Technologies

The platform allows acquisition of data from commercial and custom sensors. By now, the system has been embedded in a wearable wristband where elastomeric based strain gauge have been integrated to detect fine hand/wrist/arm movements. The platform integrates inertial sensors (accelerometers, gyroscopes) to acquire more details about the subject movements. A sensor-fusion algorithm enables advanced movement recognition (gesture, 3D orientation). A machine-learning algorithm is in development to increase the performance of the platform.

The proposed technology deals with the development of active SERS (Surface Enhanced Raman Scattering) substrates ad hoc designed for diagnostics of cultural heritage. The substrates are prepared starting from common commercial 'polishing film' sheets (lapping optical fibers) showing an intrinsic roughness (48- 1000 nm) that favors the SERS effect. A pattern of silver or gold nanoparticles are deposited on these films through Pulsed Laser Deposition (PLD).

The prototype uses soil moisture sensors which, through a measurement of dielectric permittivity, estimate the soil moisture based on which irrigation is started through relay-controlled solenoid valve. The system was developed using Open Source technologies. Specifically, for the hardware components, a small sized board computer Raspberry PI 3B + was used together with a 4G LTE Wi-Fi router and a Modbus rs485 / USB converter.

Network structures that require the use of a common database are affected by the risk of processing identification data that are necessary for sharing information and updating and processing data with equal access level between the network nodes. However, this sharing could lead risks of vulnerability when identification data are exchanged between the nodes of the network. The proposed information system involves the exchange of information by encrypting the identification data with an MD5 Hashing procedure (RFC1321).

SITODIET is an innovative software that supports a translational approach to health’s state. It integrates various sources of physiological, behavioral, and psychological data to reduce the risks associated with the onset of lifestyle-related diseases (primary prevention), to support health professionals in early diagnosis (secondary prevention) or to manage the personalized therapy’s patient (tertiary prevention). SITODIETcollects data automatically, through actigraphy tools, as wristband or smartwatch, or manually

Spark anemometry based on the analysis of an electrical discharge can be implemented in the automotive sector through measurements of the secondary circuit voltage. Actual applicability of this method is quite limited, given that it requires additional hardware that is not compatible with space requirements specific for production engines (e.g. fueled with gasoline, LPG or methane); furthermore, applying high voltage measurements is complex and entails increased cost.

This technology is based on an algorithm able to provide the probability of being asthmatic with high accuracy. This probability is based on the evaluation of respiratory function and, specifically, of forced expiratory vital capacity in the first second (FEV₁), in resting conditions, and 20 minutes after administration of a bronchodilator drug.

IMM has developed tactile sensors for the detection of objects and surface and for the handling of objects with humanoid robots (e-skin). These devices can be integrated on ultra-flexible and high conformable substrates and they can be used for multiple applications: 1) for a correct interaction with objects distributed in complex environment; 2) for a safe short-range interaction between humanoid robot and humans; 3) for fabricating smart wearables for the detection of biometric parameters (e.g. heartbeat); 4) for remotely control rovers with wearable gadgets.

This form describes a programmable, autonomous and stand-alone imaging system for the acquisition and processing of images containing subjects whose size is larger than 1cm (e.g. gelatinous zooplankton, fishes, litter, manufacts), form the seafloor or along the water column, in shallow or deep waters. It is capable to recognize and classify the image content through pattern recognition algorithms that combine computer vision and artificial intelligence methodologies.

Environmental monitoring is a rapidly growing field, both in academia and industry. The use of wearables for environmental monitoring is a promising technique, as it allows data to be collected continuously and comprehensively. The main problem with using wearables for environmental monitoring is the size and weight of the system, as well as the high degree of specialization required to develop a fully functional device.