Natural disasters occurring in inaccessible rural areas are on the rise, leading to the multiplication of first responders’ missions. However, engagement in fighting wildfires or participating in rescue missions includes risks for the well-being of the engaged first responders. Consequently, a system that monitors their actions and provides real-time and actionable information without obstructing their operational capacity is needed. The EU-funded SIXTHSENSE project aims to improve the efficiency and safety of first responders’ engagement in difficult environments by optimizing on-site team coordination and mission implementation. The project proposes an innovative wearable health monitoring system based on multimodal biosensor data that enables first responders to detect risk factors early on and allows real-time monitoring of all deployed responders. This paper is an introduction to the overall concept of the project, to the methodology and the system architecture, moreover details on Alpha version of SixthSense prototype are presented.

In the present study we propose a novel method to automatically assess the quality of ECG signals collected through a wearable device in typical mountain rescuers activities. ECGs signals have been obtained during sessions of programmed field tests at the Bormio Ski Resort (Valtellina, Lombardy, Italy) in the month of March. Here, following the defined protocol, a group of 15 mountain rescuers has carried out daily rescuers’ activities, while wearing wearable textile system by Smartex Srl. The test protocol was designed to simulate the real physiological demands of mountain rescuers during their emergency deployments. Among the activities performed rescuers had to walk up and down hill in snow-covered trails, carrying stretchers onto which simulated victims were located etc… To infer the quality of ECG signals recorded we developed an algorithm for the automatic evaluation of collected signal deterioration. This method is based on the analysis of regularity of ECGs’ P-QRS-T complexes pattern. To estimate the maintenance of typical ECGs pattern shape, Sample Entropy (SampEn) was computed in moving fixed-length windows sliding along the signal, obtained after applying wavelet transform of the row ECG. The SampEn indices series was then thresholded to spot ECG points where P-QRS-T complexes were more or less easy to identify, respect to points where signal quality was completely deteriorated. Moreover, we evaluated signal quality maintenance while performing low and high intensity activities.

Multi-Line Transmission (MLT) was recently demonstrated as a valuable tool to increase the frame rate of ultrasound images. In this approach, the multiple beams that are simultaneously transmitted may determine cross-talk artifacts that are typically reduced, although not eliminated, by the use of Tukey apodization on both transmission and reception apertures, which unfortunately worsens the image lateral resolution. In this paper we investigate the combination, and related performance, of Filtered-Delay Multiply And Sum (F-DMAS) beamforming with MLT for high frame-rate ultrasound imaging. F-DMAS is a non-linear beamformer based on the computation of the receive aperture spatial autocorrelation, which was recently proposed for use in ultrasound B-mode imaging by some of the authors. The main advantages of such beamformer are the improved contrast resolution, obtained by lowering the beam side lobes and narrowing the main lobe, and the increased noise rejection. This study shows that in MLT images, compared to standard Delay And Sum (DAS) beamforming including Tukey apodization, F-DMAS beamforming yields better suppression of cross-talk and improved lateral resolution. The method's effectiveness is demonstrated by simulations and phantom experiments. Preliminary in vivo cardiac images also show that the frame rate can be improved up to 8-fold by combining F-DMAS and MLT without affecting the image quality.

Most of ultrasound medical imaging systems currently on the market implement standard Delay and Sum (DAS) beamforming to form B-mode images. However, image resolution and contrast achievable with DAS are limited by the aperture size and by the operating frequency. For this reason, different beamformers have been presented in the literature that are mainly based on adaptive algorithms, which allow achieving higher performance at the cost of an increased computational complexity. In this paper, we propose the use of an alternative nonlinear beamforming algorithm for medical ultrasound imaging, which is called Delay Multiply and Sum (DMAS) and that was originally conceived for a RADAR microwave system for breast cancer detection. We modify the DMAS beamformer and test its performance on both simulated and experimentally collected linear-scan data, by comparing the Point Spread Functions, beampatterns, synthetic phantom and in vivo carotid artery images obtained with standard DAS and with the proposed algorithm. Results show that the DMAS beamformer outperforms DAS in both simulated and experimental trials and that the main improvement brought about by this new method is a significantly higher contrast resolution (i.e., narrower main lobe and lower side lobes), which turns out into an increased dynamic range and better quality of B-mode images.

Since the 1980s, cardiotocography (CTG) has been the most diffused technique to monitor fetal well-being during pregnancy. CTG consists of the simultaneous recording of fetal heart rate (FHR) signal and uterine contractions and its interpretation is usually performed through visual inspection by trained obstetric personnel. To reduce inter- and intraobserver variabilities and to improve the efficacy of prenatal diagnosis, new quantitative parameters, extracted from the CTG digitized signals, have been proposed as additional tools in the clinical diagnosis process. In this paper, a new parameter computed on FHR time series and based on the phase-rectified signal average curve (PRSA) is introduced. It is defined as acceleration phase-rectified slope (APRS) or deceleration phase-rectified slope (DPRS) depending on the slope sign of the PRSA curve. The new PRSA parameter was applied to FHR time series of 61 healthy and 61 intrauterine growth restricted (IUGR) fetuses during CTG nonstress tests. Performance of APRS and DPRS was compared with 1) the results provided by other parameters extracted from the PRSA curve itself but already existing in the literature, and 2) other clinical indices provided by computerized cardiotocographic systems. APRS and DPRS indices performed better than any other parameter in this study in the distinction between healthy and IUGR fetuses. Our results suggest this new index might reliably contribute to the quality of early fetal diagnosis.

