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Exploiting Context-Aware Capabilities over the Internet of Things for Industry 4.0 Applications Igor Bisio, Chiara Garibotto, Aldo Grattarola, Fabio Lavagetto, and Andrea Sciarrone

Abstract This article surveys the concept of Industry 4.0 (I4.0), which has become more and more pervasive in recent years thanks to the great effort that factories, researchers, and organizations are putting into its definition and development. We present the IoT as the key I4.0 technology since it enables faster and more efficient production and management processes, leveraging the flexibility of smart, ubiquitous, connected devices. In particular, we discuss the role of the I4.0 revolution in driving the diffusion of smart products and services, by focusing on ambient intelligence and context awareness in IoT and on the so-called DIKW hierarchy. Finally, to demonstrate the practical impact of this emerging framework, we show, as practical examples, three typical I4.0 applications in the smart factory, smart home, and smart health scenarios.

The Industry 4.0 Revolution

The rise of new digital technologies, known as Industry 4.0 (I4.0), is a transformation that allows gathering and analyzing data across machines, enabling faster, more flexible, and more efficient processes to produce higher-quality goods and services at reduced costs. Actually, I4.0 represents the fourth industrial revolution, and its genesis is due to the emergence of the Internet. Over the course of history, industry and, in general, society have benefited from technological advancements with overwhelming impact, which were focused in a certain time period, and therefore called industrial revolutions [1]. During the first industrial revolution (1765), we witnessed the emergence of mechanization, a process that replaced agriculture with industry as the foundation of the economic structure of society. With the second industrial revolution (1870), industry began to develop and grow alongside the exponential demands for steel. Methods of communication were also revolutionized with the invention of the telegraph and telephone, and so were transportation methods with the emergence of the automobile and the plane at the beginning of the 20th century. Since 1969, the third industrial revolution has witnessed the thriving of electronics, with transistors and microprocessors, but also the rise of telecommunications and computers, consequently leading to programmable logic controllers and robots. Digital Object Identifier: 10.1109/MNET.2018.1700355

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With the fourth industrial revolution, Internet technologies aim to connect all production means to enable their interaction in real time, thus providing communication among the different players and connected objects thanks to technologies such as cloud, big data analytics, and, in particular, the Internet of Things (IoT) [2, 3]. The I4.0 framework has a huge impact not only on manufacturing per se, with the emerging concept of smart factory, but also on many other aspects of daily life. Indeed, the I4.0 environment opened doors to a new commercial scenario, introducing so-called smart products [4]. This new framework is based on key concepts, such as cyber-physical systems and human-computer interaction, which apply not only to manufacturing itself, but also enable applications that have a great impact on people and their lives. There are many macro areas effectively influenced by the I4.0 framework: • Factories • Power • Healthcare • Transportation • Buildings Remarkable examples of innovative applications receiving a boost from I4.0 are the fully connected and highly customizable smart home and the continuous monitoring e-health platforms. The I4.0 revolution, enabled by the device-to-device (D2D) and IoT paradigms, provides huge advantages not only for the production cycle itself, but also for society, which can benefit from the wide connectivity and ubiquity of services. The main characteristics of the I4.0 revolution are mobility, typical of IoT, employment of cloud computing approaches, collaboration among different systems and different actors in a given framework of production, and, at the same time, the exploitation of big data [5]. In fact, in the past few years the number of connected devices has grown such that nowadays there are more IP devices than people in the world. Indeed, the possibility to have Internet connections on the move (i.e., mobility) has significantly changed our daily lives, opening the door to new services and possibilities. Also, cloud computing is widely employed (i.e., 67 percent of Internet users in the United States rely on cloud services), and for many functions, this emerging technology will even replace PCs. On the other hand, collaboration is a partial consequence of the aforementioned characteris-

The authors are with the University of Genoa.

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tics, which has enabled a new type of cooperation between the real and virtual worlds, allowing cyber-physical interactions. Finally, with big data, mobile devices equipped with heterogeneous sensors produce enormous quantities of data, up to 3 EB every day. These data are often really different from each other, and their analysis and extraction of information represents fundamental added value. However, this emerging framework, driven by the I4.0 revolution, brings not only advantages, but also great challenges, due to the huge amount of devices and data to manage. For this reason, specific solutions must be designed in order to cope with the typical issues related to the IoT, such as energy and storage constraints, and challenging scenarios. This article is aimed at emphasizing the role of ambient intelligence in IoT, focusing on context awareness in smart products and services typical of I4.0. Three real cases have been considered: smart factory, for automatic asset detection; smart home, for the personalized control of domestic automation; and smart health, for reliable diagnosis and remote care of patients. In the following section definitions and the main features of IoT are introduced, as well as its related ambient intelligence functions, including context awareness. Specific practical cases of context awareness approaches in several typical I4.0 scenarios are then illustrated. Finally, conclusions are drawn.

