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A future with ubiquitous sensing and intelligent systems Fernando V. Paulovich, Maria Cristina Ferreira de Oliveira, and Osvaldo Novais Oliveira ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.8b00276 • Publication Date (Web): 13 Jul 2018 Downloaded from http://pubs.acs.org on July 17, 2018

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A future with ubiquitous sensing and intelligent systems

Fernando V. Paulovicha,b, Maria Cristina F. De Oliveirab, Osvaldo N. Oliveira Jr.c* a) Faculty of Computer Science, Dalhousie University, Goldberg Computer Science Building, 6050 University Avenue, B3H 4R2, Halifax, NS, Canada b) Institute of Mathematical Sciences and Computing, University of São Paulo, CP 668, 13560-970 São Carlos, SP, Brazil c) São Carlos Institute of Physics, University of São Paulo, CP 369, 13560-970 São Carlos, SP, Brazil. *Corresponding Author: [email protected] Abstract In this paper we discuss the relevance of sensing and biosensing for the ongoing revolution in science and technology as a product of the merging of machine learning and Big Data into affordable technologies and accessible everyday products. Possible scenarios for the next decades are described with examples of intelligent systems for various areas, most of which will rely on ubiquitous sensing. The technological and societal challenges for developing the full potential of such intelligent systems are also addressed.

Keywords: ubiquitous sensing, machine learning, Big Data, Internet of Things, intelligent systems, nanotechnology

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Technological revolutions have shaped the course of human history, the most striking recent example after the industrial revolution being the advent of computers. Both revolutions produced a dramatic change in labor, or in the type of labor activities executed by humans, even before the concept of a job market even existed. We are currently facing the prospect of another revolution with the coming of age of artificial intelligence combined with the development of intelligent systems [1]. Recent advances in these areas are making it possible to develop algorithms and devices capable of replacing humans in many (if not most) intellectual tasks– well beyond mechanical work or routine tasks. Furthermore, for the first time in history, generating knowledge without human intervention appears as a plausible, tangible possibility. Needless to say, if machines were to be capable of transforming information into knowledge, the landscape of science and technology would never be the same. Two rapidly-converging movements are responsible for the development of intelligent systems. On one hand, sensing and communication technologies are being used to control daily activities, with the so-called “Internet of Things” (IoT) paradigm combining the real and digital worlds through a continuous symbiotic interaction [2]. The other movement, vaguely referred to as “Big Data”, is the one associated with providing machines with the capability of integrating and interpreting the tremendous amounts of data being collected and curated for use in transforming information into knowledge. Big Data includes methodologies from machine learning, natural language processing, data visualization, and other areas. One may wonder whether there will be anything left for (human) researchers to do in areas such as nanotechnology and sensing. Indeed, there is. And a lot, at least in the foreseeable future (the next two decades, perhaps?). For efficient intelligent systems will only be developed and deployed if considerable amounts of reliable data are available to them. Many of the intelligent systems envisaged, e.g., for purposes of surveillance, quality control in food and beverage, and clinical diagnostics, require extensive sensing. Ubiquitous sensing, already demonstrated to be applicable in environmental monitoring or in clinical diagnosis, may become routine. In this paper, we shall discuss the requirements for producing the sort of intelligent systems we claim may revolutionize many areas of science and technology. By intelligent system we mean those systems capable of taking decisions depending on 2 ACS Paragon Plus Environment

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the input they receive, and which can also learn from previous interactions and adapt. Analogously, an intelligent sensor can take actions depending on the environment and on what they sense, rather than merely providing a measurement of a signal. Obviously, intelligent systems may be built with sensors that are not intelligent, as the learning and adapting parts could depend on other components in the system. We believe some of the predictions we make are informed guesses based on recent progress in sensing and artificial intelligence, in general, as well as in machine learning algorithms. Emphasis will be placed on intelligent systems rather than on issues related to sensing, as these have been covered in reviews and references, some of which are mentioned throughout the paper.

