Chewing not only converts food chunks to digestible proportions, it also conveys audible acoustics resulting in a perception on the type and condition of the food being eaten. As biomedical engineers, we may want to reproduce the same eating experience for those who cannot chew or for those who have allergic reactions to some foods. But to understand this psychoacoustic phenomenon better, it is crucial to understand what produces the sound of specific foods. The purpose of this paper is to present a straightforward methodology to produce audible acoustics from a notoriously loud Finnish delicacy and analyse the sound produced. One hundred samples of leipäjuusto and one hundred samples of Gouda cheese for controls were subjected to shear between a bamboo board and a wetted blade. All leipäjuusto samples and none of the Gouda cheese samples produced audible squeaks. A 0.1-s delay between blade displacement and sound production was observed. We attribute this delay to the buildup to release. The frequency spectra from pushing and pulling movements were observed to have only negligible differences. This indicated that the internal structure between events did not change. Therefore, the hypothesis that a disruptive event underlies the squeaking process is less plausible.

In anaesthesia and intensive care medicine, managing the multitude of signal and substance lines connected to patients presents challenges, risking dislocations, incorrect treatment, and increased workload for healthcare professionals. Existing solutions for signal and substance line management have seen limited acceptance, lacking universality and flexibility. Addressing these challanges, the “intelligent Medical Organization Device” (iMOD) was developed as a modular tool. iMOD, evaluated through simulator training, demonstrated positive results in intuitiveness and acceptance. Feedback highlighted areas for improvement, focusing on line insertion and fixation strength. The organization device may be a component in reducing treatment time and costs, increasing patient safety and improving the satisfaction of patients and healthcare professionals.

Introduction

The current medical S3-guideline on dementia recommends the use of cognitive training and stimulation, e.g. through shared games, for mild cognitive impairment and mild dementia. The use of cobots, which allow direct human-robot collaboration, enables people with paralyzed upper limbs to actively participate in social activities. This research presents a novel approach through the development of a voice-controlled board game specifically tailored to the inclusion needs of people with severe disabilities.

Methods

The human voice commands recorded via USB microphones are digitally filtered. Speech recognition of the control commands is performed using a Convolutional Neural Network (CNN) based on the VGG-16 architecture. The robot´s activity is controlled utilizing the ROS 2 robot operating system.

Results

A portable table-based complete system with a 3D-printed Tic-Tac-Toe playing field and robot assistant for severely disabled paralyzed people has been developed. The robot control system employs a pick-and-place mechanism, seamlessly integrating with speech recognition to enhance gameplay interaction. The CNN model achieves an impressive accuracy rate of 97%, ensuring reliable speech recognition performance throughout gameplay.

Conclusion

The targeted integration of robotic technologies and artificial intelligence opens up new avenues in the prevention of mental illness, care support and inclusion of older and disabled people.