IoT, Ambient Intelligence, and Context Awareness

IoT is the main enabling technology of the fourth industrial revolution. Among many good definitions, the following expresses the concept very well: the Internet of Things (IoT) is the network of physical objects, or “things,” embedded with electronics, software, sensors and network connectivity, which enables these objects to collect and exchange data [6]. IoT allows objects to be sensed and controlled remotely across the existing network infrastructure, creating the opportunity for more direct integration between the physical world and computer-based systems, and resulting in improved efficiency, accuracy, and economic benefit. IoT is not the result of a single novel technology; instead, several complementary technical developments provide capabilities that, taken together, help to bridge the gap between the virtual and physical worlds. These capabilities include communication and cooperation, addressability, identification, sensing, actuation, embedded information processing, localization, and user interfaces. Thanks to these characteristics, IoT provides the ability to make objects recognizable and smart by enabling context-related decisions. The appearance of cheap ubiquitous sensors, which can be easily integrated virtually anywhere, has accelerated the growth of ambient intelligence. This concept refers to the capacity of a system to sense the environment and to react to certain events or conditions, according to the typical situation awareness principles and behaviors. The concept of ambient intelligence is of huge importance in the I4.0 framework, as manufacturing

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Wisdom Understanding

Why?

Knowledge Information

How? Who?

What?

Where?

When?

Data

Signals

FIGURE 1. The extended DIKW hierarchy [7]. relies more and more on data collected from sensors, and the effectiveness of the production cycle strictly depends on the capability of smart systems to react and interact, based on contextual information and feedback coming from people or the environment itself. Therefore, this aspect has a dual effect on the I4.0 revolution: on one hand, IoT as a technology pushes toward the development of I4.0, and on the other hand, I4.0 itself, by generating smart products, gives boost to the growth of IoT. Either way, the Internet of Things, and the related ambient intelligence applications are the key factors driving the I4.0 revolution. As a result, IoT has gained a lot of popularity, propelled by the new advancements in mobile information systems. In general, the concept of ambient intelligence is strictly related to the definition of the data-information-knowledge-wisdom (DIKW) hierarchy [7], which has evolved in many different variants, each characterizing a different aspect of information. This concept is usually represented by a pyramid structure (Fig. 1), illustrating the direction of the information flow. The technological evolution and increasing diffusion of tiny smart objects embedded in everyday things, equipped with enough computational capabilities, and easily interconnected with each other and to the Internet, provide the opportunity to design more advanced and innovative cross-domain applications. These services are able to exploit multiple sensors, actuators, and user-generated data by managing them and interoperating among different contexts. A large number of these applications are based on context awareness, and are highly customizable and tailored to the user’s preferences and needs, relying on real-time knowledge of the surroundings, without requiring complex configurations. Methods to extract context information by employing smart objects are aimed at answering the following questions about the users’ environment: What, Who, Where, When, Why, and How [8]. These answers represent a fundamental part of the overall process needed to provide a complete context-aware service, and they are key steps of the aforementioned DIKW hierarchy. Context refers to the information characterizing the situation of an entity or a group of entities,

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Smart health

Smart home

the so-called data tsunami with thousands of IoT nodes carrying a huge amount of information (big data). Finally, full connectivity and easy access of smart nodes creates multiple points of vulnerability, thus making security a very delicate aspect of IoT applied to I4.0 environments. In this work we try to address some of the aforementioned issues by surveying ambient intelligence solutions based on context awareness, specifically designed to cope with the typical challenges and constraints of IoT applications.

Smart Factory : Bluetooth Low Energy Based Asset Detection and Tracking

Smart factory

FIGURE 2. Example of emerging scenarios boosted by the Industry 4.0 framework. and it provides knowledge about their current status. The term context may assume different meanings based on the scenarios and users involved: activities, geo-spatial information, network states, battery levels, events on social networks, energy consumption, environmental parameters, signal-to-noise ratios. Context awareness also allows for customization and personalized contents to match the preferences of the involved entities. In this article we describe context-aware systems and applications specially designed and implemented as smart products typical of the IoT era. In the next section we show through practical examples the pervasiveness and ubiquity of smart solutions based on context awareness, aimed at enhancing ambient intelligence applications driven by the I4.0 revolution.

