Stephen Jia Wang

Volume-invariant ionic liquid microbands as highly durable wearable biomedical sensors

Most current wearable electronic products are often based on rigid circuit board technologies, limiting their ‘true wearability’ on the soft human body due to the mechanical mismatch between electronic and biological materials. ‘True wearability’, which means intimate contact with the soft human body, can only really be achieved by stretchable electronics that can mimic the mechanical features of the human skin. The use of nanomaterials or wavy metal/semiconductor materials represents a promising strategy to achieve stretchable electronic devices, but such devices often experience local material delamination or cracking. In this work, we describe an ionic liquid (IL)-based approach for the fabrication of rubber band-like, stretchable strain sensors, which can circumvent these limitations. Non-volatile and flow properties allow us simply to ‘fill and seal’ microchannels fabricated by 3D printing to obtain lightweight, waterproof and thermally sensitive wearable sensors. Despite the simplicity of their fabrication, the sensors show excellent performance, including tunable sensitivity, detection of a wide range of strains (0.1–500%), high durability (little change in signal-to-noise ratios after 6 month storage under ambient conditions), an excellent long-term stability of 50 000 life cycles under both low (5%) and high (100%) strains. We further show that our IL-based sensor can accurately identify wrist pulses, and can be woven with commercial rubber bands into colourful bracelets for hand gesture detection, and seamlessly interface with wireless circuitry to allow the detection of cervical movements.

Hybrid-Dimensional Visualization and Interaction - Integrating 2D and 3D Visualization with Semi-Immersive Navigation Techniques

The integration of 2D visualization and navigation techniques has reached a state where the potential for improvements is relatively low. With 3D-stereoscopy-compatible technology now commonplace not only in research but also in many households, the need for better 3D visualization and navigation techniques has increased. Nevertheless, for the representation of many abstract data such as networks, 2D visualization remains the primary choice. But often such abstract data is associated with spatial data, thereby increasing the need for combining both 2D and 3D visualization and navigation techniques. Here, we discuss a new hybrid-dimensional approach integrating 2D and 3D (stereoscopic) visualization as well as navigation into a semi-immersive virtual environment. This approach is compared to classical 6DOF navigation techniques. Three scientific as well as educational applications are presented: an educational car model, a plant simulation data exploration, and a cellular model with network exploration, each of these combining spatial with associated abstract data. The software is available at: http://Cm4.CELLmicrocosmos.org.

The Virtual-Spine Platform—Acquiring, visualizing, and analyzing individual sitting behavior

Back pain is a serious medical problem especially for those people sitting over long periods during their daily work. Here we present a system to help users monitoring and examining their sitting behavior. The Virtual-Spine Platform (VSP) is an integrated system consisting of a real-time body position monitoring module and a data visualization module to provide individualized, immediate, and accurate sitting behavior support. It provides a comprehensive spine movement analysis as well as accumulated data visualization to demonstrate behavior patterns within a certain period. The two modules are discussed in detail focusing on the design of the VSP system with adequate capacity for continuous monitoring and a web-based interactive data analysis method to visualize and compare the sitting behavior of different persons. The data was collected in an experiment with a small group of subjects. Using this method, the behavior of five subjects was evaluated over a working day, enabling inferences and suggestions for sitting improvements. The results from the accumulated data module were used to elucidate the basic function of body position recognition of the VSP. Finally, an expert user study was conducted to evaluate VSP and support future developments.

Barriers to the Implementation of Big Data

This chapter sounds a cautionary note about big data applications. In general terms, it discusses, in turn, the potential serious challenges to its use, including privacy, data security, reliability, cost, technical challenges, and potential barriers to its acceptance, which will need to be overcome. The barriers to acceptance and use vary greatly from one application to another, being close to zero for some applications (for example, urban weather forecasting), to possibly serious for more sensitive applications that involve even anonymised personal data. We conclude that big data is not a panacea for all urban problems—some important areas of urban sustainability are probably best tackled by traditional small data approaches or a judicious use of both big and small data. The barriers for some applications, particularly those based on personal data, will for some time be greater in the cities of many industrialising countries than in OECD cities.

Big Data for Sustainable Urban Transport

This chapter re-examines the general solutions proposed to improve the environmental sustainability of transport discussed in Sect. 1.3.2, with a view to understanding the potential for big data in each of these approaches. How can big data be used to reduce transport energy and emissions in cities? Specifically, how can big data encourage modal shift from cars to more environmentally friendly modes, and reduce vehicular transport overall through better trip planning? The chapter also includes a case study of a ‘personal transport planner’ designed for use in Beijing, based on the idea of a monthly personal transport energy quota.

Big Data for a Future World

This chapter looks to the future, given that applications of big data for urban sustainability are still in their infancy, and it could be many years before it can make a real difference. We try to place big data and urban sustainability problems in the year 2050 or even later, by first describing what the world of 2050 might look like, assuming that nations seriously tackle the global environmental and resource problems the planet increasingly faces. We then explore the possible role of big data in the cities of such a world, both in OECD and non-OECD countries, both in the transition period and later.

Urban Health and Well-Being Challenges

This chapter is a detailed look at urban sustainability from a health and well-being viewpoint, and is thus a complement to Chap. 1, which emphasised the biophysical aspects of urban sustainability. Two globally important health problems are the ageing of the population and the widespread rise in health costs as a share of national income. The health and well-being of urban residents, which goes beyond the mere absence of physical and mental illness, are examined for both OECD and non-OECD countries. The creation of a truly sustainable city in the future not only requires simple increases in energy efficiency. The personal quality of life for urban residents—the creation of livable, stable and vibrant communities—is also important, and will become increasingly so in future. The urban problems of China, home to 20% of the global urban population, are given particular emphasis.

The Potential for Big data for Urban Sustainability

This chapter first looks at existing data collection in cities, and its limitations, then at the reasons why making cities sustainable will need vastly increased amounts of data in future. It next describes the rise of the Internet of Things (IoT) and how the data from vast numbers of urban sensors could make cities ‘smarter’. The chapter also gives a number of examples of how big data and IoT is presently being used in various cities. Since the impact on sustainability in smart cities is presently minimal, we also look at the more advanced use of big data in other sectors. But big data alone will not in itself guarantee urban sustainability: supporting policies, including those for reducing energy and private transport use, and improving public health, will also need to be in place.

Big Data for Urban Energy Reductions

This chapter first discusses the smart grid, which will be a necessity if electricity production in the future is to be sustainable. The chapter then looks at energy in an urban context, emphasising domestic energy consumption and the role of big data in its reduction. It is found that experience to date shows that data provision alone, for example that made possible by smart meters, can not on its own effect the large cuts needed in household energy use. However, in future, householders could well be both consumers and producers of energy (for example, from rooftop PV cell arrays). Householders will inevitably become far more aware than they are now of the price of electricity and how this varies over time.

Big Data for Urban Health and Well-Being

This chapter examines the potential for big data in improving urban health and well-being, in the face of the ageing of global society and the rise in real healthcare costs. It looks at how more use of big data could help solve these and other health challenges, then gives actual or planned examples of its use in healthcare. The Quantified Self movement, discussed next, could prove a forerunner of a more general move to greater patient involvement in monitoring their personal health. The data would come from various apps on their smart phones, wearable devices, or body sensors. The chapter stresses the connection with the transport and energy chapters, given the role of these two sectors in urban air pollution, UHI and global warming and for transport, traffic-related casualties. As a specific example, a case study of a design of an instrumented chair (‘Virtual Spine’) to improve spinal health and general well-being is included.