Increased migraine-free intervals with multifocal repetitive transcranial magnetic stimulation.
Introduction: Episodic migraine is a debilitating condition associated with vast impairments of health, daily living, and life quality. Several prophylactic treatments exist, having a moderate ratio of action related to side effects and therapy costs. Repetitive transcranial magnetic stimulation (rTMS) is an evidence based therapy in several neuropsychiatric conditions, showing robust efficacy in alleviating specific symptoms. However, its efficacy in migraine disorders is unequivocal and might be tightly linked to the applied rTMS protocol. We hypothesized that multifocal rTMS paradigm could improve clinical outcomes in patients with episodic migraine by reducing the number of migraine day…
Academia-industry Collaboration for Augmented Reality Application Development
Abstract Due to our previous experience in AR development, with this research project we propose to study how Augmented Reality (AR) can be adopted by an industrial partner and which are the major outcomes from which a company may benefit. For this purpose, we partnered with a forward-looking company willing to embrace the idea of implementing new technologies for industrial purposes. In this research we identified that the most significant impact which AR may have for our industrial partner is in providing remote assistance and for product exploitation and marketing purposes. For the latest we developed a customized AR application which is currently available on Apple’s App Store. By analy…
Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions
This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in so…
Engineering a Digital Twin for Manual Assembling
The paper synthesizes our preliminary work on developing a digital twin, with learning capabilities, for a system that includes cyber, physical, and social components. The system is an industrial workstation for manual assembly tasks that uses several machine learning models implemented as microservices in a hybrid architecture, a combination between the orchestrated and the event stream approaches. These models have either similar objectives but context-dependent performance, or matching functionalities when the results are fused to support real-life decisions. Some of the models are descriptive but easy to transform in inductive models with extra tuning effort, while others are purely ind…
Towards an Assembly Support System with Dynamic Bayesian Network
Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bay…