Knowing this, stress reducing applications incorporated in interactive systems should consider the hardware reliability, as well as the confounding variables when obtaining stress data with physiological sensing hardware. As suggested in the implications mentioned previously, designers should check the users’ stress level while using their applications preferably with assessing subjective data given the high inaccuracy in mobile sensing contexts. By answering the named research questions, the foundation is laid for investigating suitable techniques incorporated in interactive technologies to reduce stress. Nevertheless, the implications stated can have a severe impact on the realization of stress mitigating interactive systems. Therefore, the following chapter will concentrate on the investigation of the revealed constraints and consequently a design space will be introduced summarizing considerable decision criteria for sensing hardware choices from the researcher and end-consumer perspective answeringRQ1d.
Chapter 4
Clustering Limitations of Physiology-Aware Systems
In Chapter 3 it has been pointed out how physiological measurements can be used to detect stress based on their correlation with subjectively assessed data.
Moreover, the limitations in physiological measurement hardware have been revealed resulting into certain constraints in such measurements. Based on these findings implications for the design of interactive systems have been derived addressing the challenge of choosing suitable physiological sensing devices.
This chapter presents a detailed investigation of the potential shortcomings in physiological measurement devices from a practitioner perspective. Hereby, consumers and researchers, being named experts in the following, were inquired and involved in the development of the Design Space for Physiological Measurement Tools. This tool provides a summary of the distinct aspect being worth to consider when designing interactive systems that incorporate physiological sensing.
Throughout the past decades sensing physiological signals has evolved from a high-end technological challenge into a standard functionality of every-day consumer ware. The global mobile communications analyst CCS Insight published a "Global Wearable Forecast" illustrating the development of wearables from 2015 to 2019. They predicted that the sales number for wearables would 20% per year until 2024. The market would be estimated with $29 billions given
243 million unit sales that could be reached by 2022 [37]. They further observed that around 25 millions of kids’ watches were sold in 2017 on the Chinese market what is said to be outstanding given that privacy concerns and regulations are more severe particularly in Europe. According to a survey by the market research platform Statista conducted in 2018, 43% of the respondents admitted that they are "somewhatlikely" or even "very likely" to purchase a wearable, such as the Fitbit or a smartwatch [48]. Despite such impressive stats promising the ongoing success of physiology sensing hardware, increased distrust among its users could be observed. Especially, the target group of the elderly has difficulties to adapt the new technology also due to the distrust regarding the system and problems to accept such new technology if its functioning is unknown [32]. What has been suggested by prior work [15, 188], was confirmed by Rupp et al. [225, 226]
who found that the two factors self-determined motivation and technological trust are significantly affecting the users’ willingness to use a fitness tracker continuously. In practical terms, this means that doubting the benefits of fitness tracker usage will lead to abandoning such technologies because Kononova et al.
[142] could show in their work that maintenance strongly relates to perceiving the tracker usage as a longitudinal personal benefit. Nevertheless the recording of physiological signals is still a valuable method to detect physiological changes and trace them back to stress states. Since not only consumers rely on such data, but also researchers use fitness trackers for their studies [105, 122, 145], the suitability of such tools is important. The relevance of this issue certainly lies in the awareness of the given limitations physiological sensing bears. Some researchers [122, 202] even report that they reviewed different hardware before deciding upon a suitable sensing technology or device. This behavior clearly shows that there is need for a comprehensible and concise summary of predominant decision criteria of which consumers and researchers alike would benefit.
As it has been pointed out in the previous chapter, there are certain constraints when obtaining physiological data under different conditions. In this chapter, I show how consumers and researchers perceived these limitations and in how far they affect the user experience of both groups contributing to answer what implications such constraints have responding toRQ1c. Following up on the findings from prior work [98], I demonstrate how relevant criteria for physiology sensing systems have been identified in two user target groups. As a finding from this investigation, the "Design Space for Physiology-Aware Systems" is being present and visualized. Consequently, I answerRQ1dhow we can identify and summarize criteria representing the differences among physiology measuring hardware. Being aware of the restrictions in different hardware is crucial from a design and development perspective when aiming for a reliable detection of stress
in the user. I regard the conscious decision for or against a specific measurement tool as the prerequisite for the potential success of an application which is able to mitigate stress. In case the detection part of stress fails, such an application will fail too and consequently the trust in technology will decrease.