Recent developments with nanotechnology Virtually all intelligent systems applications involve sensing devices, and in many cases also actuators that, controlled by computers, can turn almost anything into an “intelligent entity”. They can be homes, buildings, cars, and even commercial transportation vessels [3] or whole cities with intelligent management of power grid or transportation infrastructure [4] [5]. Developments in nanotechnology should play a leading role in the expected progress in this field since both sensors and actuators are built with nanomaterials and/or using nanotechnology methods. The unique properties of nanomaterials allow embedding specific functionalities into sensors and actuators, as well as reducing costs, which is essential for the dissemination of devices. Nanotechnologies have already yielded significant advances in sensing and biosensing, in many different directions. Just by way of illustration, a non-systematic survey on ACS Sensors shows promising developments in self-powered biosensors in which biofuel cells are employed as power source and biosensor simultaneously [6], in microfluidic systems [7], including electronic tongues [8] [9] [10] [11], in the use of plasmonics for enhanced biosensing [12] and in detection systems with embedded circuitry to facilitate monitoring processes, products or the environment. One example of the latter systems is a sensing platform that uses a device designed to detect explosives and other harmful chemical vapors [13]. We should remark that, in this paper, the examples are normally related to sensors or biosensors according to their traditional definitions; e.g. a biosensor is made with biological molecules and/or detects biologically-relevant molecules. However, data analysis is central to the discussion of 3 ACS Paragon Plus Environment

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ubiquitous sensing and intelligent systems in general, and the examples could be extended to any type of sensing as in the various modalities of imaging. Figure 1 illustrates our concept of the interdependence between intelligent systems and nanotechnology, analytical chemistry and other areas of chemistry and materials science. For the intelligent systems are based on learning from data, and obviously there will be no learning if reliable, quality data are not available. Therefore, these areas are crucial to fabricate and optimize the sensing units that are required for ubiquitous sensing. This is the meaning implied by the box on the left with an arrow point to the box on the right, of Intelligent systems. As for the Intelligent Systems, the gathering of data with IoT – which again is dependent on ubiquitous sensing – is one of the sources for generating the data (Big Data box), onto which a range of machine learning techniques is applied to produce the variety of applications mentioned along this review.

Figure 1. The box on the right represents a class of intelligent systems based on IoT, where ubiquitous sensing is essential as a data provider for both IoT and Big Data. Machine learning can then be used in data analysis to yield the final applications. The box on the left is meant to indicate that intelligent systems rely on other areas, such as nanotechnology and chemistry, to produce the sensors and devices with which data are generated. The list of developments is too long to be discussed here in detail and we shall not dwell upon them, since such developments have been described in review papers [14][15]. Rather, we shall concentrate on the main requirements we believe are needed 4 ACS Paragon Plus Environment

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for the next-generation sensors and biosensors to fulfill the promise of IoT and intelligent systems. We identify four major topics where important challenges exist: i) Wearable and implantable devices ii) Device engineering for producing low-cost, robust sensing units iii) Understanding the physical mechanisms behind sensing and biosensing. iv) Enhanced methods to analyze data While the fourth topic is mostly related to computational and statistical methods, to be discussed later on, advances in the first three depend basically on approaches akin to nanotechnology. Wearable and implantable sensors are key to ubiquitous sensing, and have demanded considerable efforts in new materials and detection principles (see [16] and [17] for reviews). The quest for low-cost, robust sensing units has motivated research into a variety of materials and methodologies for their fabrication. In electrochemical sensors and biosensors, for instance, screen-printed electrodes are functionalized with cheap carbon-based materials for detecting neurotransmitters relevant for neurodegenerative diseases [18]. Investments in device engineering, on the other hand, seem to be increasingly required to ensure the multitude of sensing systems developed at lab scale will reach the market. Indeed, it is surprising that relatively few biosensors are commercially available, which seems paradoxical given the thousands of papers published and the many uses at lab scale. This is a manifestation of the disconnect between university research and real-world applications. As for understanding the physical mechanisms responsible for the increasingly superior performance of sensors and biosensors, a myriad of theoretical and experimental techniques is being employed. Bizzoto et al. have stated that designing improved biosensors requires analytical methods to be integrated to better characterize biosensor interfaces [19]. Theoretical modeling, on its turn, is crucial for the design of some stateof-the-art plasmonic sensors which exploit surface-enhanced phenomena associated with nanostructures for sensing and biosensing (see e.g., [20]). A prototypical example is perhaps the detection of chiral molecules with plasmon-enhanced circular dichroism [21]. Resorting to more sophisticated data analysis was inevitable in view of the large amounts of sensing data generated in modern equipment and the possibilities opened by the enhanced processing capability of computers [22]. Statistical and computational methods used to a limited extent in the last few decades are now being revisited and 5 ACS Paragon Plus Environment

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gaining prominence, as is the case of chemometrics methods [23], multidimensional projection techniques [24][25][26] and machine learning algorithms [27]. One recent example was provided by Li at al [28], who described an approach that yields successful discrimination of 14 representative liquors (including scotch, bourbon and rye whiskies, brandy, and vodka) using a 36-element colorimetric sensor array comprising multiple classes of cross-reactive, chemically responsive inks. The authors demonstrated liquor discrimination is possible using several multivariate data analysis methods, including principal component analysis, hierarchical clustering, and support vector machines [29].

