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Internet of You

Loc2Vec: Learning location embeddings with triplet-loss networks

Introduction At Sentiance, we developed a platform that takes in smartphone sensor data such as accelerometer, gyroscope and location information, and extracts behavioral insights. Our AI platform learns about the user’s patterns and is able to predict and explain why and when things happen, allowing our customers to coach their users and engage with them…

  • By Vincent Spruyt
  • May 3, 2018

Learning meaningful location embeddings from unlabeled visits

Introduction At Sentiance, we developed an AI platform that learns to detect and predict a person's behavior, routines and profile from mobile sensor data such as accelerometer, gyroscope and GPS. Many low level algorithms, such as our transport mode detector, are trained by supervised learning techniques for which we gathered labeled data by closely collaborating...
  • By Kevin Mets
  • January 29, 2018

Large scale transport mode classification from mobile sensor data

Abstract   Transport mode detection based on mobile phone sensor data is a subdomain of activity detection. State-of-the-art results in literature are usually obtained on limited datasets with few human participants. Approaches used often require ideal situations where sensor data is always available or phones are expected to be in a specific position. In this...
  • By Stavrianos S.
  • December 4, 2017

Applying deep learning to distinguish drivers from passengers using sensor data

Sentiance provides a cutting-edge driving behaviour scoring technology. Based on sensor data collected from the user’s mobile phone we compute scores characterising smooth driving, anticipative behavior, compliance with speed limits and focused driving. However, not every car trip should be aggregated into the user profile. Being a passenger of a reckless taxi driver shouldn’t penalise your...
  • By Bertrand Fontaine
  • September 25, 2017

What does it take to be a data scientist at Sentiance?

Our VP Chief Data Scientist, Vincent Spruyt is heading up the application of machine learning methods for context modeling and behavioral profiling based on mobile sensor data. With his background in computer vision and machine learning, he laid the foundations of several AI solutions that quickly made Sentiance one of the strongest competitors in the...
  • By Branka M
  • June 30, 2017

Predictive superpowers: Applying deep learning on mobile sensor data to predict human behavior

At Sentiance we believe that smart devices, applications and the Internet of Things should work on your behalf, conform to your desires and preempt your needs. We call it the Internet of You. We are building the intelligence and contextual engine to fuel the Internet of You, by analyzing sensor data to recognize behavioral patterns…

  • By Ashish Jha
  • April 25, 2017
contextual intelligence, machine learning as a service, predictive analytics solutions, IOT sensor data

How AI drives the mobile contextual revolution

Introduction In “Predictions 2016: The Mobile Revolution Accelerates”, Forrester forecasted that by the end of 2016 more than 25% of companies will consider mobile not as a channel, but as a fully integrated part of their overall strategy. Moreover, the research firm expects customer-focused companies to take personalization to the next level by extracting relevant…

  • By Vincent Spruyt
  • May 31, 2016

Driving behavior modeling using smart phone sensor data

Introduction The car insurance industry is rapidly transitioning from traditional fixed fee insurance packages to usage based insurance (UBI). With recent car insurance cost surges, insurance companies are starting to realize that the majority of well behaved customers shouldn’t have to pay for the excesses of the few. Traditional UBI approaches such as Pay As You…

  • By Vincent Spruyt
  • February 11, 2016

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