Marc Hesse

Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters

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.

A respiration sensor for a chest-strap based wireless body sensor

In this paper we present a respiration sensor suitable for an integration into a wireless body sensor worn around the chest. The thorax expansion and contraction during in- and exhalation is captured using a force-sensing resistor. Based on the captured thoracic movements, the breaths are determined with a peak detection algorithm. For evaluation, a treadmill experiment with five subjects was conducted using an ergospirometry system as a reference. Overall, an average deviation of -0.32±0.68 min-1 in the respiration rate between the ergospirometry and our sensor was observed. In general, the captured thoracic movements showed breaths as distinctive oscillations, but in some cases a non-optimal pressure transfer between thorax and sensor was observed. Therefore, a mechanical housing mechanism was developed. A comparison of our construction with a respiratory inductance plethysmography (RIP)-based sensor shows a close relationship with the captured thoracic movements during normal and deep respiration.

Semantical occupancy grid mapping framework

In recent decades, mapping has been increasingly investigated and applied in unmanned terrain, aerial, sea, and underwater vehicles. While exploiting various mapping techniques to build an inner representation of the environment, one of the most famous remaining is occupancy grid mapping. It has been applied to all domains in a 2D/3D fashion for localization, mapping, navigation, and safe path traversal. Until now generally active range measuring sensors like LiDAR or SONAR are exploited to build those maps. With this work the authors want to overcome these barriers by presenting an occupancy mapping framework offering a generic sensor interface. The interface handles occupancy grids as inverse sensor models, which may represent knowledge on different semantical decision levels and therefore build up a semantic grid map stack. The framework offers buffered memory management for efficient storing and shifting and further services for accessing the 2D map stack in different cell-wise pre-fused and topometric ways. Within the framework, two novel techniques operating especially with occupancy grids are presented: First, a novel odds based interpolation filter is introduced, which scales grid maps in a Bayesian way. Second, a Supercell Extracted via Variance-Driven Sampling (SEVDS) algorithm is presented which, abstracts the semantical occupancy grid stack to a topometric map. While this work focuses on the frameworks introduction, it is extended by the evaluation of SEVDS against state-of-the-art superpixel approaches to prove its applicability.

Robust estimation of physical activity by adaptively fusing multiple parameters

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.

Towards a comprehensive power consumption model for wireless sensor nodes

Energy efficiency is the most outstanding design criterion for wireless sensor nodes and especially wireless body sensors. Because a detailed measurement of the systems power consumption is not possible during the design process and often too complex for already manufactured devices, the power consumption has to be estimated. This leads to the need for a comprehensive and modular model for the power consumption of WSNs, which is proposed in this work. Due to the modular structure of the model the user is able to get a first estimate in an early stage of the design process (e.g. choose components) and to get a more accurate estimation later in the design process by lowering the abstraction level. This tackles the demanding trade-off between accuracy and usability in modeling.

AMiRo: A Mini Robot as Versatile Teaching Platform

Since robots become increasingly ubiquitous and system complexity increases, teaching university students in robotics is essential for modern studies in computer science. This work thus presents the education curriculum around the Autonomous Mini Robot (AMiRo) as a solution to this challenge. The goal is to provide insights to the various fields related to robotics and allow students to specialize in a wide range of topics, depending on their interests. Concept as well as platform have been evaluated and the results reveal a generally positive feedback as well as some issues, for which according solutions are proposed.

Generic Architecture for Modular Real-time Systems in Robotics

With the continuous progress in robotics and application of such systems in evermore scenarios, safety and flexibility become increasingly important aspects and new designs should thus emphasize real-time capability and modularity. This work points out all related topics for such an endeavor and proclaims to move from conventional bottom-up design to more holistic approaches. Based on experience gained with the modular mini robot platforms BeBot and AMiRo, a novel generic modular architecture is proposed that offers high flexibility and system wide real-time capability.

A connected chair as part of a smart home environment

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 users actions. The actuators are used to adjust the chair to the actual users 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.

An Adaptive Acknowledgement On-demand Protocol for Wireless Sensor Networks

The concept of packet acknowledgement in wireless communication networks is crucial for reliable data transmission. However, reliability comes with the cost of an increased duty cycle of the network. This is due to the additional acknowledgement time for every single data packet sent. Therefore, energy consumption and latency of all sensor nodes is increased whilst the overall throughput in the network decreases. This paper contributes an adaptive acknowledgement on-demand protocol for wireless sensor networks with star network topology. The goal is to tackle the trade-off between energy efficiency and reliable data transmission. The proposed protocol is able to detect network congestion in real time by constantly monitoring the overall packet delivery ratio for each sensor node. In case the packet delivery ratio of any sensor nodes in the network is dropped significantly (e.g. due to environmental changes), the protocol switches automatically to a more reliable data transmission mode utilizing acknowledgements concerning the affected sensor nodes. Our proposed method is tested and evaluated based on a specific hardware implementation and the corresponding results are discussed in this paper.

Detailed Estimation of Cognitive Workload with Reference to a Modern Working Environment

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.