ABSTRACT
The events of the past 2 years related to the pandemic have shown that it is increasingly important to find new tools to help mental health experts in diagnosing mood disorders. Leaving aside the long-covid cognitive (e.g., difficulty in concentration) and bodily (e.g., loss of smell) effects, the short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the "online” handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients. To this purpose, patients with clinical depression (n = 14), individuals with high sub-clinical (diagnosed by a test rather than a doctor) depressive traits (n = 15) and healthy individuals (n = 20) were recruited and asked to perform four online drawing/handwriting tasks using a digitizing tablet and a special writing device. From the raw collected online data, seventeen drawing/writing features (categorized into five categories) were extracted, and compared among the three groups of the involved participants, through ANOVA repeated measures analyses. The main results of this study show that Time features are more effective in discriminating between healthy and participants with sub-clinical depressive characteristics. On the other hand, Ductus and Pressure features are more effective in discriminating between clinical depressed and healthy participants. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements.
ABSTRACT
The pandemic triggered by the SARS-CoV-2 virus has produced worldwide interruptions of face-to-face teaching activity in both schools and universities. In Italy, the quarantine began in the second half of February 2020 and lasted for all the second semester of lectures. The University of Bologna, where all the authors of the present article are based, developed and activated several interfaces necessary to efficiently deliver online teaching courses with the utmost speed. The framework used by the authors is based on a common platform, Microsoft TEAMS, available to all teachers at Bologna University. © 2021 American Association of Physics Teachers.