The Problem
Women’s safety is an increasingly prevalent topic in the UK following the high-profile reporting of the murder of Sarah Everard in March 2021, prompting women to speak out about their own experiences on the streets of the UK.
To address these concerns, the team sought to design a wearable technology product that helps women feel safer when walking alone at night.
Project Outline
Details
UX/UI Research and Design | University Case Study
Role
UX/UI Researcher and
Lead Designer
Tools
Figma, Axure, Creative Cloud, PowerPoint
Duration
12 weeks
FemLENS
FemLENS system helps users feel safe whilst going from A to B.
Fem LENS is a combined physical and digital futuristic solution helping users get home safely. The Fem LENS package includes a contact lens worn by the user and used to map the environment around them.
This data is fed through to the mobile application where AI integration is used to analyse the information. A biometric sensor placed on a pulse point (wrist or ear) further relays data to the AI. The combonation of data points builds the users personal safety profile.
The Process
The team used an agile approach, segmenting and defining the teams interpersonal and technical skillset into defined stages. Initially approaching the tasks collaboratively, the team explored the research and early implementation processes, along with planning and generating ideas. Additionally, team members undertook a Colour Framework personality test (Kerzner, 2018) used to gauge individual strengths and communication styles.
The team was missing a 'red' personality, known for their goal orientation and assertiveness. The team's working personalities reflected mostly an enthusiastic and sociable yellow dynamic with precision of blue and the steadiness of green.
Organisation
Group Dynamics
Organisational Chart
Tasks were assigned accordingly with individuals' personal hard and soft skill set, to optimise time, and were split by scope, research, and design.
Additionally, the team devised a project planner to keep the project on track.
Research
Competitor Analysis
Competitor research was conducted by exploring the existing market through different channels online. The analysis indicated a variety of devices and services available on the market, specialising in personal safety products for women.
Assault alarms and wearable technology currently consume the personal safety market with personal alarms as the most popular. The devices are available in varying shapes and sizes and are usually activated using a manual button. These products may then function by emitting a noxious odour, a piercing sound or making an emergency call.
A number of mobile based applications exist (BSafe) and feature location tracking or sharing via GPS. These systems can also be combined with other devices and wearables such as smart watches and jewellery (Invisiwear). Some of these app-based systems have additional features such as subscription packages to 24/7 call centres, voice activation, live streaming of journey, audio/video recording when attacked and fake phone calls.
The research also found that defence-based, non-weapon products were available, which work by giving the person a tool to strike back at an attacker effectively. Price points varied between affordable and expensive. Subscription-based features were assumed to be a potential financial burden that could be a barrier to some users.
The team members generated ideas using the Whole Product Game (Gray, 2011) – based on the Whole Product Strategy of Ted Levitt. The purpose was to elaborate on the initial concepts by suggesting functionality for the product, with the aim to provide solutions to the project aim and limitations seen in available products. Ideas were brainstormed onto the collaboration tool Miro board allowing the team to collate the information.
Whole Product Game
PACT Analysis
To initially map potential users, a PACT analysis (Benyon, 2019) was performed. This enabled the team to create an overview of our target group.
Initial User Interviews
Five qualitative interviews were conducted on a one-to-one basis. Prior to the interviews the participants were given a debrief of the interview and singed consent forms. As the topic was of a sensitive nature which could be triggering to some participants, each participant was informed that they were free to discontinue the interview at any point without negative consequence.
Demographic information such as age and occupation were collected. All responses collected were otherwise anonymised. The interviews were conducted in various scenarios, depending on interviewee preference. Some interviews were conducted in a public space and consideration was taken to ensure a calm and undisturbed environment, in order not to compromise privacy. Other interviews were conducted over phone or in person in a private setting.
User Survey
Due to time and geographic constraints, additional data was gathered through a qualitative questionnaire which was shared online via www.SurveyMonkey.com. The total sample size consisted of 29 female, 40 male and 2 non-binary/third gender participants. The purpose of the survey was to reach and gather further data from a wider female audience to gauge user needs and to understand pain points.
As the survey platform limited the number of items in the questionnaire, this impacted the amount of data gathered through comparable questions asked in forementioned qualitative interviews. Consent was obtained via an online consent form which was displayed prior to starting survey.
User Personas
The data analysed form the initial research period and user collected data helped inform the creation of a fictional user persona aimed to address the main points brought up in research. The persona aimed to address as wide a target audience as possible.
Initial Ideation
Two initial concepts were devised following the early research period and user involvement.
Concept One
Biometric smart lens concept which featured:
- Augmented reality maps and directions
- Biometric sensors
- Notification view
- Automatic alarms activation based on biometric data
- Echolocation to detect unseen persons
- Automatic street light activation upon approach
Concept Two
Biometric sensor concept which featured
- Biometric sensors
- Discreet design
- Biofeedback
- Automatic alarm activation based on biometric data
Customer Journey Map
The data analysed form the initial research period and user collected data helped inform the creation of a fictional user persona aimed to address the main points brought up in research. The persona aimed to address as wide a target audience as possible.
