2023 Essay for Design and Innovation, Mangement and Processes module

Introduction

The development of affective computing marks the departure from the traditional paradigm of computers as purely logical entities towards a more nuanced and human-centric approach. The significance of this shift lies in its potential to enhance the overall user experience bringing a transformative shift in human-computer as well as human-human interaction. The development of affective computing could be considered incremental, as it builds upon existing wearable device hardware capabilities, although with the introduction of emotion identification software the fundamental capabilities of wearables have progressed altering the meaning of the device. This can consequently be considered discontinuous from previous wearable device advancements (Norman and Verganti, 2014).

The implementation of new software features have enabled a radical shift in how the technology can be used creating new product categories and opening up possibilities for major changes (Norman and Verganti, 2014). Affective computing in wearables can therefore be considered a radical innovation and have developed new markets in the healthcare space as well as revolutions in education, music and market research.

Using WHOOP as an example of innovative practices in wearable technology, having incorporated affective insights into their services, and also indicating strong growth and market position with a $3.6 billion dollar valuation (Parr and Puri, 2023); we can analyse key innovation blocks for affective wearables.

Key components that make affective computing in wearable devices innovative can be divided into the following groups categorised by the author: hardware capabilities, software capabilities and service provision.

The development of wearables capable of interpreting emotional responses relies on the accuracy of physiological sensors, a small size of sensor that can be integrated into a wearable device as well as a feasible cost of manufacturing that makes the production of the device profitable. This progress has been driven by advancements in flexible materials, sensor technology, miniaturization, wireless communication, and data analytics (Zhang, 2023), that without these developments wearables would be too uncomfortable and intrusive to use.

However reliable sensor information equally requires advanced software to analyse complex patterns and generate meaningful insights into the users state (Zhang, 2023). These developments represent a range of innovations from the fields of software, artificial intelligence and machine learning as well as a progressive understandings of the human emotional system.

The final innovation facet is the coordination of software and hardware to supply users with valuable services. Utilising new ways of visualising previously unseen data, creating novel systems and determining innovative ways people can interact with technology. An example of this being WHOOP’s new ways of presenting the user with health information, using data to produce percentages for more abstract insights such as recovery and strain over more concrete data points such as activity level and steps (WHOOP, 2023).

Using innovation categories from ten types of innovation (Keeley, L. et al. 2013), these facets correspond with the following innovation types:

Product Performance Product performance has been improved with added engaging functionalities going beyond basic health tracking. For instance, a fitness tracker that recognises stress levels and adjusts its recommendations accordingly improves the overall product performance by addressing users’ emotional well-being (WHOOP, 2023).

Service Advanced software in affective computing wearables transforms the service by providing tailored insights and feedback. The use of evolving machine learning algorithms improves the accuracy of the data filtering process over time (WHOOP, 2023), meaning it understands your individual nuances the longer you wear it. This adds value and personalises the service.

Customer Engagement There are a number of contributing factors to make affective wearables innovative in the customer engagement category. Firstly the intimate nature of the technology - as something you wear continuously, the personal degree of understanding on you as an individual, and the engaging service provided to users through interactive data visualisation techniques. This personalised insight and sense of emotional understanding, enhances utility while also fostering a deeper connection between the user and the device, increasing engagement.

Continuing to use WHOOP as an example, product performance and service enhanced with affective computing have been utilised as a tactic to revolutionise the customer experience and engagement with wearables.

Previous wearable devices such as Fitbit utilised the innovation tactic of experience automation to make tasks such as run tracking easer, however with the introduction of affective features new experiences have been enabled with insights into stress and mental wellbeing (WHOOP, 2023). The emotional features of the WHOOP band have also capitalised on the mastery innovation tactic “helping customers to obtain great skill or deep knowledge” (Keeley et al., 2013).

Diffusion and Transition

Using Rogers’ Diffusion of Innovations theory (Rogers, 2003) we can analyse how wearable emotion recognition devices have diffused the stages of innovation adoption. identifying the innovators as being the MIT Media Labs research team, the early adopters being spin-offs from the innovations made at MIT with the companies of Affectiva and Empatica and the latest stage of emotion recognition wearables being the early majority with companies such as Apple and Whoop investing in and incorporating affective computing into their fitness wearables.

Initial attempts to create emotionally aware wearable devices can be traced back to the ‘Galvactivator’ designed in the 1990’s to be worn on the wrist and could detect changes in skin conductance displaying this with LEDs. The Galvactivator was patented but never available for sale (Picard, et al., 2001). The idea of computers expansion into the emotional sphere of human experience was later solidified in the MIT media lab.

Picard’s research which ultimately resulted in the development of a new field of computing, was stimulated by her work in computer vision. While attempting to improve computers ability to recognise the contents of images and video by modelling human mechanisms of vision and perception, Picard ran into emotion as an integral part of how humans perceive (Picard, 2000). This marked a turning point in her research and inspired confidence in the intrinsic importance of emotion in decision making, perception and learning. Picard’s research in this area lead to the release of the acclaimed book ‘Affective Computing’ which aimed to combat the early challenges of peoples negative perception of emotion being used in computing (Picard, 2000). Picard’s research also ultimately culminated in the founding of affective computing start-ups: Affectiva in 2009 and Later Empatica in 2013.

Affectiva was started with the aim to use affective computing in the form of artificial intelligence, and was trained on facial expressions.

