Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables.

https://www.mdpi.com/1424-8220/21/15/5218/pdf

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

https://www.mdpi.com/1424-8220/21/2/460/pdf

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

Managing finances is crucial for older adults who are retired and may rely on savings to ensure their lives’ quality. As digital banking platforms (e.g., mobile apps, electronic payment) gradually replace physical ones, it is critical to understand how they adapt to digital banking and the potential frictions they experience. We conducted semi-structured interviews with 16 older adults in China, where the aging population is the largest and digital banking grows fast. We also interviewed bank employees to gain complementary perspectives of these help givers. Our findings show that older adults used both physical and digital platforms as an ecosystem based on perceived pros and cons. Perceived usefulness, self-confidence, and social influence were key motivators for learning digital banking. They experienced app-related (e.g., insufficient error-recovery support) and user-related challenges (e.g., trust, security and privacy concerns, low perceived self-efficacy) and developed coping strategies. We discuss design considerations to improve their banking experiences.

/pdf/i-used-to-carry-a-wallet-now-i-just-need-to-carry-my-phone-20xfzdg2.pdf

“I Used To Carry A Wallet, Now I Just Need To Carry My Phone”: Understanding Current Banking Practices and Challenges Among Older Adults in China

The banking industry has been integrating digital technologies globally. However, accepting new technologies is challenging in particular for older adults. We focus on older adults’ banking experiences in China, where digital transactions have been growing rapidly, to provide a perspective on how they adapt to this trend. We conducted an online survey with 155 older adults who are 60 or above (M = 70, SD = 9) from 18 provinces to explore their banking practices and challenges. Our results show that older adults conduct banking transactions frequently. However, few do so using digital platforms despite long wait times in physical banks. The main concerns reported by them are about security and usability. Nonetheless, they hold a positive attitude towards digital platforms (e.g., apps, virtual banks). Interestingly, age and gender have significant effects on particular banking behaviors. We discuss our findings in the context of prior studies and highlight design opportunities for improving banking accessibility for older adults.

”Too old to bank digitally? ”: A Survey of Banking Practices and Challenges Among Older Adults in China

Despite a global upward trend in mobile device ownership, older adults continue to use few applications and fewer features. For example, besides directions, maps provide information about public transit, traffic, and amenities. Mobile maps can assist older adults to navigate independently, avail city facilities, and explore new places. But how accessible are current mobile maps to older adults? In this paper, we present results from a qualitative study examining how older adults use mobile maps and the difficulties they encounter. 172 problems were identified and categorized across 17 older adults (ages 60+). Results indicate that non-motor issues were more difficult to mitigate than motor issues and led to maximum frustration and resignation. These non-motor issues stemmed from three factors, inadequate visual saliency, ambiguous affordances, and low information scent, making it difficult for older adults to notice, use, and infer, respectively. Two design solutions are proposed to address these non-motor issues.

“Maps are hard for me”: Identifying How Older Adults Struggle with Mobile Maps

Long-term exposure to air pollution can cause adverse health effects. Many efforts are underway to develop affordable, portable, and accurate technologies to help people monitor air pollution regularly. Although personal, wearable air pollution monitoring technologies are popular among some technology enthusiasts and citizen scientists, we know little about air pollution monitoring practices and preferences of lay individuals. We conducted a sequential explanatory mixed-methods study (n = 321) to understand people’s current air pollution monitoring practices and their requirements for personal air pollution monitoring technologies. Although concerned about the adverse effects of air pollution (94%), less than 10% reported checking the levels of air pollution at least once a week. Respondents were more likely to carry a monitoring device as a bag accessory (74%) or wear it on their wrist (42%), than around their shoes, waist, or neck. If monitoring were available, however, it was unclear how much that would manifest behavior changes in individuals. We discuss how our findings can inform future technology design.

Personal Air Pollution Monitoring Technologies: User Practices and Preferences

We demonstrate “Blurry (Sticky) Finger” in which one uses the unfocused blurred finger, sense of proprioception, to aim, point and directly select a distant object in the real world with both eyes open. We showcase two demo applications. The first illustrates the accuracy and usability of the proposed method with the target objects lying at a fixed depth on a monitor. The second is an AR based object inquiry system, a more practical application. The user aims and encircles a real 3D object whose image is captured with the eye-to-camera offset compensated. The image is searched through the data base with the result augmented on an OST display.

Blurry (Sticky) Finger: Proprioceptive Pointing and Selection of Distant Objects for Optical See-Through Based Augmented Reality

In this poster, we propose "proprioceptive" pointing (and selection) in which we use the finger and the sense of proprioception, without focusing on the finger, to point and select an object in the real world for the purpose of further interaction. The assumption is that this will reduce user fatigue since the user will switch one's focus less frequently, while still being able to point effectively through the proprioceptive sense. Figure 1 illustrates the concept and how such a technique could be effectively used e.g. for object query system using a see-through glass. In this typical scenario, the user selects an object in the real world (by proprioceptive pointing/designation), which in turn is captured by the on-glass camera and then identified and recognized for final augmentation. Note the "blurry" fingertip used for aiming to the target object.

Resolving view difference between eye and camera for proprioceptive pointing and selection in augmented reality applications

Most AR interaction techniques are focused on direct interaction with close objects within one's reach (e.g. using the hands). Interacting with distant objects, especially those that are real, has not received much attention. The most prevalent method is using a hand-held device to control the cursor to indirectly designate a target object on the AR display. This may not be a natural and efficient method when used with an optical see-through glass due to its multi-focus problem. In this paper, we propose the "Blurry (Sticky) Finger" in which one uses the finger to aim and point at a distant object, but focusing only on the target with both eyes open (thus without the multi-focus problem) and relying upon the proprioceptive sense. We demonstrate and validate our claim through an experiment comparing three distant pointing/selection methods: (1) indirect cursor based method using a 3D air mouse, (2) proprioceptive finger aiming (Blurry Finger) with a cursor, (3) proprioceptive finger aiming without a cursor. In the experiment, Blurry Finger showed superior performance for selecting relatively small objects and in fact showed low sensitivity to the target object size. It also clearly showed advantages in the initial object selection where the hand/finger starts from a rest position. The Blurry Finger was also evaluated to be the most intuitive and natural.

Blurry (sticky) finger: proprioceptive pointing and selection of distant objects for optical see-through based augmented reality

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