Capitolo contenuto all’interno dell’edizione italiana del manuale di Psicologia generale di R. S. Feldman che, grazie a un’impostazione didattica chiara e motivante, è adatto a tutti gli studenti che si accostano allo studio di questa disciplina nell’ambito di un corso di laurea triennale nelle facoltà di Psicologia e Scienze della formazione.

Bracco, F. (2008). Ergonomia. In R.S. Feldman (Ed.), Psicologia Generale, edizione italiana a cura di G. Amoretti, M.R. Ciceri. Milano: McGraw-Hill.

Improving resilience through practitioners’ well-being: an experience in Italian health-care

Abstract: Building and maintaining resilience in health care requires psychological and organizational attitudes that could be affected by the lack of worker well-being. Resilience requires the ability to give strong responses to weak signals, but, if well-being is threatened, workers are more committed to defend it, than detecting and monitoring weak signals in foresight. Malaise is a weak signal itself that, as well as leading to accidents due to fatigue, miscommunication, distraction, etc., blocks operators at a resource-saving cognitive level that prevents noticing and reporting further weak signals. We adopted the Skill-Rule-Knowledge model by Rasmussen as a framework to conceive resilience as continuous movement of workers along the three steps of the ladder. According to this model, we describe a research-intervention project carried out in a few Italian hospitals where trainees were enabled to develop a tool for detecting and monitoring malaise and threats to safety. Its potentials for reducing effects like distrust, resignation, cynicism, helplessness are discussed in light of a well-being-based resilience engineering.

Bracco, F., Bruno, A., & Sossai, D. (2011). Improving resilience through practitioners’ well-being: an experience in Italian health-care. In E., Hollnagel, E., Rigaud, & D., Besnard (eds.). Proceedings of the Fourth Symposium on Resilience Engineering, (pp. 43-49). Paris: Presses des Mines. PDF

Towards Real-Time Affect Detection Based on Sample Entropy Analysis of Expressive Gesture

Abstract: Aiming at providing a solid foundation to the creation of future affect detection applications in HCI, we propose to analyze human expressive gesture by computing movement Sample Entropy (SampEn). This method provides two main advantages: (i) it is adapted to the non-linearity and non-stationarity of human movement; (ii) it allows a fine-grain analysis of the information encoded in the movement features dynamics. A realtime application is presented, implementing the SampEn method. Preliminary results obtained by computing SampEn on two expressive features, smoothness and symmetry, are provided in a video available on the web.

Glowinski, D., & Mancini, M. (2011). Towards real-time affect detection based on sample entropy analysis of expressive gesture. In S., D’Mello et. all (Eds.)m Affective Computing and Intelligent Interaction (pp. 527-537). Berlin: Springer Heidelberg. PDF

Validation of an Algorithm for Segmentation of Full-Body Movement Sequences by Perception: A Pilot Experiment

AbstractThis paper presents a pilot experiment for the perceptual validation by human subjects of a motion segmentation algorithm, i.e., an algorithm for automatically segmenting a motion sequence (e.g., a dance fragment) into a collection of pause and motion phases. Perceptual validation of motion and gesture analysis algorithms is an important issue in the development of multimodal interactive systems where human full-body movement and expressive gesture are a major input channel. The discussed experiment is part of a broader research at DIST-InfoMus Lab aiming at investigating the non-verbal mechanisms of communication involving human movement and gesture as primary conveyors of expressive emotional content.

Glowinski, D., Camurri, A., Chiorri, C., Mazzarino, B., & Volpe, G. (2007). Validation of an algorithm for segmentation of full-body movement sequences by perception: A pilot experiment. In M., Sales Dias, M. M., Wanderley, & R., Bastos, (Eds.),  Gesture-based human-computer interaction and simulation (pp. 239-244). Berlin: Springer Heidelberg. PDF