Ricerc@Sapienza

Biometrics and cloud computing are converging towards a common application context aiming at deploying biometric authentication as a remote service (Biometrics as a Service - BaaS). The advantages
for the final user is to be relieved from the burden related to acquire/ maintain specific software, and to gain the ability of building personalized applications where biometric services can be embedded through suitable cloud APIs. Gait is one of the promising biometric traits that can be investigated in this scenario. In particular, this paper deals with

Context awareness is major component of Ambient Intelligence. In fact, Ambient Intelligent environments are designed to combine ubiquity, awareness, intelligence and natural interaction. Awareness is defined as the ability by the system to locate and recognize people and objects, and their intentions. Then, intelligence is the ability of the system to analyze

Impedance plethysmography is a technique which allows the monitoring of respiratory activity through the measurement of variations in the trans-thoracic impedance. Provided the measurement system has enough sensitivity, the same technique can also detect heartbeats, thus adding cardiac activity monitoring capabilities. In this paper, a comparison is performed between 2-electrode and 4-electrode systems, considering both volt-amperometric and AC-bridge solutions.

Nowadays, a growing number of researchers is using advanced wearable technologies with inertial measurement units (IMUs) to improve the evaluation of motor symptoms in patients with Parkinson’s Disease (PD). In this contest, wearable sensors are promising technologies possibly helpful for the overall clinical management of PD.

Freezing of Gait (FOG) is an unpredictable gait disorder typical of Parkinson's Disease (PD). The main goals of this work are detecting FOG episodes, classifying FOG subtypes and analyzing the leg muscles activity toward a deeper insight into the disorder pathophysiology and in the associated risk of fall. Fusion of inertial and electromyographic signals in our wearable system allows distinguishing correctly 98.4% of FOG episodes and monitoring in free-living conditions the activity type and intensity of leg antagonist muscles involved in FOG.