Practical Cases

The framework of I4.0 propelled the definition of new applications and scenarios, such as smart home, smart factory, and smart health (Fig. 2). In these IoT environments, smart products cooperate to collect valuable information, and they therefore increase their potentialities by adapting their behavior based on knowledge about the environmental context and user preferences. This mechanism, based on ambient intelligence, represents one of the biggest revolutions in the near future in society, influencing more and more aspects of the life of people involved in the I4.0 revolution. However, such a revolutionizing scenario brings not only great opportunities, but also specific challenges. In this article we describe a framework that allows envisaging critical issues that need to be addressed thoroughly in the near future. A first challenge concerns environmental sensing. IoT applied to I4.0 scenarios is often characterized by complex and challenging environments, which do not allow gathering clear and precise data from the IoT sensor networks. A second issue concerns the energy consumption, because the majority of the I4.0 applications are based on mobile/embedded IoT systems, characterized by resource and power constraints. Scalability represents another significant issue: the heterogeneity of I4.0 scenarios generates

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One of the main scenarios introduced by the I4.0 revolution is related to the so-called smart factory. This environment is represented by efficient logistics and connected, monitored, and aware factories/construction sites. The efficiency of manufacturing and construction operations can be seriously affected by the amount of time spent searching for misplaced or lost tools and assets. This problem is particularly serious in wide construction sites (e.g., railway or highway construction) where lost assets must be searched for in very large areas. Asset tracking systems represent a promising approach for reducing wasted time and costs. The IoT framework offers new and smarter solutions to this important issue, thanks to the great availability of mobile and portable devices always connected to the Internet. In this framework we analyze a context-aware asset tracking solution based on Bluetooth Low Energy (BLE) technology, which leverages the presence of multiple connected devices to sense the environment. Their task is to scan the nearby area and search for BLE tags embedded in the factory assets in order to locate them and advertise their position inside the working site. The general design, originally reported in [9], consists of tagging every asset with BLE tags, which can be detected by the smartphones available within the construction site, as depicted in Fig. 3. Each smartphone has an active GPS interface, so its absolute position is known. Every time a BLE tag is detected, an application running on the smartphone senses the received signal strength (RSS) of the nearby BLE so as to estimate the distance between the tag and the smartphone itself. Finally, the smartphone sends all these data together with a timestamp and the detected BLE medium access control (MAC) address to a cloud database. Information related to the detected BLEs can be exploited by any other smartphone that needs to localize a specific asset, thus allowing time savings. This solution also tries to tackle the aforementioned issue of energy constraint typical of mobile IoT scenarios by limiting power consumption through intelligent use of resources. In more detail, each smartphone executes a Bluetooth scan every S s and then switches off its Bluetooth and GPS interfaces for another N s in order to save energy and to guarantee the duration of the smartphone battery for at least an entire working shift (i.e., 8 h). The sum of the quantities S + N forms the scan period (SP). Two important metrics that can be used to understand the usefulness of such system are the aging time, and the full detection probability

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(FDP). The former is the time between two successive detections of the same BLE tag, while the latter is the probability to detect, at least once, all the BLE tags in the reference construction area. Table 1 reports some simulated numerical data related to the aging and the FDP for different values of the SP and BLE number. The reference construction site is 250  250 m, and the smartphone moves at 4 m/s. When the SP increases, the aging time increases and the FDP decreases. Keeping the SP fixed, an increasing number of BLE tags provides a decrease of the aging time. This is due to the fact that if many tags are present within the construction site, the detection of an asset (with a BLE tag on) becomes easier. However, increasing the BLE tag number is a detriment to the FDP: when many tags are present, detection becomes more difficult.

T T

T

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MD

Warehouses

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Smart Home: Audio Speech Processing for Domotics Applications

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FIGURE 3. Overview scheme of the BLE-based asset tracking solution [9].

