Human-Machine-Centered Design Methods

Human-machine-centered methods are crucial for the development of human-oriented technologies that synergistically interact with their users. Current research indicates that human factors and technical aspects should be equally considered to design efficient solutions that are accepted by users. For this purpose, we combine user and expert studies with established engineering design methods frameworks for human-machine-centered design.

Our research is concerned with identifying and modeling human factors and the development of design methods. To this end, we aim at a holistic understanding of the influence of human factors on the development of robotic systems and considering these relations methodically.

 

Current projects on this topic

Learning Predictive Maintenance of Fleets of Networked Systems

Funded by the Bayerische Forschungsstiftung AZ-1586-23

The project aims at advancing predictive maintenance for networked device fleets using learning approaches and integrating expert knowledge. To this end, we will combine machine learning with physical models and analyze and evaluate data flows between systems as well as integrate expertise on system behavior and failure modes. The resulting predictive maintenance approach for networked systems will be transferred to various classes of systems. Besides investigating industrial applications, we will create a fleet of mobile robots to demonstrate the capabilities of the predictive maintenance approach and make it available to academia, industry, and beyond.

Active transfer learning with neural networks through human-robot interaction (TRAIN)

Funded by the DFG: BE 5729/16

In order to be able to use autonomous robots flexibly in interaction with humans in the future, procedures are needed that enable the learning of various motor and manipulation skills and that can also be applied not only by experts. We aim to improve the learning of robot skills with neural networks, taking into account human feedback as well as the experience and instructions of the users. To implement this systematically, we evaluate subjective feedback and physiological data from user studies and develop assessment criteria for the development of human-oriented methods of transfer learning and the shared autonomy of humans and robots.

More information can be found here.

  • Cansev, M. E., Xue, H., Rottmann, N., Bliek, A., Miller, L. E., Rueckert, E., & Beckerle, P. (2021). Interactive Human–Robot Skill Transfer: A Review of Learning Methods and User Experience. Advanced Intelligent Systems, 3(7), 2000247.
  • Denz, R., Demirci, R., Cansev, M. E., Bliek, A., Beckerle, P., Rueckert, E., & Rottmann, N. (2021, December). A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning. In 2021 20th International Conference on Advanced Robotics (ICAR) (pp. 1109-1115). IEEE.

EFficient and Fast text ENtry for persons with motor Disabilities of neuromuscular orIgin (EFFENDI)

Funded by the DFG: FE 936/6

People with motor impairments are often unable to operate a computer keyboard efficiently and therefore need alternative input methods. For users with neuromuscular diseases, this project will develop alternatives that can adapt to the individual symptoms of individual persons through modular, multi-sensory interfaces. The practical use of the resulting input devices is ensured as part of a human-centered development process through the continuous involvement of the target group.

  • Gür, D., Schäfer, N., Kupnik, M., & Beckerle, P. (2020). A human–computer interface replacing mouse and keyboard for individuals with limited upper limb mobility. Multimodal Technologies and Interaction, 4(4), 84.
  • Andreas, D., Six, H., Bliek, A., & Beckerle, P. (2022). Design and Implementation of a Personalizable Alternative Mouse and Keyboard Interface for Individuals with Limited Upper Limb Mobility. Multimodal Technologies and Interaction, 6(12), 104.

Completed projects on this topic

Users’ body experience and human-machine interfaces in (assistive) robotics

Funded by the DFG: BE 5729/3 & 11

The scientific network dealt with the body experience of individuals who use assistive or wearable robots. For a better understanding of technical possibilities to improve experiences, the participating scientists analyzed measures for the assessment of body representations and their consideration in novel design methods. This includes the identification of suitable perceptual channels and supports the development of new human-machine interfaces and human-in-the-loop experiments, i.e., robot hand/leg illusions.

Further information on the activities of the network can be found here.

  • Beckerle, P., Kõiva, R., Kirchner, E. A., Bekrater-Bodmann, R., Dosen, S., Christ, O., … & Lenggenhager, B. (2018). Feel-good robotics: requirements on touch for embodiment in assistive robotics. Frontiers in neurorobotics, 12, 84.
  • Beckerle, P., Castellini, C., & Lenggenhager, B. (2019). Robotic interfaces for cognitive psychology and embodiment research: a research roadmap. Wiley Interdisciplinary Reviews: Cognitive Science, 10(2), e1486.

Human-oriented methods for intuitive and fault-tolerant control of wearable robotic devices

Supported by the “Athene Young Investigator” program of TU Darmstadt

In this project, control approaches for wearable robotics systems for movement support and augmentation were developed to provide efficient and natural support and prevent users from feeling “controlled by the robot”. Psychophysical experiments of how users experience device elasticity help to tune adaptive impedance control to ensure versatile locomotion and fault tolerance. Human-in-the-loop experiments were applied to investigate the body scheme integration of wearable robotics systems by their users.

  • Stuhlenmiller, F., Schuy, J., & Beckerle, P. (2018). Probabilistic elastic element design for robust natural dynamics of structure-controlled variable stiffness actuators. Journal of Mechanisms and Robotics, 10(1), 011009.
  • Stuhlenmiller, F., Perner, G., Rinderknecht, S., & Beckerle, P. (2019). A stiffness-fault-tolerant control strategy for reliable physical human-robot interaction. In Human Friendly Robotics: 10th International Workshop (pp. 3-14). Springer International Publishing.

Optimized measurement, adjustment, and manufacturing of lower limb prosthetic sockets

Funded by AiF / IGF: 18873 N / 2

In this project, methods for measuring, adapting, and manufacturing lower limb prosthetic socket systems were developed. Based on biomechanical measurements, the know-how of orthopedic specialists, and the assessment evaluation by people with amputation, models of the interaction between residual limb and socket were developed and suggestions to use them in technical design processes were made.

  • Noll, V., Eschner, N., Schumacher, C., Beckerle, P., & Rinderknecht, S. (2017). A physically-motivated model describing the dynamic interactions between residual limb and socket in lower limb prostheses. Current Directions in Biomedical Engineering, 3(1), 15-18.
  • Noll, V., Whitmore, S., Beckerle, P., & Rinderknecht, S. (2019). A sensor array for the measurement of relative motion in lower limb prosthetic sockets. Sensors, 19(12), 2658.