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Royal Society Research Fellowship – Human activity recognition

  • Manchester Metropolitan University
  • Massey University

Project: Research

Project Details

Description

A prestigious international research fellowship supported by the Royal Society, UK, dedicated to designing foundational, high-reliability sensor alignment algorithms to track human kinetic activities within complex environments.

Key findings

Successfully developed novel foundational methodologies in sensor fusion utilizing depth sensors and sensory device networks. The resulting algorithm layouts drastically enhanced ambient data tracking reliability and were subsequently integrated into global Ambient Assisted Living (AAL) and remote health monitoring frameworks.

Layman's description

Supported by the UK's Royal Society, this project created the math and software needed to make smart home sensors much more accurate. By combining specialized depth cameras with regular smart sensors, the system can reliably map out how a person is moving around their home, paving the way for better remote monitoring systems for independent living.
Short titleHuman Activity Recognition
AcronymRoyal Society Fellowship
StatusFinished
Effective start/end date01/01/2015 → 31/12/2017

Collaborative partners

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Sensor Fusion, Depth Sensors, Human Activity Recognition, Ambient Assisted Living (AAL), Spatial Analytics, Smart Environments