Big Data and Machine Learning There have been many examples of machine learning applied to classify samples of materials based on measurements obtained from sensors and biosensors. Albeit such data cannot be characterized as Big Data (which poses many challenges, as mentioned later in this paper), nevertheless the applications may be taken as proof-of-concept of the feasibility of intelligent systems. Many examples originate in the area of medical diagnostics, well beyond the widespread traditional image processing and analysis techniques [30][31]. Computer-assisted diagnosis based on analysis of data collected by typical sensors or biosensors are increasingly common, as in the study reported by Nakhleh et al. [32]. The team used a nanoarray of sensors whose data from collected breath samples of over 1,400 subjects were treated with machine learning methods for the diagnosis of 17 diseases. The Big Data component of intelligent systems is concerned with learning from the data, for which significant advances have been attained over the past few years. Deep learning methods and the virtually endless computational capacity available are paving the way to creating computer systems capable of performing activities only imagined on science fiction books, from “simpler” tasks such as driving cars or recognizing elements in images, to sophisticated intellectual tasks such as composing complete music pieces, language translating or even writing meaningful texts [33][34][35]. Through examples and training, computers are now able to learn and make decisions; in some cases with advantages over the cognitive system of humans. Thus, in many situations they can enhance human capability for executing some tasks, whilst in others they can replace a human altogether. For example, electronic noses and electronic tongues may perform sophisticated tasks that typically require highly trained human 6 ACS Paragon Plus Environment

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expertise, as in predicting the scores a human sensory panel would assign to a wine [10]. Yet, there are many bottlenecks to overcome before it becomes possible to benefit from automated intelligent systems in their full potential, as discussed later.

Intelligent systems Several intelligent systems have already been deployed, either as proof of concept or in commercially available products. Many such applications, although perceived and commercialized as “intelligent solutions”, and indeed capable of handling complex situations from a human perspective, require quite limited intelligence and no creative thinking at all – their impressive capabilities are afforded by the combination of real time sensing and data processing. One of the main targets of such systems has been the automation of objects in a house. For instance, the Goji Smart Locker can be operated through a mobile application, to open and close doors automatically, and send alerts when a door is unlocked. Intelligent systems operated with cell phones also allow one to turn lamps on-off, adjust the intensity of an illumination source according to a song, set a time to turn it on-off and set it to flash when the user's phone receives a call [36]. Ambient temperature can also be controlled considering the user's previous choices [36], and household or home activities can be managed by a system connected to motion and temperature sensors. Another class of so-called intelligent systems involves monitoring and assessing the performance of individuals during physical activities. There are already wristbands that record user movements and provide assessments of physical activity, including recommending exercises [37]. In many cases, sport analysis relies on the combination of IoT and Big Data approaches. Cycling is one example with a long history in datadriven innovations. Global Positioning System (GPS) data, power measurements (pedal stroke), heartbeats, and other data have been combined to assess the performance of an athlete and to track his/her evolution during a competition or a season. Sensors have also been attached to kayaks to help professional athletes to measure and improve their paddling techniques [38]. Team sports have taken advantage of in-detail information provided by sensors, such as tactical analysis in elite soccer [39]. Location data and graph theory make it possible to identify key players [40] and understand relationships between the ball passing patterns and the outcome of a match [41].


