Motion-robust, continuous, and contactless respiration monitoring has the potential to enhance clinical assessments by enabling more frequent inclusion of respiratory rate in cardiopulmonary risk evaluation. Depth sensing can be used to extract the torso surface area trend over time, which is the result of the breathing process. Movement artifacts still represent a major problem. In this paper, four subjects are asked to walk and breathe freely in a volume measurement. A commercial wearable chest strap is employed as a ground truth. Each subject pose is tracked by deep learning. Four regions of interest are evaluated for respiratory extraction: the entire torso, chest, abdomen, and chest centroid. To compensate for the effect of motion, the nose, the middle of the shoulder, and the abdomen are considered as possible movement reference points and subtracted from the signal extracted from the previously mentioned region of interest. The best results given by the chest area correspond to a Mean Absolute Error (MAE) of 0.958 bpm, a standard deviation (STD) of 2.132 bpm, and a Root Mean Square Value (RMSE) of 2.338 bpm.

Respiratory rate is a relevant parameter to be monitored to prevent adverse cardiopulmonary situations. Radar sensing represents a promising alternative to wearable sensors, since it gives the opportunity to remotely monitor a subject, while maintaining privacy and fairness. In this work, radar is used to enable a single sensor to track and extract the respiratory rate of a freely moving subject. To validate performance, the subject first walks in a straight line and then moves randomly within a room. As a reference, they are instructed to control their breathing pace using a metronome. A state of art deep learning algorithm, previously developed for person detection purposes and called mmDetect, is applied to localize and track the subject. The developed processing pipeline allows the extraction of the beat signal phase shift associated with chest displacement at each location, which is then used to reconstruct a phase signal linked to the respiratory phenomenon. A resulting measurement error of 1 breath per minute (BPM) is demonstrated.

Automotive radars could have a pivotal role in human-machine interfaces thanks to their ability to monitor human activity and physiological state in a contactless way. Nowadays, data-driven models, such as deep learning approaches, are the needed cutting-edge technology to achieve appropriate results in the classification of activities. One of the issues in the literature is the identification of walking patterns from the radar acquisition and doing so in real-time would be beneficial to give instant feedback to the user. For this reason, we propose a model composed of a Convolutional Neural Network followed by a bidirectional Long-Short Term Memory for the classification of the range-Doppler-time data obtained from radar acquisition. The approach reaches at least a 90% f1 score for the abnormal walking patterns class observing just one gait cycle or one second of acquisition and it is a perfect anomaly detector with 2.6 seconds of acquisition time. Instead, to achieve good accuracy on the classification of all the classes we need a larger window of observation with an overall accuracy of 89.1% for 8 seconds and 95.6% for the complete acquisition, lasting 12 to 16 seconds.

The study of motor disorders due to neurodegenerative diseases (NDD) is assuming a central role in healthcare systems, this is certainly due to the needs of early recognition systems that can allow a better management of the patients daily-life. Many studies in the literature faced the problem of finding digital biomarkers from data collected through gait experiments to discriminate between control (CN) and NDD groups without systematically face the problem of which gait time-series were more appropriate to extract opportune descriptors for characterizing the NDD considered. In this work, such problem was modeled through a machine learning approach. Thus, 6 time-dependent spectral features (PSDTD) were extracted from 4 gait time-series, i.e., stride (SR), stance (SA), swing (SW) and double support (DS) duration intervals. A publicly available data set containing data of CN, Parkinson's (PD), Huntington's (HD) and amyotrophic lateral sclerosis (ALS) diseases was employed to the purpose. Low error rates using leave one out validation scheme were obtained using PSDTD features computed over DS and SA for CN-PD and CN-HD classification, i.e., error rate < 0.1 for DS and < 0.15 for SA. Regarding CN-ALS classification, best results were obtained using SA features, i.e. error rate <0.07. This supports the research line that dynamic equilibrium phases of the gait can hide important biomarkers for the characterization of different NDD.