The importance of audio speech processing in the framework of IoT and I4.0 is demonstrated by the use of this type of processing in a wide variety of commercial smart products. The diffusion of related enabling technologies, such as D2D communications, cloud computing, and big data analysis, has strongly improved the feasibility of connecting and communicating through a large number of mobile nodes, often in non-ideal environmental conditions [10, 11]. Moreover, this growing interest in speech and speaker recognition is witnessed by the widespread use of applications for security purposes, such as speaker verification and authentication procedures, which might also be extremely useful in the framework of smart manufacturing. Think, for example, of access to restricted areas within production sites, or handling of specific tools and workstations by employees. Other common applications of speech processing techniques lie in the range of accessibility solutions: the most remarkable examples of this kind are the speech-to-text and textto-speech functionalities. The main scenario in which this kind of functionality is exploited is related to the concept of smart environment, be it a home, a production site, an office, or even a city. A commercial embodiment of these concepts is domotic and connected cars, which are rapidly gaining popularity on the market, driven by the

fact that an increasing number of consumers are willing to access this type of technology, looking for automation, connection, and customization of services. For this reason, understanding audio context represents an important tool that can be extremely useful in several realistic IoT scenarios. The problem of speaker recognition is to identify a speaker by analyzing the speech signal extracted from an audio sample produced by an unknown speaker. It can be tackled by two different perspectives: closed-set, when the speaker belongs to an a priori known group of people, and open-set if the identity of the test subject could also be outside the predefined speaker set. As mentioned before, the I4.0 framework is not always able to offer favorable sensing conditions, while it often requires ambient intelligence solutions specifically designed to cope with challenging environmental conditions. For this reason, an exhaustive study of the performances of the most common speech processing techniques in variable noise conditions and at different source-receiver distances is fundamental. The design of the proposed system is such that the acquired audio signal is divided into short segments called frames, during which speech can be considered as stationary. In order to enhance BLE number

5

10

15

20

SP (s)

Aging (s)

FDP (%)

Aging (s)

FDP (%)

Aging (s)

FDP (%)

Aging (s)

FDP (%)

20

344

100

365

96

372

96

388

93

25

542

100

574

93

605

95

622

89

30

586

98

591

93

614

91

627

86

35

840

95

833

93

847

91

853

83

40

1266

94

1237

92

1435

91

1549

81

45

1760

94

1516

90

1589

92

1672

77

50

2345

93

2249

91

2453

92

2484

68

TABLE 1. Aging and full detection probability for different values of scan period and number of employed BLE units.

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Closed-set Clean, VAD/no VAD PINK, VAD/no VAD AWG, VAD/no VAD SS, VAD/no VAD

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FIGURE 4. Performance of speaker recognition in a) closed-set; b) open-set for a challenging environment with and without VAD. the speaker recognition performance in challenging conditions, a suitable pre-processing method involving voice activity detection (VAD) can be used. Next, speech features are extracted, and they are used to train an SVM classifier. For a detailed description and definition of the system design, we refer readers to a previous article by the same authors [12, references therein]. As for the simulations, we take into account 5 different source-receiver distances in the range d ∈ {1 ÷ 5} m, spaced by 1 m steps. We performed the tests in clean speech conditions and when the signal is corrupted by environmental noise of different types. In particular, we tested the proposed algorithm in the presence of additive white Gaussian noise (awg), flicker noise (pink), and speech-shaped noise (ss), which are typical environmental noise types that can corrupt a speech signal. We chose as performance parameter the classification accuracy, defined as the percentage of correctly classified audio files over the total number of files tested in the considered scenario. Figure 4a shows a comparison of the effects of noise on a speaker recognition test both with and without VAD in closed-set. When we consider clean speech, the recognition performance at 1 m distance reaches around 95 percent. The influence of noise appears particularly critical as the source-receiver distance grows above 3 m. In these conditions the recognition accuracy is strongly affected by noise, especially by the white Gaussian and speech shaped types. However, the results prove that globally, the use of our proposed VAD approach is able to effectively reduce the detrimental effects of noise on the voice signal. We conducted the same analysis in an openset scenario, as shown in Fig. 4b. The results exhibit similar behavior with respect to the recognition accuracy, even if the global performance is lower due to the fact that recognition is harder since we are considering an open-set scenario. We can see that, despite the effects of a challenging environment on the signal, such a method allows us to enable personalized responses of a smart-space system based on the identity of the user.