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Intelligent systems are often also associated with personalized medicine, such as in the real-time management of data collected in connected medical devices in wearable electronics or home installed health monitors. Such data permit, for instance, real-time electrocardiogram telemetry [42] follow-up by health professionals, or fall detection in elderly individuals [43]. Another example is the integration of sensors into sanitary appliances to capture weight, blood sugar levels, and other vital signs, allowing the early detection or monitoring of some medical conditions, e.g. helping diabetics to control their schedule for insulin shots [44]. Specifically for diabetes, machine learning and sensors have been used in commercial products [45], such as in intelligent glucose monitoring systems for optimal insulin dosages [46], as a nutrition coach for meal option recommendations [47], and as a diagnosis tool through the screening for eyes diseases [48]. Wireless in-body devices for medical prevention, prognosis and treatment are also already in use, such as capsule endoscopes and pacemakers, both replacing conventional invasive procedures [49] and promoting a shift from a symptom-based healthcare model to a proactive model [49]. Even simple applications running in mobile and wearable devices have made it possible to identify early signs of mental disorders such as depression and schizophrenia [50]. For industry and commerce, in general, the concept of intelligent systems can be exploited in the incorporation of sensors for control of various kinds. It is possible to track where, when and how a product is used, or the condition identification and use of connected components, or the state of conservation of a product. Intelligent sensors in machines can report component wear and identify faults before they stop working. In manufacturing, civil engineering, and aerospace industries, sensors allow monitoring, performance evaluation, prediction and reporting of structure integrity [51]. In vehicles, sensors can monitor speed, fuel consumption, number of stops, and engine functioning. In addition to reducing fuel costs and maintenance, they can reduce CO2 emissions and increase the vehicles lifespan. Considerable impact on logistics is expected with this type of system, such as truck stop monitoring, and surveillance camera management [36]. Beyond logistics, safety issues can be addressed as in monitoring physiological or behavioral signals to detect driver´s drowsiness and issue immediate alerts [52], where a major challenge is to deploy non-intrusive sensing devices. The intelligent systems concept will also very likely transform precision agriculture, enabling detailed monitoring of air temperature, soil, wind speed, humidity, rainfall probability, solar


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radiation, nutrient concentration in the soil, leaf moisture and coloration of fruits. Irrigation mechanisms and controlled release of fertilizer can be triggered, potentially improving production and nutrient use efficiency [53].

The future The point has already been made that intelligent systems are closely related with ubiquitous sensing. It is thus evident that addressing critical issues in sensing and biosensing is key to the future of such systems. Taking the case of computer-assisted diagnosis, for instance, a glimpse at the literature indicates there is still a lot to be done as far as new materials, film architectures, detection methods, overall performance optimization, and device engineering are concerned. For example, in order to achieve a reliable use of deployed sensor systems, issues such as reproducibility, sensor drift and calibration, must be addressed, especially during extended use that may be 10 years or more for implantable sensors [54]. Data produced by interconnected sensing systems is one pillar of our twofold vision of the future, the other is the ability to make sense of it all. The full potential of data can only be developed if advanced analysis solutions are adopted beyond the standard statistical methods. Analyzing data produced by a single sensor system is one problem, converting into knowledge data collected from multiple interconnected systems can be considerably harder. Machine learning has shown promising results in handling the data deluge problem over the last years, mostly enabled by the impressive computational power now available that permits implementing approaches that would be unfeasible in a not so distant past. Nonetheless, many challenges remain to be tackled for pervasive intelligent systems to be deployed in practice to wider consumer groups. These are related with both a computational perspective contemplating aspects related to the data and the algorithms, and a human perspective with the potential implications of relying on such systems. Without serious consideration of those aspects, intelligent systems may fail to gain consumer trust. Robust data management is an important issue. Full data integration and realtime data analytics capabilities are requirements of systems operating on large-scale data distributed across connected products and IoT systems. The current data management solutions rely mostly on the client-server model, in which data is sent to 9 ACS Paragon Plus Environment