With the progressive aging of the population, monitoring the state of frailty of a person becomes increasingly important to prevent risk factors, which can lead to loss of autonomy and to hospitalization. Hygiene care, in particular, represents a wake-up call to detect a decline in physical and mental well-being. With the assistance of both environmental and localized sensors, measurements of hygiene-related activities can be made quickly and consistently over time. We here propose to remotely monitor these activities using a fixed camera and deep learning algorithms. In particular, three activities are considered, i.e., washing face, brushing teeth and arranging hair, together with the non-action class. Considering a dataset consisting of 11 healthy subjects of different age and sex, we show that using a Long-Short Term Memory (LSTM) neural network the selected activities can be distinguished with an accuracy of more than 92%, thus proving the validity of the proposed approach.

The use of flying Unmanned Aerial Vehicles (UAVs) for communications is becoming more and more widespread, especially in 5G and beyond networks. In such a context, detection and authentication of UAVs is assuming an increasingly important role. In this paper we show that it is possible to distinguish different drones which communicate with a fixed ground base station (BS) on the basis of their channel characteristics and of the micro-Doppler signature associated to the specific features of each UAV. An urban scenario is simulated where UAVs fly at a constant height and channels are affected by Additive White Gaussian Noise (AWGN) and fading. With the aim of helping the BS in its authentication task, we take advantage of a sparse autoencoder trained on the channel of the legitimate transmitter, while data coming from possible attackers are classified as anomalies. We prove that, with proper network training, low levels of false alarm and missed detection can be achieved, especially if the attacker has no line-of-sight link, and that the presence of micro-Doppler actually contribute to enhance the authentication performance.

Sleep quality is an index of well-being, since sleep disorders, such as sleep apnea, may constitute a health risk. A constant monitoring of subjects, especially when there are heart or respiratory diseases, is essential. The present paper aims to offer a non-invasive and comfortable sleep monitoring, by employing a BallistoCardioGraphic (BCG) signal processing. In particular, with a BCG device located below the mattress, we are able to extract the heart rate, respiratory rate and, therefore, to exploit this information to develop an automatic sleep apnea recognition algorithm. The automatic approach presented has proven to achieve accuracy and reliability and could represent a valid resource to prevent serious damages during sleep.

In the recent years the number of vehicles on the road equipped with radar sensors is increased, especially thanks to their ability to support the main Advanced Driver Assisted Systems (ADAS), such as cruise control or assisted braking. This represents a great improvement for safety, but it has a main disadvantage: all these sensors in fact transmit at the same frequency and may suffer from mutual interference, leading to incorrect detection of the targets, which causes an incorrect operation for ADAS systems. However, the determination of the effects of this mutual interference requires the definition of a theoretical model of interference, the design of which, capable of adapting to real devices, is complex. In fact, depending on how the sensors are designed, current automotive radars generate signals in different ways and the interference patterns found in the literature do not always correspond to real-life scenarios. Therefore experimental validation represents the key to understanding and mitigating the interference. In this work, an experimental analysis of radar interference based on range-Doppler maps is carried out using two automotive radars, providing also a comparison with a theoretical model.

In this article we consider authentication at the physical layer, in which the authenticator aims at distinguishing a legitimate supplicant from an attacker on the basis of the characteristics of a set of parallel wireless channels, which are affected by time-varying fading. Moreover, the attacker's channel has a spatial correlation with the supplicant's one. In this setting, we assess and compare the performance achieved by different approaches under different channel conditions. We first consider the use of two different statistical decision methods, and we prove that using a large number of references (in the form of channel estimates) affected by different levels of time-varying fading is not beneficial from a security point of view. We then consider classification methods based on machine learning. In order to face the worst case scenario of an authenticator provided with no forged messages during training, we consider one-class classifiers. When instead the training set includes some forged messages, we resort to more conventional binary classifiers, considering the cases in which such messages are either labelled or not. For the latter case, we exploit clustering algorithms to label the training set. The performance of both nearest neighbor (NN) and support vector machine (SVM) classification techniques is evaluated. Through numerical examples, we show that under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a small spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is large.

