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Introduction Telehealth and its usage strongly depend on regulatory frameworks and user acceptance. During the COVID-19 pandemic, physiotherapists, occupational therapists, speech-language therapists and their patients experienced restrictions regarding the usual face-to-face therapy. Teletherapy has become a highly discussed medium for providing therapy services. This study aimed at assessing Austrian therapists’ attitudes towards teletherapy, including perceived barriers, during and before the COVID-19 lockdown. Further interest referred to therapists’ technical affinity and experiences with the application of teletherapy. Methods Therapists ( n = 325) completed an online survey amid the COVID-19 lockdown in 2020. Retrospective indications referred to the time prior to the lockdown. Ratings were opposed across the three therapeutic professions. Subgroup analyses investigated the role of gender and age regarding technical affinity. Measures included custom-made attitudinal statements towards teletherapy and the standardized TA-EG survey. Results The COVID-19 lockdown caused attitude changes towards teletherapy – for example, in terms of interest ( r = 0.57, p > 0.01), perceived skills for performance of teletherapy ( r = 0.33, p > 0.01) and perceived need for physical contact with patients ( r = 0.35, p > 0.01). Regarding technical affinity, women reported significantly higher values than men did ( r = 0.32, p > 0.01). Nearly half of the participants already applied teletherapy, with mainly positive ratings regarding perceived skills and feasibility. Barriers identified were missing or unstable reimbursement policies by insurance companies and therapeutic software with guaranteed data security. Discussion Austrian therapists indicate a relatively high level of telehealth positivity, with an improvement in the course of the COVID-19 lockdown. However, therapists outline the need for stable reimbursement policies and secure software solutions.

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Abstract-As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source or not from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 288 declaration experiments were carried out at different robot-to-source distances. Based on these readings, two machine learning techniques (ANN, SVM) were evaluated in terms of their classification performance. With learning parameters that were optimised by grid search, a maximal hit rate of approximately 87.5% could be obtained using a support vector machine.

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