@article {paolanti2017modelling, title = {Modelling and Forecasting Customer Navigation in Intelligent Retail Environments}, journal = {Journal of Intelligent \& Robotic Systems}, year = {2017}, month = {10}, pages = {1{\textendash}16}, abstract = {

Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers{\textquoteright} shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76\% in all test cases.

}, issn = {0921-0296}, doi = {10.1007/s10846-017-0674-7}, url = {https://doi.org/10.1007/s10846-017-0674-7}, author = {Marina Paolanti and Daniele Liciotti and R. Pietrini and Adriano Mancini and Emanuele Frontoni} } @conference {Cenci:2017:MAP:3109761.3109773, title = {Movements Analysis of Preterm Infants by Using Depth Sensor}, booktitle = {Proceedings of the 1st International Conference on Internet of Things and Machine Learning}, series = {IML {\textquoteright}17}, year = {2017}, month = {10-2017}, pages = {12:1{\textendash}12:9}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Qualitative assessment of general movements in preterm infants is widely used in clinical practice. It can enable early detection of neurological dysfunctions and consequent neuromotor impairments in high risk infants. However, the outcome of these assessments is not standardized and it is influenced by examiner{\textquoteright}s subjective interpretation. For this reason, there is an increasing interest in the use of automated movement recognition technologies being applied in this field. In this work, we use a video-based system for preterm infant{\textquoteright}s movements assessment to provide a 3D\ motion analysis method able to extract some important indicators from the sequence of depth images collected by using an RGB-D sensor placed over the infant lying on the crib. The advantage of the proposed method is that it is objective, contactless, non-invasive, easy to install, affordable and suitable to be used in an indoor environment with poor lighting, as might be rooms in the Neonatal Intensive Care Unit, where these infants are taken into care. Experimental results show that the proposed method is able to derive from statistical analysis of depth data some key performance indicators, each of which describes different characteristics of the infant{\textquoteright}s spontaneous movements. Preliminary tests are conducted in the experimental phase on a preterm infant hospitalized in a women{\textquoteright}s and children{\textquoteright}s hospital. The project can be used to investigate the relationship between the characteristics of spontaneous movements and the presence of pathologies as cerebral palsy or other minor neurological dysfunctions.

}, keywords = {3D tracking, clustering, modelling, preterm infant{\textquoteright}s movement analysis}, isbn = {978-1-4503-5243-7}, doi = {10.1145/3109761.3109773}, url = {http://doi.acm.org/10.1145/3109761.3109773}, author = {Annalisa Cenci and Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Virgilio Paolo Carnielli} }