Florian Fischer

Ankit Bhattarai, Hannah Selder, Florian Fischer et al.

Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuated simulated users within minutes, reducing training times by up to 98%. A workshop study with 12 interaction designers revealed that MyoInteract allowed novices in biomechanical RL to successfully setup, train, and assess goal-directed user movements within a single session. By transforming biomechanical RL from a days-long expert task into an accessible hour-long workflow, this work significantly lowers barriers to entry and accelerates iteration cycles in HCI biomechanics research.

Florian Fischer, Arthur Fleig, Markus Klar et al.

Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC is based on the assumption that humans aim to control their body optimally, within the constraints imposed by body, environment, and task. In this paper, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via muscle control signals, and estimated from observations. Between-trial variability arises from signal-dependent control noise and observation noise. We compare four different models from optimal control theory and evaluate to what degree these models can replicate movements in the case of mouse pointing. We introduce a procedure to identify parameters that best explain observed user behavior. To support HCI researchers in simulating, analyzing, and optimizing interaction movements, we provide the Python toolbox OFC4HCI. We conclude that OFC presents a powerful framework for HCI to understand and simulate motion of the human body and of the interface on a moment by moment basis.

Florian Fischer, Miroslav Bachinski, Markus Klar et al.

Among the infinite number of possible movements that can be produced, humans are commonly assumed to choose those that optimize criteria such as minimizing movement time, subject to certain movement constraints like signal-dependent and constant motor noise. While so far these assumptions have only been evaluated for simplified point-mass or planar models, we address the question of whether they can predict reaching movements in a full skeletal model of the human upper extremity. We learn a control policy using a motor babbling approach as implemented in reinforcement learning, using aimed movements of the tip of the right index finger towards randomly placed 3D targets of varying size. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal with the curse of dimensionality, we use a simplified second-order muscle model, acting at each degree of freedom instead of individual muscles. The results confirm that the assumptions of signal-dependent and constant motor noise, together with the objective of movement time minimization, are sufficient for a state-of-the-art skeletal model of the human upper extremity to reproduce complex phenomena of human movement, in particular Fitts' Law and the 2/3 Power Law. This result supports the notion that control of the complex human biomechanical system can plausibly be determined by a set of simple assumptions and can easily be learned.

Florian Fischer, Matthias Niedermaier, Thomas Hanka et al.

More and more industrial devices are connected to IP-based networks, as this is essential for the success of Industry 4.0. However, this interconnection also results in an increased attack surface for various network-based attacks. One of the easiest attacks to carry out are DoS attacks, in which the attacked target is overloaded due to high network traffic and corresponding CPU load. Therefore, the attacked device can no longer provide its regular services. This is especially critical for devices, which perform real-time operations in industrial processes. To protect against DoS attacks, there is the possibility of throttling network traffic at the perimeter, e.g. by a firewall, to develop robust device architectures. In this paper, we analyze various concepts for secure device architectures and compare them with regard to their robustness against DoS attacks. Here, special attention is paid to how the control process of an industrial controller behaves during the attack. For this purpose, we compare different schedulers on single-core and dual-core Linux-based systems, as well as a heterogeneous multi-core architecture under various network loads and additional system stress.

Florian Fischer, Arthur Fleig, Markus Klar et al.

In this paper we explore the Linear-Quadratic Regulator (LQR) to model movement of the mouse pointer. We propose a model in which users are assumed to behave optimally with respect to a certain cost function. Users try to minimize the distance of the mouse pointer to the target smoothly and with minimal effort, by simultaneously minimizing the jerk of the movement. We identify parameters of our model from a dataset of reciprocal pointing with the mouse. We compare our model to the classical minimum-jerk and second-order lag models on data from 12 users with a total of 7702 movements. Our results show that our approach explains the data significantly better than either of these previous models.

Matthias Niedermaier, Florian Fischer, Alexander von Bodisco

Programmable Logic Controllers are used for smart homes, in production processes or to control critical infrastructures. Modern industrial devices in the control level are often communicating over proprietary protocols on top of TCP/IP with each other and SCADA systems. The networks in which the controllers operate are usually considered as trustworthy and thereby they are not properly secured. Due to the growing connectivity caused by the Internet of Things (IoT) and Industry 4.0 the security risks are rising. Therefore, the demand of security assessment tools for industrial networks is high. In this paper, we introduce a new fuzzing framework called PropFuzz, which is capable to fuzz proprietary industrial control system protocols and monitor the behavior of the controller. Furthermore, we present first results of a security assessment with our framework.

Matthias Niedermaier, Florian Fischer, Dominik Merli et al.

The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect "unwanted" devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.