Smart Health : Wearable Prototype for Post-Stroke Rehabilitation

The framework of I4.0 with the employment of

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IoT is providing a great boost to the healthcare system, through the development of remote technologies and the diffusion of smart and connected e-health solutions. Great emphasis is given to the study of advanced and practical information and communications technology (ICT)-based health-related technologies and services directly applied in the home environment [13]. Examples of possible smart health applications include remote monitoring platforms relying on cloud databases, wearable sensors for patient rehabilitation, and smart IoT devices for the early detection of specific injuries or diseases. In this framework we focus on an innovative prototype system, called Smart Pants, tailored for in-home post-stroke rehabilitation and coaching. The aim of the prototype is to exploit intelligent sensing to remotely guide and assist patients during their post-stroke rehabilitation therapy. It consists of multiple sensors (accelerometers, pressure and flexible sensors) embedded in easy-to-wear pants, whose task is to detect the movements of the lower limbs performed by the patient. The final aim of this prototype is two-fold: it allows the medical staff to remotely monitor a patient’s movements, and at the same time, it coaches the patient during his/her rehabilitative phase, providing feedback on how he/she is performing the exercises, thus activating a virtuous cycle that will speed up the patient’s recovery. Figure 5 shows the outcome of the rehabilitative sit-to-stand (S2S) exercise (Fig. 5c), usually employed with patients who have suffered from brain stroke [14], monitored with Smart Pants. The different plots show acceleration (first row), foot pressure (second row), and fibular-to-femur (F2F) angle (third row), respectively, for the left leg (Fig. 5a) and the right leg (Fig. 5b). The measurements start with the patient sitting down. In this phase, the acceleration plots show for the Z-axis (yellow line) a value close to 9.81 (the gravity acceleration), while for the Y-axis (red line) and the X-axis values close to 0. Foot pressure is also zero in this phase because the patient does not produce any force on his/her feet while he/she is sitting. The F2F angle is close to 90° since the patient’s legs are bent. The S2S movement begins approximately at 3 s (see the straight vertical line separating Sitting from Standing). While the patient is standing up the accelerations change in the same way, inde-

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FIGURE 5. Sensor readings showing the movement and position related to the patient’s: a) left; b) right; c) lower limbs during the sit-tostand exercise.

pendent of the considered leg: the values on the Y-axis become close to –9.81, while the values on the Z-axis become close to zero. It means that the patient is standing, moving the gravity vector from the Z-axis to the Y-axis. The values on the X-axis remain mostly stable. The negative peak at the very beginning of the movement means that the patient has experienced a small imbalance while he/she was trying to stand up. During the standing time, no significant differences can be noted. The second row shows the pressure produced by the patient’s feet, expressed as a percentage of the total body weight. Two important peaks at the beginning and at the end of the exercise can be noted. These peaks are motivated by the fact that the patient has suffered from a brain stroke in the right part of his/her brain, leading to a significant deficiency in his/her left side. Consequently, all the movements he/she performs strongly rely on the right side of the body. This fact becomes evident when the patient is trying to stand up or sit down: all the force is performed by his/her right leg, as shown by the two peaks around 3 and 47 s. Similarly, during the standing time most of the body weight is supported by the right foot. Finally, the F2F angle exhibits values close to 90° when the patient’s legs are bent and close to 0° when he/she is standing. Values above 90° are considered as spurious measurements due to clothes thickness, and they are removed by a successive filtering operation.

Conclusions

In this article we analyze the emerging framework of Industry 4.0 and its deep relationship with the Internet of Things concept. Furthermore, we focus on the ambient intelligence and context-aware capabilities that enable the diffusion of smart products and services typical of the I4.0 revolution. Finally, we survey some practical cases of this innovative framework by introducing examples of context-awareness-based applications,

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and analyze the related system performance. In particular, we describe an asset tracking solution for smart factories, a robust speaker recognition algorithm for smart homes, and a prototype rehabilitation platform for smart health applications, and discuss how they embody practical outcomes of the Industry 4.0 revolution for the society of today and tomorrow.

