Fade

Fade

fall detector

Fade is a mobile app which is capable of detecting and emitting an alarm signal when the user falls. Once Fade detects a possible fall it will emit an alarm signal and wait for the user to verify that he is fine. If not the app will automatically notify the emergency contact established by the user beforehand.

Problem statement

The Fade project was initiated back in 2012 and early research provided the following two main insights:

  1. Although smartphones were already housed with sensors such as GPS, accelerometers, gyroscopes, magnetometers, etc, not many fall detection apps were widely available in the market or provided a reliable algorithm.
  2. The algorithms were only triggered when one type of fall was detected.
UX methodologies
  • Desk research and benchmarking
  • Affinity diagrams
  • User interviews
  • User personas, scenarios and journeys
  • Information architecture
  • Tree testing
  • Prototyping
  • Heuristic evaluation
  • Usability testing
What is Fade?

A fall detection app aimed at sending a notification and localization statistics to a predefined emergency contact when the user falls, thanks to its proprietary mathematical algorithm.

Once a fall is detected, it triggers an alarm requesting the user to confirm if he’s alright. If no interaction takes place, the app understands that further assistance is required and alerts the emergency contact via SMS, phone call, e-mail or push notifications (e.g. WhatsApp). The exact location of the user is then obtained from the GPS sensor or by mobile mast triangulation.

Objectives
  • Introduce to the market a seamless and non-intrusive app which continuously monitors the user in search for any possible fall.
  • Design a solution where the user clearly understands when the algorithm is running and provides them with a clear alarm screen which immediately notifies their emergency contact if no user interaction takes place.
  • Take into account any type of fall, ranging from a low to high sensitivity depending on the activity the user is undertaking.
Testing

A great part of the project timeline was spent testing the algorithm. Its main complexity resided in the need of activating the alarm regardless of the activity being carried out by the user: it’s not the same to trip over when strolling than a heavier fall at a certain and constant speed (e.g. riding a motorbike).

Simulating a real fall might seem effortless, but it’s not and must be done by professionals. This complicated the testing phase, since this stage is normally based on simply performing usability tests with real users. The help of Kung Fu Master Saha C. Afonso was essential during this stage.

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