SWAY
SUMMER 2018

cultivating a growing community garden wall in Ljubljana, Slovenia

PHOTO CREDIT: UNSPLASH

Design Strategy

Includes research recap, design criteria, and engagement strategies to set the foundation for user-centered design decision-making.

IN THIS SECTION

• User Requirements
• Design Criteria
• AR Criteria
• Engagement Strategy
  

• Educational Strategy
• Usage over Time
• Variable Reward
• Product Criteria

User Requirements

I want to pick the best hammock spot, so I would like to see available options.

I want to feel safe in my hammock, so I would like to see potential hazards.

I want to hang my hammock correctly, so I would like to see how it should be hung.

I want to remember previous spots, so I would like the ability to to view my trip history.

User Goals

• I want to find the perfect trees every time, so I enjoy my time.
• I want to hang my hammock on the first try, so I can relax.
• I want to be aware of any hazards nearby, so I can remain safe.
  

MOTIVATIONS

• Finds hammocking to be relaxing
• Looks forward rocking in hammock
• Wants to get out on more solo trips
• Enjoys hammocking with others
• Likes to be in nature

FRUSTRATIONS

• Hanging a hammock takes experience
• Hard to find a good pair of trees
• Missing hang options
• Not aware of all the hazards nearby
• Frustration with adjustments

PERSONAS

Hammockers
Looking for guidance in where and how to hang their hammock.

Casual Hammockers
Searching for spots quickly if their favorite spot is taken or when they don’t have much free time.

Hammock Campers
Comparing sites for multiple hangs and checking for nearby hazards.

Design Criteria

The experience of finding a hammock spot should not disrupt the experience of being in nature.

Complement the act of hammocking — laid back and functional, with a minimal UI factor. 

• Help the user find the best hammock hang spot
• Help them identify hazards and dead trees
• Assist them in visualizing where to hang a hammock
• Respect the user's environment
• Focus on solutions that are non-invasive
• Limit sounds and notifications
• Work offline

AR Criteria

Design for a first-person perspective with an open discovery viewpoint. Virtual components should be easily and quickly identifiable against variable environmental backdrops. Optimize for the range of potential spots to be limited to a five meter radius. 

• Limited low light technical capabilities
• Design for a first-person perspective
  
• Five meter interaction range

Engagement

Engagement Strategy

Measuring engagement is a cornerstone of experience design, and every unique design problem has unique participation measurement needs. For this project I’m focusing on the types of engagement that augmented reality brings to a user experience: immersive and educational [note]https://ieeexplore.ieee.org/document/6681863/  Section 4.2 Design Strategies[/note].

ENABLE EXPLORATION 

Flows and feedback that are non-linear and encourage further inspection, like encouraging the user to explore natural environments for hammock spots.

• UP SIGNAL: user consistently scans more than one grove per session. 
  
• DOWN SIGNAL: user picks the first spot shown.
  

PROMOTE COLLABORATION 

Content that requires users to exchange ideas, like prompting the user to share information about known hazards and dead trees, and later hammock spots. 

• UP SIGNAL: user scans hazards, and shares consistently.
• DOWN SIGNAL: user scans hazards, but doesn't share to database.
  

ENSURE IMMERSION

Content that allows for users to be engaged at a constant level.

May include showing additional information about the environment to the user, and learning about the types of information users might want to see in a forest context.

Educational Strategy

Education is part of the overall engagement strategy because user research revealed a learning curve around hanging hammocks, a stage of trial and error most new hammockers don't anticipate. This experience-gathering stage mirrors an experiential learning [note]https://books.google.com/books?hl=en&lr=&id=jpbeBQAAQBAJ&oi=fnd&pg=PR7&ots=Vn4VpU0TQh&sig=6j2Bm4ZsGj4dHpz-USZZ5riVFIY#v=onepage&q&f=false[/note] process, as highlighted below:

EXPERIENTAL LEARNING PROCESS

Start with a concrete experience

Basis for observation and reflection

From there to creating theories

Then testing results

Outcome informs new experience

HAMMOCK HANGING PROCESS

Spends enjoyable time in a hammock.

Wants to recreate experience.

Tries different distances and angles.

Hangs hammock with applied theory.

Develops a new model for spatial details.

Contextual Learning

By understanding the user's learning model, we can explore opportunities to improve task success. For example, applying contextual learning concepts [note]https://ieeexplore.ieee.org/document/6681863/  Section 6.3 Contextual Visualization[/note] to an existing model may increase immersion and enjoyment.

CONTEXTUAL LEARNING PROCESS

Relating
Link new concept to familiar one

Experiencing
Explore, discover, invent

Applying
Use concepts in realistic exercises

Cooperating
Chance to share and respond

Transferring
Use new knowledge in different context

HAMMOCK HANGING PROCESS

Virtual hammock in the app connects to real hammock behavior

User sees hang options and is motivated to explore surroundings

User is provided with an interactive guide to assist with hammock hanging

User is asked to share hazardous trees with other hammockers

User experiments with hanging hammocks in non-forest settings

Usage over time

Usage over Time

Plan for these stages as guideposts toward successfully measuring adoption and retention.

STARTS AND FITS

• New user
  
• Explores app multiple times while figuring it out
  
• May try in areas without trees to test functionality
  

THIRD TIME'S A CHARM

• Still a fairly new user
  
• Has trees, hammock, and app in the same place
• Successfully flows through hanging a hammock
  

HELLO OLD FRIEND

• Understands what to expect from the app
  
• Finds it trustworthy enough to keep relying on
  
• Mindshare shifts to ephemeral utility app
  
• May only use the scan feature to “fill in the blanks”

Variable reward

Variable Reward

The nature of the experience produces an unintentional variable reward loop [note]https://en.wikipedia.org/wiki/Reinforcement#Intermittent_reinforcement_schedules[/note], since there’s no way of knowing how many hammock spots are in a location until the user takes the action of scanning there.

The challenge for the UX component is to understand the variable range of spots and better respond to any outcome. At the moment, our parameters in any scan fall between [no matches] and [too many to visualize]. How do we respond when there are no matches for the user? And where there are too many? How do we define “too many”? One of the first tentpole metrics we'll want to gather is number of matches per scan.
 

Product Criteria

Product Criteria

Show you where to hang a hammock by scanning the trees around you and calculating ideal hang spots based on base weight and hammock size.

• Notifies you of any hazards or dead trees nearby.
  
• Overlay strap position onto trees to help adjust your hammock.
• Scans area for hammock spots
• Displays hammock lines
• Displays hang points
• Displays location of tree hazards
• Allows user to select hammock lines
• Allows user to select hang points
• Displays measurement details

System

REQUIREMENTS

• Supports AR
• Clinometer
• Access to camera
• Access to geolocation

CORE FUNCTIONALITY

• Highlights available hang locations
• Shows overlay of a hammock
• Spots potential hazards

User

HANGING A HAMMOCK

• A hammock
• Straps or rope
• Access to trees
• Free time
• Walking / hiking ability

COMMON MATERIALS

• A bag or backpack
• A mobile phone
• Headphones
• Water bottle
• Notebook

EXTENDED

• Ridgeline
• Underquilt

Data

HAMMOCK MODEL

• Length of hammock
• Weight of persons
• Length and quantity of straps
• Tree circumference
• Distance between trees
• Angle of hang

FROM USER

• Length of hammock
• Weight of persons
• Length and quantity of straps

FROM SYSTEM

• Tree circumference
• Distance between trees
• Angle of hang

NEXT

Interactions

explores selection models and feature flows