The detection of vital parameters with traditional approaches, as the electrocardiograph, requires to appropriately place electrodes in direct contact with patients’ skin, often causing irritation. On the other hand, contactless measurement of physiological parameters provides an unobtrusive and comfortable instrument for subjects’ conditions monitoring, with application to home monitoring of aging people and in particular to those suffering of heart disease. In this paper two contactless techniques are proposed, based on radar technology and on video processing from an RGB camera. In order to validate their precision, the proposed methods are compared with three wearable low cost devices, taken as a reference for the outcomes. The developed approaches prove to achieve excellent performances, with an estimated mean relative error of 0.55% with respect to a commercial cardiac strap device.

Analysis of a person's movement provides important information about his or her health status. This analysis can be performed with wearable devices or with contactless technologies. These latter in particular are of some interest, since the subject is free to move and the analysis of the movement is realistic. Despite being designed for other purposes, automotive mmWaves radars represent a powerful low-cost technology for detecting people's movements without contact which finds interesting applications as a support for home monitoring of health conditions. In this paper it is shown how to exploit commercial radars to distinguish with high precision the way of walking of a subject and the position of his hands during the activity carried out. The application of Principal Component Analysis (PCA) for feature extraction from raw data is considered, together with supervised machine learning algorithms for the actual classification of the various activities carried out during the experiments.

In this paper we assess the security performance of key-less physical layer authentication schemes in the case of time-varying fading channels, considering both partial and no channel state information (CSI) on the receiver's side. We first present a generalization of a well-known protocol previously proposed for flat fading channels and we study different statistical decision methods and the corresponding optimal attack strategies in order to improve the authentication performance in the considered scenario. We then consider the application of machine learning techniques in the same setting, exploiting different one-class nearest neighbor (OCNN) classification algorithms. We observe that, under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a low spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is higher.

The paradigmatic shift brought by the Internet of Things has already revolutionized many key sectors, like environmental monitoring, grid and energy management, manufacturing, and it can be seen as a promising solution to address challenging societal issues, like the ability to provide significant enhancement to quality of life for the elderly and, in general, people in need. As a consequence, Internet of Things emerges also as the possible next evolution for the Ambient Assisted Living domain. One major issue to address in this context is the identification of a suitable middleware able to leverage the potentialities offered by the Internet of Things and, at the same time, ensure the necessary support to services and functions related to healthcare and personal assistance. This paper provides an overview of middleware solutions designed for Internet of Things in health and wellness domains, and presents a case study related to assistive technology for the home monitoring of people with dementia. It is illustrated how a specific middleware designed for telemetry applications, the MQTT, can be effectively applied in assistive scenarios too, with different architectural options and communication technologies.

Systems for well-being and active aging increasingly rely on wireless technologies, as Bluetooth Low Energy (BLE), featuring low-power consumption. The wearable system presented in this paper consists of sensorized shoes connected via BLE to a smartphone. The peculiar streaming scenario, characterized by low throughput, but strict reliability requirements, has been modeled, and an experimental evaluation of the system performed. This study shows that BLE fulfills the requirements, opening the way to further investigations on power management analysis, and the impact of increasing throughput on the reliability requirements.

The intent of the paper is to propose a new set of scrambling codes to spread information in a manner that provides superior physical layer security. In wireless communication systems, physical layer elements are vulnerable to possible security-related attacks by undesired entities. Basic premise of a reliable communication infrastructure is to mitigate risks related to violations of the user information integrity, authenticity, and to provide efficient control and management information flow. The 3GPP Long Term Evolution - Advanced achieves this goal by using robust scrambling technique for control channel information, by means of binary Gold sequences used as scrambling codes. Gold sequences provide good correlation-related properties, however they have been known for a long time, thus they are weak in security-related features, starting from their very limited cardinality. This paper suggests the use of De Bruijn sequences as the next generation set of scrambling codes, for their huge cardinality and satisfactory correlation-related properties. Different spans of De Bruijn codes are analyzed to examine their performance, with a specific emphasis on sequences of length 32, that are comparable to the Gold codes currently suggested by the standard. De Bruijn sequences are seen to provide much more favorable results, even with respect to security-related tests.