This chapter is based on the following publication:
• R. Poguntke, K. Hänsel, H. Haddadi, A. Alomainy, and A. Schmidt.
Developing the Design Space for Physiological Measurement Tools: A Theoretical Foundation of Decision Criteria. (to be published)
4.1 Related Work
Prior work has been mainly focused on exploring the consumer perspective often with focus on usage patterns or efficiency. In contrast, we hardly know on what criteria consumers base their decisions to use fitness trackers incorporating physiological sensing. Even less explored is the decision process of researchers when choosing suitable hardware for their research projects.
User Perspectives on Sensing Wearable Usage With the rise of wearable sensing devices in form of fitness trackers or smartwatches, huge companies threw various physiological sensing hardware on the market promising consumers to increase health and wellbeing by monitoring physical activity.
Nevertheless 50% of the users abandon their fitness trackers after one year [153], which has been extensively portrait in prior work [50]. To overcome this problem Canhoto and Arp [33] suggested to promote adoption and increase
"device portability" and "resilience" to drive long-term usage. Respectively, there has been previous work on the capturing of consumer feedback on affective devices and on technological innovations in general as Prahalad and Ramaswamy [213] discuss. The information about consumers, their activities and usage contexts, being referred to as "customer intelligence" [49] has been proposed to be used to gain more insight in purchase criteria [31, 45, 84]. To bridge the gap between consumers and research examining user needs, Schuurman, Mahr and De Marez [239] suggest to apply the so-called "Lead User-concept" to increase user engagement as a driver for innovation. From a research perspective, there has been taken into account the user perspective recently. For example, Fritz et al. [77] investigated in a long-term study how 30 wearable owners use and
benefit from their fitness trackers, like Spiel et al. [254] did; both contributing to the understanding of the persuasive mechanism activity tracker manufacturers’
often aim at. Moreover specific user groups had been interviewed, as Tholander and Nylander [261] did with athletes to learn more about their benefit from smartwatches used for sports. Moreover, various studies focus on the target group of elderly using health monitoring devices [32, 142]. In a first exploration of user needs regarding affective wearables, Hassib et al. [106] conducted an online survey on acquiring, sharing and receiving physiological data with 109 participants. They found that users preferred to get information from all types of data and mostly wanted to share the information with closely related people, i.e.
partners and families.
While this work focused on the effects of sharing affective and physiological data neglecting the pragmatic requirements and challenges when dealing with physiological sensing hardware, Niess and Wo´zniak [188] had a distinct focus.
They inquired users on the motivation and the related goals for using fitness trackers against the background of the decreasing popularity of activity recording wearables. Additionally, their participants reported that they would like to have a better understanding of the data provided by their device and more importantly, that they lack confidence in these data. With respect to the design of physiological sensing wearables prior work has put a lot of effort into the exploration of user preferences regarding wearable wellness devices [149, 217] and its integration in design. For example, Kim et al. [133] proposed a development process that comprises three stages to promote user-centered design and Zhou, Ji and Jiao [296] aimed to mass customize devices according to consumer needs by using affective and cognitive design.
In contrast to the manufacturers interests to investigate the consumers’ usage patterns and opinion by market research methods, there has been no exploration on the requirements set by researchers who work and apply physiological sensing devices. The work by Nasir and Yurder [185] represented a starting point by collecting consumers’ and physicians’ feedback about wearable health technologies in the overall context of technology acceptance through an online survey. While Márquez Segura et al. [170] focused in their work on designing wearables to be socially engaging by asking design experts, i.e. larp designers providing interesting observations for the HCI community, the present work contributes to the understanding of consumers’ and researchers’ needs from a pragmatic point of view resulting in the seven dimensions of theDesign Space for Physiological Measurement Toolsand its sub-dimensions.