Choosing the right control strategy has always been an important aspect of autonomous localization and navigation. One of the ways of doing it is to identify the surface characteristics and assign the appropriate controller. The most prominent ways of recognizing the surface type are either to do it visually or using proprioceptive signals induced by robot–environment interactions. However, these interactions are very complex and to obtain the full perspective the classifier should make the decision based on data from several diverse sources. A potential source, which has not been extensively studied in the literature, is power consumption measured at the locomotion devices. The question which this study aims to answer is whether this signal carries enough unique information to boost the classification result in a significant way. The analysis incorporates usage of two different machine learning algorithms, namely a convolutional neural network and gradient boosted trees, to determine the difference in classification results between training on vibration data only and on a combination of vibration and estimated power consumption data. The results show that the gap between these training datasets is meaningful and the additional information about power consumption might positively impact the classification outcome.

Stoma surgery may concern patients with colorectal cancer and inflammatory bowel disease. More than half of patients diagnosed with colon cancer present at an advanced stage, and palliative treatment may involve stoma formation. This type of surgery may change the patient’s life strongly, therefore they should receive special medical care. The paper presents the assumptions, concept, and architecture of the Stoma-Alert diagnosis support system, including the assessment of stoma complications and issuing recommendations for treatment and care. In particular, the telediagnosis subsystem, which is essential in the pandemic era, supports the assessment of stoma status on the basis of photos and symptoms provided remotely by the patient. The first experiences in the development of the system and interaction with it, both on the part of doctors and medical personnel, as well as the patients themselves, are also presented.

We present the methodology and results of designing a robot for presenting epileptic seizure. The goal of the project was to create a prototype device for series of pilot workshops for improving teachers reactions during an epileptic seizure and their attitudes towards epileptic students. Various design goals had to be accomplished to fit the needs of all of the stakeholders. We used a co-design (participatory design) approach though series of workshops participated by members of the association for epileptic patients, students, and faculty, members of the biomedical engineering and robotics departments, teachers, psychologists and medical specialists (epileptologist, neurologist). As the result of the co-designing process, an inexpensive robot was created and used in a series of 10 pilot workshops with 217 participants, mainly teachers of primary and middle schools. During the workshops, teachers improved their understanding of epilepsy and suggested various improvements for next runs of the workshop. Co-creation strategy used during the project resulted in a prototype robot that combined goals of various stakeholders, such as an accurate presentation of an epileptic seizure, ease of use and control, lightweight while preserving the dignity of persons with epilepsy.

In this paper we demonstrate one of the prototypes that were constructed by an interdisciplinary team of roboticists, designers and therapists working with mentally disabled children. We focus on how end-users participated in (and were motivated during) the design process and how we applied several robotic technologies to develop an interactive tool (called Sensor Box) for integrated sensory therapy.

In this paper, we explain our approach to designing a robot-based environment for aiding in the therapy of severely disabled children. During a one year project, a group of roboticists, designers and therapists worked together on developing novel tools to support both the therapy of autistic children and the personnel working with them. The outcome of the participatory design process are therapy devices fitting the needs of professionals that can be programmed by them.

This paper introduces a new control method for pneumatic actuators, called "Proportional Position and Stiffness (PPS)" controller. The PPS method provides both position and stiffness control for a robot joint driven by a pneumatic cylinder with four ON-OFF valves. In addition, the proposed control system consumes much less compressed air than comparable strategies. These features make the PPS method highly suitable to application in mobile robots.