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A safety impedance micro-sensor based on flexible electrodes for continuous monitoring of the surface cleanness of pipes of small diameter (down to few mm) handling drinkable fluids is presented. It allows discrimination between biofilm and limescaling, grants high resolution (few µm in thickness) and enables early warning and predictive (closed-loop) maintenance. It is coupled with a temperature and fluid conductivity probes in order to calibrate for their fluctuations. The current prototypal electronics, measuring impedance in the MHz range, can be scaled towards low-cost and low-power pervasive implementations. Preliminary experimental results showing the consistence with non-curved geometries are presented.

Among variegated efforts supporting the application of Internet-of-Things technologies to the management of Smart Cities and to environmental monitoring, we present a novel wireless sensors network for drinking water. While the majority of IoT systems for water handling are developed for remote metering and leakage detection, here we focus on safety and quality. In particular, the compact electronic board performs analog conditioning, acquisition and Could-based processing of pH, temperature, conductivity, thickness of micrometric deposit fouling the pipes, flow rate and pressure in real-time with state-of-the-art resolution. The electronic system also manages battery recharge, harvesting energy (15 rnA) from the velocity of water, data logging and transmission through the LTE network. Experimental results of continuous real-time operation of three nodes for several days are reported.

This work presents the first prototype of the Smart Pipe, a compact and intelligent wireless node hosting miniaturized sensors, embedded in a flange, for distributed and real-time monitoring of water contamination and for optimization of water treatment plants. Beyond pH and temperature, it features a low-cost and high-resolution (13 ppm, 11 ms response time) custom-designed 4-channel conductivity measurement circuit. Long-term operation and optimized on-demand maintenance are achieved by the innovative combination of an impedance sensor measuring the thickness of sludge and biofilm depositing on microelectrodes with self-cleaning capabilities offered by UV photo-regeneration of nanometric TiO2 electrodes coatings. Finally, here we also demonstrate self-powering of the unit by means of an energy harvesting control valve, regulating the flow in the water distribution network where an efficient installation of these sensors is today foreseeable.

The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and dimensions to be independently distributed, making it a less-than-ideal algorithm for the analysis of time series data with temporal correlations. Here, we seek to derive an NMF that accounts for temporal dependencies in the data by explicitly incorporating a very simple temporal constraint for the coefficients into the NMF update rules. We applied the modified algorithm to 2 multi-dimensional electromyographic data sets collected from the human upper-limb to identify muscle synergies. We found that because it reduced the number of free parameters in the model, our modified NMF made it possible to use the Akaike Information Criterion to objectively identify a model order (i.e., the number of muscle synergies composing the data) that is more functionally interpretable, and closer to the numbers previously determined using ad hoc measures.

Here we investigate whether torque or myoelectric control was more efficient during active, robotic-assisted therapy. Eleven hemiparetic stroke patients employed the RehaArm-robot, which consist of an exoskeleton that supports the entire arm and measures in real time the joint position and moment of the shoulder, in four sessions of one hour on consecutive days. At each session, the patients repeatedly performed basic movements of the shoulder in passive and active mode. During the passive mode, subjects learnt the movements. During the active mode, subjects were asked to complete 40 task repetitions in 20 min for each modality, torque and myoelectric control. The number of repetitions achieved, completion rate (CR), was tracked for each control modality as well as subjects' opinion about the ease of use of each modality. The results showed that the severe-to-moderate group (Fugl-Meyer Motor Assessment of the Upper Extremity 40), the CR was very similar for both control modalities. Subjectively, the two groups considered both control modalities similarly easy to use, being the myoelectric control slightly easier (higher median and middle fifty values). These results support the higher efficacy of the myoelectric control for active, robotic-assisted therapy.

A five-bar linkage mechanism with two active and three passive degrees-of-freedom (rotations) at the end-effector was used as a haptic device and integrated inside a Matlab/Simulink general framework, in which Handshake proSENSE toolbox was chosen for the haptic and graphic rendering, conceiving a virtual reality based rehabilitation system. Such device can generate forces at the fingertip up to 20 [N], which is suitable for finger and hand rehabilitation. Virtual reality based rehabilitation was conducted in two patients: one with a stroke, and the other with musculoskeletal disease. Two different kind of exercises were proposed. The system was tested successfully. A finger and hand rehabilitation case study and their results are presented. These results show the effectiveness of the proposed application.

Previous studies have shown that the motor training in a virtual-environment with the augmented feedback promotes motor learning in normal subjects and in long-term post-stroke patients. We evaluated whether this approach could be useful also for treating patients with arm motor deficits due to a recent stroke. Thirty-eight patients were included in the study within 3 months from an ischemic stroke in the territory of the middle cerebral artery. Twenty-five subjects received training with the Reinforced Feedback in Virtual Environment (RFVE) therapy for the arm, and thirteen patients received an equal amount of a conventional rehabilitation (CR) therapy focused to the upper limb. Before and after therapy, the autonomy of daily living activities were assessed with the Functional Independence Measure (FIM) and the degree of motor impairment was measured with the Fugl-Meyer scale for the upper extremity (FM-UE). The RFVE therapy group showed significant improvements in the FM-UE and the FIM scale mean scores. The conventional therapy determined smaller and not statistically significant scores improvements. These data indicate that the recovery of arm motor function in patients after a recent stroke appear to be speeded up by an augmented feedback provided in a virtual-environment.

Enhanced feedback provided by a virtual reality system has been shown to promote motor learning in normal subjects. We evaluated whether this approach could be useful for treating patients with motor deficits following brain lesions. Thirty subjects with mild to intermediate arm impairments due to stroke were selected for the study. They were randomly divided into two groups: the patients assigned to the study group received an hour of reinforced feedback in virtual-environment (RFVE) therapy daily for one month. The patients assigned to the control group received the same amount of treatment in time, consisting of traditional physical therapy (Conventional Therapy, CT). In both groups, before and after the therapy, we assessed the degree of motor impairment and autonomy in daily living activities using the Fugl-Meyer scale for the upper extremities and Functional Independence Measure, respectively. Both therapies yielded significant improvements over baseline values in the mean scores on the Fugl-Meyer Upper Extremity and Functional Independence Measure scales. These data indicate that motor recovery in post-stroke patients may be promoted by the enhanced feedback provided in a virtual environment.