In the last few years, much effort has been devoted to the development of wearable sensing systems able to monitor physiological, behavioral, and environmental parameters. Less has been done on the accurate testing and assessment of this instrumentation, especially when considering devices thought to be used in harsh environments by subjects or operators performing intense physical activities. This paper presents methodology and results of the evaluation of wearable physiological sensors under these conditions. The methodology has been applied to a specific textile-based prototype, aimed at the real-time monitoring of rescuers in emergency contexts, which has been developed within a European funded project called ProeTEX. Wearable sensor measurements have been compared with the ones of suitable gold standards through Bland-Altman statistical analysis; tests were realized in controlled environments simulating typical intervention conditions, with temperatures ranging from 20 °C to 45 °C and subjects performing mild to very intense activities. This evaluation methodology demonstrated to be effective for the definition of the limits of use of wearable sensors. Furthermore, the ProeTEX prototype demonstrated to be reliable, since it produced negligible errors when used for up to 1 h in normal environmental temperature (20 °C and 35 °C) and up to 30 min in harsher environment (45 °C).

The 12 papers in this special section are original and relevant contributions in the area of smart wearable devices (SWDs) applied to the health and civil protection domain.

Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.

Financed by the European Commission, a consortium of 23 European partners, consisting of universities, research institutions, industries, and organizations operating in the field of emergency management, is developing a new generation of "smart" garments for emergency-disaster personnel. Garments integrate newly developed wearable and textile solutions, such as commercial portable sensors and devices, in order to continuously monitor risks endangering rescuers' lives. The system enables detection of health-state parameters of the users (heart rate, breathing rate, body temperature, blood oxygen saturation, position, activity, and posture) and environmental variables (external temperature, presence of toxic gases, and heat flux passing through the garments), to process data and remotely transmit useful information to the operation manager. The European-integrated project, called ProeTEX (Protection e-Textiles: Micro-Nano-Structured fiber systems for Emergency-Disaster Wear) started on February, 2006 and will end on July, 2010. During this 4.5 years period, three subsequent generations of sensorized garments are being released. This paper proposes an overview of the project and gives a description of the second-generation prototypes, delivered at the end of 2008.

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

This paper describes the dynamic control of a 3 degree of freedom (DOF) ringer emulating a human finger for reaching a desired fingertip position in space. The control consists of a neural network (NN) which provides the necessary three torques to the phalanges granting a smooth "natural" speed profile of the fingertip motion. To obtain the results, we face these problems: 1) the elimination of the redundancy due to the third joint; 2) the mathematical description of a natural movement; 3) the calculation of the torques for executing movement; and 4) the optimization of the NN's structure. Assuming a "cognitive" approach well-established in the literature, the first and the second points are solved by adopting an extension of the minimum jerk theory. The classic Lagrange equations are applied to compute the three-motor torque. Finally, a multilayer perceptron (MLP) NN is trained to move the device in a natural manner. The generalization capabilities of the NN are checked on new never-seen movements, and different MLP architectures are compared on the basis of indexes representing the motor performance. The results suggest we should pursue this approach for multifinger hand in order to achieve a natural NN prosthetic/robotic dynamic control system.

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and low-price tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless very poor indications on fetal pathologies can be inferred from the actual CTG analysis methods, either they consist of the clinician eye inspection or of automatic algorithms. A relevant amount of this unsatisfactory performance resides on the weakness of methods used for classifying fetal conditions and generate a risk alarm during pregnancy. In the paper three neural classifiers are proposed to discriminate among fetal behavioral states and among normal and pathological fetal conditions, on the basis of CTG recordings. All classifiers are fed by indexes extracted from fetal heart rate signal. Results show very promising performance towards the prediction of fetal outcomes on the set of collected FHR signals.

This study investigates how the saccadic components (quick phases) of vestibular nystagmus are generated. We propose a neural network model for the vestibule saccadic pathway, which shows dynamic adaptation of the quick phase parameters in order to faithfully reproduce the vestibular nystagmus. A structure similar to that proposed in this paper is likely to exist in the neural pathway linking the vestibular nuclei to the PPRF (paramedian pontine reticular formation) through the nucleus prepositus hypoglossi.

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multilayer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data.>

The authors evaluate the performance and try to identify the strategies of human operators (HOs) teleoperating a robot along 2-D-trajectories in simulations of place-like tasks in an obstacle encumbered environment. The experiments utilize computer graphic simulations of a remote robot whose end-effector displacements are dynamically controlled in the X-Y plane by the HO's hand displacements. The performance is described in the various displayed conditions in terms of movement duration, spatial and temporal errors, and energy. The data show that performance depends on the nature of the cues and instructions provided to the operators. Comparative analysis of the various conditions suggests that visual feedback position control is used when continuous static or dynamic information about the trajectory is provided, while feedforward control, corrected by sampled visual information, is adopted when a self-generated movement can be planned and executed. The data also show that for a given set of execution cues, equivalent performance is achieved in both visual frames of reference.>

Some problems raised by bimanual coordination control of articulated systems are studied. In particular, the performance of human operators driving an anthropomorphic robot along 3-D trajectories using either a bimanual micromanipulator or a keypad is compared. The trajectories were materialized in space by a thin wire twisted to trace a cosine wave. The performance was quantified in terms of total time (that is, the time to drive the robot effector through the trajectory), cumulated error, standard deviation and maximum error along the trajectory.>