISBN: (纸本)9783319547169

In modern industry, employees are confronted with ever more complex working tasks. As a consequence, cognitive workload of the employees rises. This makes automatic estimation of cognitive workload a key subject of research. Such an estimate would enable adaptive Human- Machine Interaction that could be used to fit the employees’ workload accordingly to their needs. In this work, a tablet interaction study is presented that is designed to induce cognitive workload. Supervised machine learning methods are used to estimate the induced cognitive workload based on features taken from heart rate, electrodermal activity and user interaction (touch input). Ground truth data is obtained from the subjects’ self-reported cognitive workload. Inter-subject accuracy of the best learner is 74.1% for the detailed 5-class problem and 96.0% for the simplified binary problem. © Springer International Publishing AG 2017.

关键词： Heart

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From CPU to FPGA - Acceleration of Self-Organizing Maps for Data Mining

From CPU to FPGA - Acceleration of Self-Organizing Maps for ...

引用

International Joint Conference on Neural Networks

作者： Jan Lachmair Thomas Mieth René Griessl Jens Hagemeyer Mario Porrmann Cognitronics and Sensor Systems Group CITEC Bielefeld University

ISBN: (纸本)9781509061839

Big data and machine learning applications are posing steadily increasing challenges to the used compute platforms in terms of performance and energy efficiency. In this paper we utilize the highly scalable heterogeneous server platform RECS for evaluation of a wide variety of hardware platforms ranging from general purpose CPUs via ARM-based SoCs to GPGPUs and FPGAs. The self-organizing map, a popular neural network model for unsupervised clustering and dimensionality reduction, is used as a typical example for machine learning applications in the big data domain. Optimized implementations of the algorithm have been developed for each of the target architectures. An in-depth analysis of the achieved performance and energy efficiency for a wide variety of application parameters shows that no single architecture performs best in terms of energy efficiency for the complete design space. In our study, ARM-based SoCs achieved the highest efficiency for small network sizes while FPGAs and GPGPUs perform best for large data sets. Compared to an implementation based on the Matlab SOM toolbox, our optimized multi-threaded CPU implementation achieves two orders of magnitude higher performance and energy efficiency. Large simulations especially benefit from the FPGA implementation, which outperforms the optimized CPU implementation by a factor of 220 and provides a 28-times higher energy efficiency.

关键词： Machine Learning Big Data Self-Organizing Map SOM GPGPU FPGA FPGA-Cluster ARM-SoC Performance Evaluation self-organising feature maps big data Energy efficiency Field programmable gate arrays central processing units Performance Evaluation GRHL3 gene Artificial Intelligence Machine Learning data mining Neural Networks (Computer)

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Fine-grained prediction of cognitive workload in a modern working environment by utilizing short-term physiological parameters 9

Fine-grained prediction of cognitive workload in a modern wo...

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9th International Conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2016 - Part of 9th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2016

作者： Hörmann, Timm Hesse, Marc Christ, Peter Adams, Michael Menßen, Christian Rückert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany

ISBN: (纸本)9789897581700

In this paper we present a method to predict cognitive workload during the interaction with a tablet computer. To set up a predictor that estimates the reflected self-reported cognitive workload we analyzed the information gain of heart rate, electrodermal activity and user input (touch) based features. From the derived optimal feature set we present a Gaussian Process based learner that enables fine-grained and short term detection of cognitive workload. Average inter-subject accuracy in 10-fold cross validation is 74.1 % for the fine-grained 5-class problem and 96.0 % for the binary class problem. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Physiological models

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A connected chair as part of a smart home environment

A connected chair as part of a smart home environment

引用

International Workshop on Wearable and Implantable Body sensor Networks (BSN)

作者： Marc Hesse André Frank Krause Ludwig Vogel Bhavin Chamadiya Michael Schilling Thomas Schack Thorsten Jungeblut Cognitronics and Sensor Systems Group Cluster of Excellence Cognitive Interaction Technology (CITEC) at Bielefeld University Bielefeld Germany Neurocognition and Action - Biomechanics Group Cluster of Excellence Cognitive Interaction Technology (CITEC) at Bielefeld University Bielefeld Germany Advanced Engineering Group at Hella KGaA Hueck & Co. Lippstadt Germany