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the ‘cloud’ for centralized storage and processing, and data servers are handled as isolated standalone entities. This model is not adequate to meet such requirements, as it does not favor integration and real-time processing. Standardization, reliability and interoperability are also major issues for system deployment. From a societal perspective, further debate must be encouraged towards establishing proper regulations on data sharing and privacy to protect individual interests against those of large corporations or illegitimate actions. Deeper concerns with security and privacy emerge as people bring more Internet-connected devices into their home and their life, e.g., cameras and microphones could be hacked and used for spying, or data collected may be stolen or used against the interests of the owner. Given the inherently interconnectivity of IoT systems, recent attacks exploiting specific IoT vulnerabilities had a direct impact on many websites, including Twitter, Netflix, Spotify, Airbnb [55]. Several episodes have exposed the need for more secure architectures and clear policies to protect and guarantee the transmitted data will not be intercepted or captured by unauthorized users, as well as for legislation to attribute responsibility when misuse does happen. All in all, society must be aware that the facilities yielded by intelligent systems come at a privacy risk. Machine learning experts are also concerned with fairness, accountability and transparency in machine learning (http://www.fatml.org/) [56], after several published studies revealed potential bias issues in learning algorithms, e.g., towards race, gender or age. For instance, a recent study has shown that word associations learned by a machine learning algorithm reflect human semantic historical and cultural biases – which may be just factual or neutral, or potentially harmful, e.g., reflecting prejudice towards race or gender [57]. Perhaps for the first time ever, issues of trust and ethics of algorithms are taking priority over effectiveness. Data curation and the definition of best practices to ensuring data quality are vital components in these scenarios, so that learning algorithms will need to incorporate identification and resilience mechanisms for handling situations in which data might be poor-quality, or biased. There is a pressing need for legislation to establish responsibilities in cases where decisions and actions are taken by intelligent systems or by humans assisted by them. This is a particularly complex issue, as legislation is handled within political borders. Nonetheless, as a first step, system developers, data scientists, government officials and


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the lay person need to become aware of the implications and risks associated with databased decisions. Optimists as we may be with the future of machine learning and intelligent systems, we cannot foresee machines being able – in the short term – to establish the requirements for the new developments and executing the research associated with them. Intelligent systems will have to be created to mimic such human activity as in the design of new systems. And it is not merely learning from the data, at least not as we have seen done in the Big Data movement. Algorithms with truly intelligent behavior should be able to reason, plan and act creatively [58]. According to Hawkins [58], understanding how the brain works, in particular the neocortex responsible for learning, is essential for building future thinking machines. Hawkins identifies three essential aspects of biological intelligence not embedded into current AI algorithms which he believes are required for intelligent learning behavior. These aspects are: learning by rewiring, which ensures fast, incremental and continuous learning; sparse distributed representations that are intrinsically robust, generalizable and fault tolerant; and the use of movement to learn about the world, or sensorimotor integration. Maybe, however, systems that exhibit ‘human-like’ intelligent behavior may not be needed, whereas systems that can offload humans from tasks that are stressful, distressing, mechanical or dangerous are obviously welcome. For these, we have been witnessing impressive developments in machine learning over the last decades. As far as sensing and biosensing is concerned, we believe the future will be shaped by a combination of efforts in materials science, chemistry, physics, nanotechnology, etc. in creating new devices, much as it is done today, and initiatives to integrate data and employ computational methods (especially related to machine learning) for generating the truly intelligent systems. Perhaps the health care segment will be among the first to benefit from these systems, particularly because there is already a suitable combination of demand & possibilities & affordance, driven by the high health care costs. Indeed, computer-assisted diagnosis systems are already being proposed and implemented. In their design [59], two major types of problems may be identified: difficulties associated with data collection, curation, accessibility, and privacy preservation, and the limited ability of artificial intelligent algorithms. With present technology, the latter have been proven efficient in classification tasks but not in explanation-types of task, which may be crucial for diagnosis as well as many other 11 ACS Paragon Plus Environment

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applications. Regardless of which sector, the development of intelligent systems is not going to be simple, easy endeavors. The pace of progress will be dictated by the human ability in gearing research and development into integrated projects that allow for the abovementioned combination of initiatives. For the wide community of ACS Sensors, the main message we want to convey is related to the need of integrating the inherently multidisciplinary tasks of sensing and biosensing into a broader framework that caters for societal aspirations. The efforts toward developing new materials and methods for sensing must be continued much in the same way as the community is used to, also because developments in IoT and intelligent systems will depend upon them. On the other hand, researchers should be conscious that at some point the data and contents they produce should be made machine readable, which in addition will assist in implementing more sophisticated strategies of data analysis. Indeed, in spite of the recent publicity received by datadriven discovery, we believe the community still makes use of limited resources in terms of data analysis. Machine learning techniques, for example, have been proven beyond doubt in many cases to provide higher performance, but they are still scantly used. Improving the methods of data analysis should be pursued by researchers in sensing and biosensing, regardless of whether they are interested in IoT or intelligent systems.

Acknowledgements This work was supported by INEO, CNPq and FAPESP (2013/14262-7, 2017/05838-3).

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