Data Analytics & Machine Learning
Proper data investigation is of key importance to achieve the goal of extracting knowledge from data itself. Systems that make the most out of this knowledge and stand on algorithms able to learn from their own experience map out the path of innovation. Data Analytics and Machine Learning, in the broader field of Artificial Intelligence, are strong enabler for cutting edge software solutions in a large spectrum of industry and research fields.
Ab.Acus offers wide experience in managing large amount of sensors and behavior data from different sources with the goal of extracting key features and implementing models to describe, classify, predict and take decisions. Adopted techniques are chosen to fit the nature of the data along with the final goal of the solution. They span from unsupervised approaches of clustering to supervised models of classification, targeting also the more specialized areas like Natural Language Processing and Computer Vision.
The main field of action for these solutions is the one of human health and behavior analysis where the data sources are smartphone data, wearable sensors records, texts, audio and video files. Long term acquaintance with biomedical and computer sciences makes Ab.Acus the ideal partner to bridge the gap between novel analysis techniques and clinical research needs.
TrHandy
TrHandy is a solution developed for a research project in the field of Reaching and Grasping study with the goal of studing the evolution of reach and grasp movements in children.
SPEAKapp
SPEAKapp is an internal research project with the aim of exploiting novel Artificial Intelligence branch of Natural Language Processing (NLP) in the field of clinical evaluation of neurodevelopmental disorders and cognitive impairment.
longITools
LongITools is a European research project studying the interactions between the environment, lifestyle and health in determining the risks of chronic cardiovascular and metabolic diseases.
mResist
The primary objective of the m-Resist project is to develop a therapeutic program that draws on the support of mobile devices to actively involve patients with treatment-resistant schizophrenia, to make them capable of self-managing their own illness, as well as to support their careers.