Data Analysis
Key Takeaways from Interviews and Survey:
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Walking makes up >40% of the participants’ mode of travel
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For fun, participants predominantly report activities that are away from their home
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At night, participants predominantly report activities that are done at home
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Participants’ technology use is dominated by mobile phone and laptop
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Our participants are aware of the safety (or not) of their own community but less aware of any local initiatives available to support on that.
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Participants are using strategies to increase safety and the dominant strategy is avoidance. They find alternatives to walking.
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Experiences of walking alone after dark were overwhelmingly reported as negative, with the most reported experience being a concern of being followed or approached.
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Participants report facing challenges when walking alone, the most reported being a fear of what other people might do.
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Precautions are being taken by participants when they go out alone and although none dominate on their own it is noted that one of the top listed was having a physical weapon available.
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Reasons participants gave for precautions were mostly negative. Positive-looking response (preparedness and independence) accounted for a minority of responses.
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All participants responded that they feel there is something missing to increase their safety when walking alone.
Customer Service Blueprint
A general service blueprint was created as an overview to Emmas journey to show the front and back-end systems working whilst the service is in use.
Testing
Focus Group
a focus group with three participants, identified as target users, was held to discuss the topic of women’s personal safety, and how user pain points could be addressed with a specialised service with focus on a biometric device. Further aims were to get user feedback on existing prototypes and to inform future iterations.
Part One - Card Sorting
Card sorting is a quantitative research method widely used to explore user's mental models (Benyon, 2019). The aim of the card sorting task was to explore the participants pre-existing associations relating to women’s personal safety, and how this could be addressed in an application.
Cards consisted of words relating to women’s safety and different elements and features which could be attributed to the biometric lens, mobile application, or biometric sensor. The participants were encouraged to sort the cards in a manner which made sense to them and the order they would see features used As no previous information on the product was provided the group participants relied on their own interpretation of the cards.
Part Two - Cognitive Walkthrough
Due to the abstract and speculative nature of the three prototypes generated from initial research participants were given a cognitive walkthrough of each low-fidelity prototype which had been generated prior to the session and asked to provide feedback.
Part Three - Gem Stickers
As an integrated application to enhance the comprehension of how a sensor might be used, participants were provided gemstone stickers which they were then asked to place somewhere on their body they felt would be suitable for a biometric sensor.
Participants felt the wrist area would be most practical for the context of use. However, there were also several concerns that came up while conversing about the card sourcing, particularly one individual repeatedly expressed concern about accuracy of biometric data of people that are considered anxious at baseline and who may frequently experience symptoms of flight or right.
Other concerns were related to or individuals who have irregular heartbeats or have higher perspiration than others.
Part Three - Measurement Scales
At the end of the session, participants were asked to complete “Measurement Scales for perceived usefulness and perceived ease of use” to gauge in a quantifiable manner how useful each prototype was perceived.
There was a unanimous negative response towards the lens. All participants responded that they were highly unlikely to use the lens in both the Perceived Usefulness and the Perceived Ease of use responses. The Sensor was overall positive. The like Perceived Usefulness was extremely high, and the Perceived Ease of Use was quite likely.
Design
MoodBoard
Mood boards are often used in design and development to collate ideas and enrich brand understanding, ensuring knowledge of purpose and direction. The board also acts as the first preposition towards creating a cohesive style guide. In the production of FemLens, several mood boards were created by the team. Keen to focus on key principles of colour psychology, the mood boards aimed to approach the application with a sense of warmth and calm whilst relating the user to the outdoor environment.
Wireframe Sketches
After the first round of user testing (focus group), the lens was removed from the ideation. New low fidelity wireframe sketches were completed to reflect this change with additional input from the user research also added into or changed in the sketches.
Low Fidelity Digital Wireframes
After the first round of user testing (focus group), the lens was removed from the ideation. New low fidelity wireframe sketches were completed to reflect this change with additional input from the user research also added into or changed in the sketches.
Digital wireframes were also developed for the biometric sensor to help users gain a clearer understanding of how it works in the next round of testing.
Mid Fidelity Digital Wireframes
Once the team were happy with initial designs and ideas, mid-Fidelity wireframes were produced from the low-fidelity frames to continue onto user testing. The frames were developed to be as encompassing of a user’s experience in the app as possible aiming to show the onboarding elements and key in app, and out, features.
Testing Two
Wireframe Testing Session
User testing for FemLens was conducted in two parts. The first section invited users to view the low-fidelity prototypes of the mobile application and complete set tasks noting whether or not they could do them. The results show that most of the tasks were effectively received by most users.
Interestingly, the alarm was located and ‘used’ by all the participants, however, less than half the users found the alarm to be efficient. Participants struggled most knowing how to access and add contacts and share their location whilst using the app.
Users were invited to provide comments regarding their experience with the wireframe application.
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The participants did not see the alarm feature as being appropriately placed, as they were worried about accidently using it, and questioned why it was there when the jewellery sensor served a similar function.
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The users were concerned over the distribution of some of the elements, worrying they would accidently touch the wrong button.
