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Location-based Ambient Intelligence

This analysis aims to cover the emerging area of ambient intelligence, a much-used term that will look to bring together many of today’s major technology innovations, such as Internet of Things (IoT), big data, connected home, wearable sensors, and smartphones. It combines advances in always-on location, voice and image recognition, augmented reality, machine learning, artificial intelligence (AI), sensor fusion, and data analytics. The result over the next 5 years will be major advances in applications such as local search, social networking, retail, advertising, and health / fitness.

This analysis considers the role that always-on, ubiquitous location will have in enabling these major changes, where the basic concept of ambient intelligence is defined, along with the current and future location technologies that will be facilitators. The analysis also looks at how major companies are now aligning their resources to meet this future market, outlining potential advantages, gaps, acquisition targets, and partnerships. Finally, it looks at the key verticals that will drive the initial adoption of ambient intelligence, with forecasts for the technologies, devices, and applications required for location-based ambient intelligence in each market.

  • 1. INTRODUCTION
    • 1.1. Introduction
    • 1.2. What Is Ambient Intelligence?
  • 2. THE THREE STAGES OF AMBIENT INTELLIGENCE
    • 2.1. Stage 1: Sensing (Perceiving the User and the World He or She Inhabits)
      • 2.1.1. Big Data, IoT, and Ambient Intelligence: What's the Difference?
    • 2.2. Location Technology in Sensing
      • 2.2.1. Hybridization
      • 2.2.2. BLE Is Not the Only Answer
    • 2.3. Stage 2: Reasoning (Context Awareness, or the Ability to Reason about Ongoing Situatio
      • 2.3.1. User Modeling
      • 2.3.2. Activity Prediction and Recognition
      • 2.3.3. Decision Making
    • 2.4. Location Technologies in Reasoning
      • 2.4.1. Indoor: IoT / IoE Location Technologies
      • 2.4.2. Outdoor and Social: LTE-Direct
    • 2.5. Stage Three: Acting (Context Sensitive, or the Ability to Behave Appropriately)
      • 2.5.1. Siloed Data
      • 2.5.2. Empowering Consumers to Sell Their Data
  • 3. COMPETITIVE ENVIRONMENT
    • 3.1. What Is Needed to Be a Stakeholder in Ambient Intelligence?
    • 3.2. Apple
    • 3.3. IBM
    • 3.4. MetaMind
    • 3.5. Clarifai
    • 3.6. Baidu
    • 3.7. Microsoft
    • 3.8. Facebook
    • 3.9. Google
  • 4. MARKET FORECASTS
    • 4.1. Smartphones
      • 4.1.1. UI: Speech, Facial, and Gesture Recognition
      • 4.1.2. Sensors and Cameras: Augmented Reality, Indoor Location, and Crowdsourced Data
      • 4.1.3. Personal Assistant
      • 4.1.4. Augmented Reality
      • 4.1.5. Social
    • 4.2. Wearables
      • 4.2.1. Wearable Device Forecasts
      • 4.2.2. Quantified Self / Fitness Application Forecasts
    • 4.3. mHealth / Home Monitoring
      • 4.3.1. Health / Home Monitoring Device Forecasts
      • 4.3.2. Smartphone Application Forecasts
    • 4.4. Smart Home
      • 4.4.1. Smart Home Device Forecasts

Tables

  1. Smartphone Technology / Feature Attach Rate, World Market, Forecast: 2013 to 2019
  2. Ambient Intelligence Smartphone Applications, World Market, Forecast: 2013 to 2020
  3. Connected Fitness, Wellness and Wearable Device Shipments by Type, World Market, Forecast: 2013 to 2019
  4. Connected Wearable mHealth Device Shipments by Application, World Market, Forecast: 2013 to 2019
  5. Connected Smart Home Device Shipments by Product Type, World Market, Forecast: 2013 to 2019
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Research Information

Price
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Publish Date
1Q 2015
Code
AN-1721
Research Type
Application Analysis Report
Pages
19