ISBN: (纸本)9781509062454

The connected chair is part of the Supportive Personal Coach in the KogniHome project, which offers guided fitness training, relaxation, and assistive functions. The chair comes with integrated sensors, actuators, control logic and wireless transceiver. The sensors are able to measure respiration and heart rate as well as the user's actions. The actuators are used to adjust the chair to the actual user's needs and the transceiver is used to connect wireless sensor nodes and to exchange data with a base station. Additional value is generated by connecting the chair to the smart home environment, which enables and expands novel features and applications.

关键词： Actuators Intelligent sensors Heart rate Radar Training Tracking

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A resource-efficient multi-camera GigE vision IP core for embedded vision processing platforms

A resource-efficient multi-camera GigE vision IP core for em...

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International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015

作者： Ibraheem, Omar W. Irwansyah, Arif Hagemeyer, Jens Porrmann, Mario Rueckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany

ISBN: (纸本)9781467394062

In vision processing systems, many applications require multi-camera support. For the connection of the cameras to the processing system, multiple interfaces and a platform capable of handling sustained high data rates are essential. To cope with these requirements, a hardware-based solution using FPGA technology is advisable, especially when targeting space and energy constrained embedded systems. The aim of this work is to develop and implement an FPGA-based scalable and resourceefficient multi-camera GigE Vision IP core for video and image processing. To reduce the number of interfaces needed, the IP core supports the connection of multi-camera interfaces to a single Gigabit Ethernet port using an Ethernet switch. The multicamera GigE Vision IP core is able to extract the raw video data from multiple GigE Vision video streams, reconstruct the video frames from every camera and pass these data for further processing. To test the system, four GigE Vision cameras are used. The IP core is implemented on a Xilinx Virtex-4 FPGA and integrated in a complete video processing platform for a full system realization. In addition to the IP core, bilinear interpolation for image demosaicing with Bayer pattern and an automatic white balance algorithm are implemented for evaluation of the platform. Benchmarking of the hardware implementation has been performed with a total resolution of up to 2048x2048 pixels. Achieved frame rates vary from 25 fps to 345 fps depending on the selected resolution and on the number of used cameras. © 2015 IEEE.

关键词： Field programmable gate arrays (FPGA)

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FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking

FPGA-based circular hough transform with graph clustering fo...

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International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015

作者： Irwansyah, Arif Ibraheem, Omar W. Hagemeyer, Jens Porrmann, Mario Rueckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany

ISBN: (纸本)9781467394062

Shape-based object detection and recognition are frequently used methods in the field of computer vision. A well-known algorithm for circle detection is the Circular Hough Transform (CHT). This Hough Transform algorithm needs a huge memory space and large computational resources. Field Programmable Gate Array (FPGA)-based hardware accelerators can be used to efficiently handle such compute-intensive applications. In this paper, we present a resource-efficient FPGA-based architecture for the CHT algorithm. Additionally, we introduce a unique approach by combining the CHT algorithm with graph clustering. The combination of these algorithms and their implementation on a Xilinx Virtex-4 FPGA is used to support real-time vision-based multi-robot tracking. Furthermore, an efficient architecture is proposed to significantly reduce the required memory in the CHT module. For the Graph Clustering module, a multiplier-less distance calculation unit is implemented, significantly reducing the required FPGA resources. The proposed CHT design can handle multi-robot localization with an accuracy of 97 %, supporting a maximum video resolution of 1024x1024 with 128 frames per second, resulting in 134 MPixel/s. Our design provides significantly higher throughput compared to other implementations on embedded processors, FPGAs, and general purpose CPUs. Compared to an OpenCV implementation on a 3.2 GHz desktop CPU, our implementation achieves a speed-up of more than 5.7. © 2015 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Robust estimation of physical activity by adaptively fusing multiple parameters 12

Robust estimation of physical activity by adaptively fusing ...