This enabled the company to evaluate the effectiveness of marketing and advertising campaigns, provided insight to software developers on how they can illicit emotional responses from their services and create customer engagement and sentiment analysis tools for retailers (Press, 2017). Affectiva licensed the Galvactivator and developed their own skin conductance sensor called the Q Sensor. A wrist band which using skin conductance, temperature, and motion can interpret identify user affects, which gained attention for its applications in healthcare and market research.

The niche of affective computing began the scaling up process (Seyfang & Longhurst, 2015) with the founding of start-ups such as Affectiva or Empatica. From these initial start-up spin offs from MITs Media labs the early developments inspired a replication of the concept in various affective wearable technologies.

The penetration into the mainstream however required the translation of the technology away from the niche areas of market research, outpatient monitoring and mental health and into fitness wearables. The adaption of the technology into fitness wearables, the success in part facilitated by the compatibility with the existing system and the widespread use of wearables with heart rate monitors has led to the recognition of the potential for emotional sensing in consumer technology.

Combined, the widespread use of physiological sensors, with the increased emphasis on individuals mental health (Foulkes & Andrews, 2023) generated opportunities to utilise affective computing to tie the two areas together with the integration of emotion aware wearables. The heightened public understanding of the interconnected nature of the physical and mental has supported the development of wearable device used for physical and mental wellbeing.

Top-down innovation typically refers to a process where the drive for developing new products or services originates from entities, such as research institutions, corporations, or government agencies, rather than directly from user demands or market forces. The development of devices dedicated to affect monitoring emerged from research into improving human-computer interaction and stems from researchers recognising potential applications in their studies, leading to the creation of products and services. Making this an initial top-down innovation with opportunities for bottom up influence increasing as the user base of this technology increases into the early and late majority of adoption.

The Competitive Landscape

Given the utility of affective computing in wearables for supporting physical and mental wellbeing the technology has started to be employed for elite athletes, police and military applications. The aim being to provide valuable insights into how an individual responds to different real life scenarios and training procedures to enhance the performance, reduce risks and increase emotion regulation skills.

Within this emerging space of affective wearables for occupational performance, companies strategically emphasize distinct product aspects to carve out their unique market positions. The main niche competitors found within this space being: Zephyr Performance Systems and Equivital. While these niche offerings are more specialised they still compete against the widespread use of fitness wearables such as the apple watch, Polar activity sensors, Fitbit and Garmin.

Competitive considerations include: sensor accuracy, dataset size, customisation, and service features. Companies seek to highlight the precision of their data collection capabilities, leveraging advanced sensor technologies to set their products apart. The size and diversity of the datasets employed for training emotion recognition algorithms serve as a competitive edge, with companies emphasising the comprehensiveness of the basis for their technology. Additionally, the difference between customisation and standardised solutions becomes a focal point, with companies showcasing the flexibility of their offerings, catering to specialist users who desire tailored emotional recognition capabilities. On the other end of the spectrum companies can seek to gain a competitive edge by highlighting their ease of use and consistency through ready-made solutions. Paramount among these considerations is the interplay between service functionality and feature richness as a distinguishing factor, as companies strategically highlight the integration of advanced features while ensuring user-friendly functionality.

The strategic decisions regarding the breadth or specificity of applications allow companies to tailor their products to diverse contexts and user needs. In this competitive landscape, the differentiation of affective wearables hinges on a nuanced emphasis on sensor capabilities, customization, dataset considerations, and service functionality, each contributes to a unique product identity in the market.

The improvements that niche occupational performance wearables offer, over more generalised solutions are as follows. Zephyr promotes its customisation and access to raw data (Zephyr Performance Systems, 2023) compared to consumer wearables like the Fitbit which do not offer easy access to the raw sensor data. Equivital also highlights the ability for the user to control how their data is handled, showcasing the variety of methods data can be transmitted and communicated (Equivital, 2023).

Feature richness and customisability are paramount with these devices as their purpose is to integrate with professional teams in sports, first response and military around the world. Aiming to assist coaches and training regiments with the enhanced objectivity and insights that sensor data can provide.

Given the benefits of affective computing applications in the work place, and the advantages tailored offerings have over more general applications, there is an identified opportunity for increasingly specialised niche offerings in the wearable occupational performance enhancing market.

Conclusion

This innovation investigation into affective wearables has developed the authors understanding of how innovation can be systematically reduced in theoretical complexity to allow for consideration of its variables and then the utilisation of these variables as a tactic. Specifically in cases discussed, how new product features can be exploited to innovate in multiple different categories of innovation, such as product performance, service and customer experience.

Affective computing has not only evolved in its technological capabilities but has also surpassed its early research limit to become a catalyst for transformative innovations in market research, health and wellness, as well as our understanding of human emotions. While having come along way to its latest stage of development, where it’s been translated into fitness wearables despite, it is still in its infancy in terms of the extent of its application.

Humans are intrinsically interested in ourselves and each other, personally and professionally. This corresponds with the main benefits of enhanced understanding and measurable results offered by wearable affective computing, suggesting that the extent of the diffusion of this technology will not be limited to its current central position in the wellbeing space but will continue its development. In particular its development into opportunities in enhancing human performance. This makes the technology likely to continue its expansion into wearables for emergency services, NHS workers, military, and police.

This brings into question ethical and privacy implications with growing debate around the handling of user information and the explicitly personal nature of the data affective wearables collect. As new products and services arise the impact of the data handling process should be rigorously investigated and user autonomy should be put first.

References

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