References

[1] D. Landes, The Unbound Prometheus: Technological Change and Industrial Development in Western Europe from 1750 to the Present, Cambridge Univ. Press, 2003; https://books. google.it/books?id=qqDWGXjhZIYC. [2] K. Xu, Y. Qu, and K. Yang, “A Tutorial on the Internet of Things: From a Heterogeneous Network Integration Perspective,” IEEE Network, vol. 30, no. 2, Mar./Apr. 2016, pp. 102–08. [3] S. Verma et al., “A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues,” IEEE Commun. Surveys & Tutorials, vol. 19, no. 3, 2017, pp. 1457–77. [4] R. Anderl, “Industrie 4.0 — Advanced Engineering of Smart Products and Smart Production,” Proc. 19th Int’l. Seminar on High Technology, Technological Innovations in Product Development, Piracicaba, Brazil, 2014. [5] X. Ma et al., “When Mobile Terminals Meet the Cloud: Computation Offloading as the Bridge,” IEEE Network, vol. 27, no. 5, Sept./Oct. 2013, pp. 28–33. [6] C. Dobre and F. Xhafa, Pervasive Computing: Next Generation Platforms for Intelligent Data Collection, Morgan Kaufmann, 2016. [7] R. L. Ackoff, “From Data to Wisdom,” J. Applied Systems Analysis, vol. 16, no. 1, 1989, pp. 3–9. [8] D. R. Morse, S. Armstrong, and A. K. Dey, “The What, Who, Where, When, Why and How of Context Awareness,” Proc. CHI ’00 Extended Abstracts on Human Factors in Computing Systems, ser. CHI EA ’00, ACM, 2000, p. 371; http://doi. acm.org/10.1145/633292.633518 [9] I. Bisio, A. Sciarrone, and S. Zappatore, “A New Asset Tracking Architecture Integrating RFID, Bluetooth Low Energy Tags and Ad Hoc Smartphone Applications,” Pervasive and Mobile Computing, vol. 31, no. Supplement C, 2016, pp. 79–93; http://www.sciencedirect.com/science/article/pii/ S1574119216000043. [10] J. Liu et al., “Device-to-Device Communication for Mobile Multimedia In Emerging 5G Networks,” ACM Trans. Multimedia Computing, Commun. and Applications, vol. 12, no. 5s, 2016; https://www.scopus.com/inward/record.uri?eid= 2-s2.0-84994495822&doi=10.1145%2f2983641&partner-

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ID=40&md5= d07a32d63938b2cd6a4ef976ff11a712. [11] J. Liu et al., “Device-to-Device Communication in LTE-Advanced Networks: A Survey,” IEEE Commun. Surveys & Tutorials, vol. 17, no. 4, 2015, pp. 1923–40. [12] I. Bisio et al., “Speaker Recognition Exploiting D2D Communications Paradigm: Performance Evaluation of Multiple Observations Approaches,” Mobile Networks and Applications, 2017, pp. 1–13; http://dx.doi.org/10. 1007/s11036017-0876-z [13] C. E. Koop et al., “Future Delivery of Health Care: Cybercare,” IEEE Engineering in Medicine and Biology Mag., vol. 27, no. 6, Nov. 2008, pp. 29–38. [14] S. Hesse et al., “Sit-to-Stand Manoeuvre in Hemiparetic Patients Before and After a 4-Week Rehabilitation Programme,” Scandinavian J. Rehabilitation Medicine, vol. 30, no. 2, June 1998, pp. 81–86; http://europepmc. org/ abstract/MED/9606769

Biographies

I gor B isio is an assistant professor and a member of the research staff of the Digital Signal Processing (DSP) and the Satellite Communications and Networking (SCNL) Laboratories in the DITEN Department of the University of Genoa. Recently he also joined the RUDN University of Russia as a senior researcher. His research concerns signal processing over the Internet of Things, context and location awareness, safety and e-health applications, and satellite communications. Chiara Garibotto is currently working as a Ph.D. student at the Digital Signal Processing Laboratory in the DITEN Department

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of the University of Genoa. Her main research activities concern signal processing applied to speaker recognition, context awareness, and multiple observations, especially in the framework of smart spaces, the Internet of Things, and intelligent transportation systems. Aldo Grattarola is currently an associate professor at the University of Genoa. He received his Master’s degree in 1981, his Ed.S. in computer science in 1982, and his Ph.D. in electronics and computer sciences in 1986. His main teaching and research activities concern communication systems, image processing and compression, computer vision, computer graphics, and signal processing. He is also an author of several publications in international journals, edited books, and conference proceedings. Fabio Lavagetto is a full professor in telecommunications at the University of Genoa. Since 2016 he has been a member of the Board of Directors of the University of Genoa. Since 1995, he has been the head of research of the DSP Laboratory in the DITEN Department of the University of Genoa, with responsibility for numerous national and international research projects and contracts. Andrea Sciarrone is currently a postdoctoral research fellow and member of the research staff of the Telecommunication Research Group and, in particular, of the DSP Laboratory in the DITEN Department of the University of Genoa. His research concerns signal processing over the Internet of Things, context and location awareness, and safety and e-health applications.

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