The most recent Wireless Sensor Networks technologies can provide viable solutions to perform automatic monitoring of the water grid, and smart metering of water consumptions. However, sensor nodes located along water pipes cannot access power grid facilities, to get the necessary energy imposed by their working conditions. In this sense, it is of basic importance to design the network architecture in such a way as to require the minimum possible power. This paper investigates the suitability of the Wireless Metering Bus protocol for possible adoption in future smart water grids, by evaluating its transmission performance, through simulations and experimental tests executed by means of prototype sensor nodes.

The need for a more efficient and reliable use of available radio resources has led to the developement of Spread Spectrum based communication networks, like WCDMA. Even though the Spread Spectrum signals are inherently robust, and provide significant level of security, it has been shown that the use of interference suppression techniques can result in a substantial performance improvement. This paper proposes an overview of interference suppression techniques used in combination with different sets of spreading codes. The focus of the paper is on Direct Sequence SS systems, and on the choice of proper spreading codes, in a blind asynchronous environment. Particular attention is devoted to analyzing the performance of spreading codes based on their cross correlation properties, in a multiple users scenario. Eigenvalue estimation and Maximum Likelihood estimation techniques are used to evaluate the robustness of the system, in the presence of Additive White Gaussian Noise.

This paper discusses the possible application of a particular family of binary sequences, the De Bruijn ones, as spreading codes in a vehicular scenario, by evaluating their performance according to different analytical tools. In an unfriendly environment, such as the vehicular one, that poses several constraints on transmission techniques for their reliability, it might be useful to have a wide set of sequences to allocate different users, in order to ensure low Inter User Interference. If needed, the same spreading sequences can also be used as radar signatures for anti-collision applications, in a highly mobile environment. The simulations show that the proposed spreading sequence set provides adequate performance, comparable to those of more traditional sets, with the advantage of greater cardinality.

Secure communications in the context of civil space missions gained a major attention in the last few years, mainly thanks to the activities promoted in this field by the Consultative Committee for Space Data Systems. Risk analyses performed by several space agencies have provided indications of the impact of different security threats on several categories of space missions. As a result, to ensure a minimum level of security, at least Telecommand authentication should be applied to all missions. Besides standard and well known algorithms, alternative authentication solutions are to be considered, and tested for possible adoption in the space context, in order to provide a scalable and flexible authentication framework. To this aim, this paper focuses on some Authenticated Encryption with Associated Data techniques, and on their thorough evaluation by a detailed model of the space Telecommand channel and protocol stack, in order to achieve an optimal selection for application in the real space communication environment.

The popularity of multimedia services over the Internet has grown heavily in recent years, and software phones and videophones have become one of the major IP telecommunication applications of interest for desktops or handheld PCs. In this paper we present a platform independent soft phone application, based on Java programming language, which is able to inter-operate in a pre-existent media gateway control protocol (MGCP) framework, in order to extend the already present LAN telephony capabilities, based on the adoption of hardware VoIP (voice over IP) telephones. The application presented in this paper has been entirely developed in Java: it can run on any platform having a JVM (Java virtual machine) installed. The prototype performance evaluation showed that JavaNetPhone interacts correctly with the VoIP gateway and the other phones on the LAN, giving acceptable voice quality.

A new encryption system is presented for compressed video signals. It employs the twofish algorithm, recently proposed for standardization purposes, as the core cipher but, through simple modifications, it permits to achieve the very high security levels promised by the standard with reduced overhead and processing time. So the new method is particularly adapted for bandwidth limited applications operating in real time. For the sake of clarity, the proposal is tested on H.263+ coded signals, but it can be easily adapted to other formats, of MPEG-x or H.26x type. Performance evaluation is done off-line, by using a simulator, but also implemented on-line in a practical environment. A comparison is made with the previous systems.

A turbo code has been included in the new Consultative Committee for Space Data Systems (CCSDS) channel coding standard for space telemetry. Many future missions with critical link budgets will benefit from its large coding gain. In this paper, the properties of this turbo code are analyzed for symbol synchronization recovery, where the transition density is essential. Key parameters like transition/symbol probability and run-length distribution are studied and compared against practical requirements. Critical working conditions are considered separately and discussed. It is shown that, thanks to the interleaver action, turbo-encoded sequences have very good properties in terms of randomness.