Towards Adaptive Biodynamic Feedthrough Modeling for Touchscreen Applications

Towards Adaptive Biodynamic Feedthrough Modeling for Touchscreen Applications

Iancu, Alexandru (TU Delft Aerospace Engineering)

Pool, D.M. (mentor)
Mulder, Max (graduation committee)
van Paassen, M.M. (graduation committee)
Merino Martinez, R. (graduation committee)

Delft University of Technology

Aerospace Engineering

2024-04-11

Given the expected increase of automation in the vehicles of the future, touchscreens are expected to be used in a wide variety of scenarios, including the ones that can become safety-critical.
One disadvantage of the direct interaction approach that characterizes touchscreen operation is the feedthrough of accelerations through the human body that can induce unwanted activations, leading to unsafe situations. The current paper investigates the feasibility of applying a method that can track the biodynamic responses of the elbow, wrist and index finger using cameras in a stereo configuration and an open-source pose estimator (OpenPose). An experiment with six participants was performed to understand whether different neuromuscular settings, task instructions or degrees of arm extension induce an adaptive behavior of the biodynamic feedthrough to the recorded joints. The outcomes of the experiments showed that the finger is the body part that exhibits the most adaptive behavior in terms of feedthrough, being dependent on whether it is interacting with the screen with no velocity, moving on the screen or not touching the screen. Moreover, the difference between keeping the upper limb relaxed or stiff would need to be taken into account for the specified body part when the finger is not touching the screen, showing a decrease of 29% in the mean RMS of the vertical feedthrough component when the stiff condition is exhibited. The adaptive nature of the biodynamic feedthrough was also demonstrated concerning two different cases of arm extension (close and far from the body). This study proved the feasibility of using a linear mass-spring-damper model to the data gathered with the camera-based system to describe the feedthrough effects for all the body parts, neuromuscular settings and tasks, being able to explain on average at least 75% of the variance of the raw signals. The approach presented in this study can be further refined toward reaching real-time capabilities, which is a key step in accurately identifying the highly adaptive and subjective nature of the feedthrough of the accelerations.

Touchscreens
Pose estimators
Turbulence
Biodynamic feedthrough

http://resolver.tudelft.nl/uuid:871614db-01b5-415f-a1cd-5527d90dec68

Student theses

master thesis

© 2024 Alexandru Iancu