Sensing and Robotic technology for Ambient Assisted Living

Develop and evaluate smart robotic environments for ambient assisted living applications
Description of the Project: 

Caring for older people, particularly those with disabilities, places a significant strain on the economy. Robots and autonomous systems, combined with AI, sensing infrastructures and smart home technology can assist professional carers to be more efficient, allowing them to focus more on the human-to-human aspects of their work. ICT and Robotic systems are currently being developed to provide general assistance around the home, for instance, performing household chores and assuring safety, but they also have the potential to reduce social isolation, support cognitive stimulation and rehabilitation practices, and even assist directly with physical aspects of care including help with personal mobility, eating and drinking, dressing, and toileting [1].

I am interested in supervising projects focusing on the use of (social) robotic technologies as part of Ambient Assisted Living systems [2][3], such as:

  • Human-robot interaction studies to address acceptance and usability concerns that currently hinder the take-up and use of care robotic technology as part of social care practices in end-user environments [4]
  • Ambient Intelligence (AmI) approaches to embed robotic technology in everyday environments, for instance, as social artefacts  providing companionship and cognitive stimulation to people with Alzheimer or Dementia.
  • Sensor-driven human-activity recognition (HAR) [5], especially using device-free techniques (e.g. based on RFID technology) for non-invasive monitoring of activities of daily living supporting novel assessment methods and the  detection of deviations from healthy ageing before people’s abilities to live and function independently is hampered by mental health illnesses.
  • Investigating the role of creativity in human-robot interaction, for instance, by developing end-user programming tools and programming by demonstration techniques to let end-users train systems in specific tasks (such as helping with daily activities, cleaning, fetching objects, etc..).


These projects will avail of a new state-of-the-art laboratory situated in the Heriot-Watt wing of the Lyell Centre[1]. The laboratory is part of a network of international “home lab” test-bed facilities that includes similar facilities in Bristol, Sheffield and Pisa. The laboratory at Heriot-Watt University is a 60 square meters, fully sensorised smart robotic space designed to resemble a typical single level home comprising an open-plan living, dining and kitchen area and a bathroom and bedroom. The apartment is equipped with state of the art smart home technology, and hosts a number of domestic robots (PAL Robotics Tiago, Softbank Robotics Pepper, Double tele-presence robot).


Ongoing collaborations exists on all the topics above with psychology research groups at Heriot-Watt University, University of Stirling, University of West of Scotland and University of Strathclyde.



Project number: 
First Supervisor: 
Heriot-Watt University
First supervisor university: 
Heriot-Watt University
Essential skills and knowledge: 
Strong programming skills; interest in multidisciplinary work; ability to work independently
Desirable skills and knowledge: 
Knowledge of robotics and human-robot interaction / Artificial Intelligence, Machine Learning.

[1]  Tony Prescott, Praminda Caleb-Solly, Robotics in Social Care: A Connected Care EcoSystem for Independent Living, UK RAS 

White Paper, HWU contributors: Oliver Lemon, Mauro Dragone. Online at, Accessed  17/2/2018

[2] M. Dragone, et. al, A cognitive robotic ecology approach to self-configuring and evolving AAL systems. Eng. Appl. Artif. Intell. 45, C (October 2015), 269-280.

[3] M. Dragone, J Saunders, K Dautenhahn, On the integration of adaptive and interactive robotic smart spaces, Paladyn, Journal of Behavioral Robotics, 2015

[4] Edinburgh Center for Robotics:

[5] G. Amato, D. Bacciu, S. Chessa, Mauro Dragone, et. al, 
A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living. ISAmI 2016: 1-9