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Quantifying muscle force and fatigue is important in designing ergonomic work stations, in planning appropriate work-rest patterns, and in preventing/assessing the progress of disorders. In 14 subjects (seven males, seven females), muscle force and fatigue were estimated by subjective perception (based on Borg scale CR10) and objective indexes extracted from surface electromyogram (EMG). The experimental protocol consisted of an isometric task selective for the upper trapezius muscle at different force levels (10-80% of maximal voluntary contraction--MVC, in steps of 10%MVC) and one fatiguing contraction (constant force level at 50%MVC until exhaustion). Surface EMG signals were detected by a two-dimensional (2D) array of electrodes placed half way between C7 and the acromion. The following variables were calculated from EMG signals: muscle fibre conduction velocity (CV), root mean square value (RMS), mean frequency of the power spectrum (MNF), fractal dimension (FD), and entropy. All detected signals were also used to build topographical maps of RMS. Both subjective and objective indications of force and fatigue can provide information on exerted force and endurance time (ET). In particular, Borg ratings, RMS, and entropy were significantly related to force, and the rate of change of CV, MNF, FD, and Borg ratings were predictive of the endurance time. Moreover, significant differences were found in Borg ratings between males and females. The correlation coefficient of pairs of topographical maps of RMS was high (of the order of 0.8). This reflects a characteristic spatial-temporal recruitment of upper trapezius motor units that is not affected by force levels or fatigue.

The reliable detection of water and ice over road surfaces is an important issue in improving traffic safety and reducing costs for the maintenance of routs, especially during winter. A low cost capacitive sensor for the estimation of road conditions is studied. A simulation model was developed to investigate the capacitance of the sensor when air, water, or ice are covering its surface and to assess the effect of the variation of environmental temperature, or of the thickness of water or ice. An algorithm for the estimation of the state of the sensor (dry, wet, or icy) was developed based on the results of the simulations, which indicated that the time derivative of the estimated capacitance provided optimal information. Accuracy and reliability of the estimates provided by the sensor were assessed in laboratory experiments, placing more sensors in a climatic chamber and investigating the estimated state of the sensors and the timing of the identification of wet-icy or icy-wet transitions. Reliable estimates were obtained by all the sensors, with a dispersion of the transition times of the order of a few minutes. The sensor was also investigated in field. Two sensors (one of which was bituminized) were embedded in a road pavement to monitor continuously the road surface condition for a month. Both sensors provided indications in line with the environmental conditions, identifying properly the icy condition and indicating the wet state of the road both during rain and fog. Thus, the sensor is suggested as a feasible tool for monitoring road conditions to support information systems improving security and efficient maintenance of roads during winter.

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A new method for the non invasive estimation of motor unit distribution within the muscle is proposed. It is based on the surface EMG signal detected at different contraction levels in single differential configuration from an array of electrodes placed over the considered muscle in the direction orthogonal to the fibres.

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Current research and improvements in the field of wireless sensor networks are focused on decreasing the power consumption and miniaturization, improved smartness and better wearability of the sensor, and especially with their capability for environmental sensing. Today, the survival of these kinds of networks is a critical issue especially in order to keep environmental information updated. This paper presents, an improvement of the environmental sensing acquisition system shown in [1], by applying more sensors to gather data. It was found a novel method of reading sensor data using smartphones and also the structure of sensors themselves helps to decrease the power consumption of the network.

The monitoring of runway surfaces, for the detection of ice formation or presence of water, is an important issue for reducing maintenance costs and improving traffic safety. An innovative sensor was developed to detect the presence of ice or water on its surface, and its repeatability, stability and reliability were assessed in different simulations and experiments, performed both in laboratory and in the field. Three sensors were embedded in the runway of the Turin-Caselle airport, in the north-west of Italy, to check the state of its surface. Each sensor was connected to a GPRS modem to send the collected data to a common database. The entire system was installed about three years ago, and up to now it shows correct work and automatic reactivation after malfunctions without any external help. The state of the runway surface is virtual represented in an internet website, using the Internet of Things features and opening new scenarios.