引用

12th IEEE International Conference on Wearable and Implantable Body sensor Networks, BSN 2015

作者： Hormann, Timm Christ, Peter Hesse, Marc Ruckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld33619 Germany

ISBN: (纸本)9781467372015

Raising the awareness of being physically active by utilizing wearable body sensors has become a popular research topic. Recent approaches combine physical and physiological information to obtain a precise prediction of a person;s physical activity ratio. However, the error in the determination of physical activity due to invalid physiological values that are resulting from underlying signal disturbances, has so far not been considered. We therefore present a robust measure of activity that fuses accelerometer data, heart rate and other personalized features, and is adaptively responding to missing physiological sensor data. To set up the model, we make use of regression analysis (MARS). Our findings indicate the need for considering signal quality when estimating physical activity. The predictive model shows close agreement (R2 = 0.97) to the reference from indirect calorimetry, even if the physiological information is partly corrupted. © 2015 IEEE.

关键词： Regression analysis

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System-Level Analysis of Network Interfaces for Hierarchical MPSoCs 8

System-Level Analysis of Network Interfaces for Hierarchical...

引用

8th International Workshop on Network on Chip Architectures, NoCArc 2015

作者： Ax, Johannes Sievers, Gregor Flasskamp, Martin Kelly, Wayne Jungeblut, Thorsten Porrmann, Mario Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany Science and Engineering Faculty Queensland University of Technology Brisbane Australia

ISBN: (纸本)9781450339636

Network Interfaces (NIs) are used in Multiprocessor System- on-Chips (MPSoCs) to connect CPUs to a packet switched Network-on-Chip. In this work we introduce a new NI archi- tecture for our hierarchical CoreVA-MPSoC. The CoreVA- MPSoC targets streaming applications in embedded systems. The main contribution of this paper is a system-level analysis of different NI configurations, considering both software and hardware costs for NoC communication. Different configura- tions of the NI are compared using a benchmark suite of 10 streaming applications. The best performing NI configuration shows an average speedup of 20 for a CoreVA-MPSoC with 32 CPUs compared to a single CPU. Furthermore, we present physical implementation results using a 28 nm FD-SOI stan- dard cell technology. A hierarchical MPSoC with 8 CPU clusters and 4 CPUs in each cluster running at 800MHz requires an area of 4.56mm2. © 2015 ACM.

关键词： Network-on-chip

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Robust estimation of physical activity by adaptively fusing multiple parameters

Robust estimation of physical activity by adaptively fusing ...

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International Workshop on Wearable and Implantable Body sensor Networks (BSN)

作者： Timm Hörmann Peter Christ Marc Hesse Ulrich Rückert Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld

Raising the awareness of being physically active by utilizing wearable body sensors has become a popular research topic. Recent approaches combine physical and physiological information to obtain a precise prediction of a person;s physical activity ratio. However, the error in the determination of physical activity due to invalid physiological values that are resulting from underlying signal disturbances, has so far not been considered. We therefore present a robust measure of activity that fuses accelerometer data, heart rate and other personalized features, and is adaptively responding to missing physiological sensor data. To set up the model, we make use of regression analysis (MARS). Our findings indicate the need for considering signal quality when estimating physical activity. The predictive model shows close agreement (R 2 = 0.97) to the reference from indirect calorimetry, even if the physiological information is partly corrupted.

关键词： Heart rate Electrocardiography Mathematical model Predictive models Mars Accelerometers Estimation

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FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking

FPGA-based circular hough transform with graph clustering fo...

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International Conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Arif Irwansyah Omar W. Ibraheem Jens Hagemeyer Mario Porrmann Ulrich Rueckert Cognitronics and Sensor Systems Group Bielefeld University Bielefeld Germany

关键词： Circular Hough Transform Image processing Multi-robot systems Object detection and recognition Reconfigurable architectures multi-robot systems Reconfigurable architectures Hough transform SLC5A7 gene Image processing Field programmable gate arrays images computer vision Line graph circle detection hardware accelerator

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