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Users wanted to see what the safest route were as well as distance
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Users wanted a ‘help’ feature to explain elements in case they are unsure
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Users mentioned adding safety features for access and not showing a name
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The AI interactions are unclear
The second part of the testing introduce A/B testing to the participants. They were asked to look at the different positioning, sizing and introduction of multiple elements and asked which they preferred.
The results (Figure 176) show that the participants preferred B in four out of five of the tests. Almost all the users selected A in preference for the lock screen display. Users were asked if they had any further comments. The users:
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Did not like the alarm being on the lock screen
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Did not want to see police presence in case it made them worry more, would rather see busy streets or free phones
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Wanted to see more of the map on the lock screen, others did not want it.
Design Two
High Fidelity Prototype
Following prototype testing, the mid-fidelity wireframes were taken to a higher fidelity to reflect the analysis post testing. Changes made are as follows:
● Buttons were more spaced out
● Name removed from everywhere but the preferences screen at Sign Up
● The alarm has been removed entirely
● Contacts now has its own screen
● Base of screen navigation added for calling the AI assistant and sharing location
● Call, share and message screens designed to show AI interaction
● Help screen added to the home screen accessed via the ‘?’
The screens were further developed by the team after an initial iteration where colours and placements were analysed and discussed. The final hi-fidelity prototypes show the onboarding, map, location, assistant and activation screens. The screens were then evaluated by users and experts.
High Fidelity Prototype - Sensor
High fidelity prototypes for the sensor were also designed using 3D rendering software and creative cloud. The sensor can be attached to jewellery and reads a users biometric data feeding it back to the mobile application AI system for analysis.
Evaluation
User Evaluation
As a finalising stage the prototype underwent user evaluation. The users were comprised of seven participants from the target group. Based on the high-fidelity prototype, the panel was asked to offer an evaluation by completing a combination of AttrakDiff, Systems usability scale (SUS) and heuristic evaluation. The combined evaluation tools were selected for their unique perspectives of the usability of the FemLens application.
Heuristic Evaluation
The heuristics questionnaire focused on various aspects of usability, comprehension, logic, and ease of use of both the mobile application and the biometric sensor. Participants were asked to indicate with ‘Yes’, ‘No’ or ‘N/A’ if the high-fidelity prototype had met the heuristic criteria for each item. The 40 questions were designed using the ‘ten qualitative principles’ (Schlecht, 2021), however, the group placed predominant focus on:
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Match between system and the real world.
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User control and freedom
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Flexibility and efficiency of use
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Aesthetic and minimalist design
The heuristic results indicated certain issues with the functionality of the application. The participants felt:
●they needed more autonomy over cancellation of elements
●said they wanted to access the help instructions earlier on in the sign up process
●Did not like the wavy blue boxes for the call and location send buttons
●Issues with some unintuitive buttons, contrast of colours and font size
●often lost in the application architecture
●confused over how they would know the sensor was connected to the app
AttrakDiff Evaluation
AttrakDiff is a questionnaire aims to evaluate how attractive the product is experienced in relation to usability, appearance, and areas of optimisation (UID, 2008). The AttrakDiff questionnaire was shared via an online link to participants which connected them to the official test platform. The results from the AttrakDiff (Appendix 5.2) revealed a comprehensive profile of the user’s perception of the design.
The confidence rectangle representing hedonic (HQ) and pragmatic quality (PQ) fell to a lesser extent within the desired boundary. The portfolio representation moreover indicated that the design was mostly self-oriented or neutral, and to a lesser extent task oriented. Therefore, this may suggest that optimisation of the design is needed for the application to score withing the desired boundary.
Results from the word pairs analysis display the mean values of each item and mean scores can be observed between one and two on majority of items.
Other factors, such as in the PQ domain indicating how predictable the application was perceived, scored below zero. Similarly, another PQ item indicating the degree which the application was confusing or structured, also received a mean score close to zero.
Within the HQ-S domain it can furthermore be seen that the application scored a mean value of zero on perceptions of undemanding versus challenging. Low scores on latter items may be attributed to the operational aspects of the application, which specifically could be an area of focus for future improvement. Overall, there were no polarising perceptions.
System Usability Scale
SUS is widely used to evaluate digital applications. As a validated measuring tool, SUS offers a way to evaluate usability. The questionnaire has an average cut off point for measuring usability at 68, and a score below this number is suggested to indicate insufficient usability. SUS results indicated that FemLens had areas to improve in terms of usability.
Expert Evaluation
During the final stage of the project, FemLens was presented to a group of design students and experts for a final stage of expert analysis and was an opportunity to gather further evaluation data.
Feedback was given verbally and written. The peer-review highlighted the following feedback, regarding testing focus and iterations:
- Continuous use of the design could have negative long-term impact on users, such as diminishing their perception of their personal safety and making them more anxious.
- Experts felt overall that the application could be relevant for real life applications and product pitch received high scores.
- Application could be made gender-neutral
Applicable iterations were implemented, i.e., the police spots were removed, and the users are only given safe routes and don’t see unsafe routes, however, due to time constraints some recommendations may be considered in future development of the product which were beyond the scope of the present project.
Live Prototype
To access the live prototype developed using Figma and Azure please visit this link https://nuuzn4.axshare.com