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Smart cyber-physical systems for assistive technology at home for the elderly

   Department of Electronic & Electrical Engineering

About the Project

The Department of Electronic and Electrical Engineering at the University of Bath is inviting applications for the following project.


Cyber-physical systems (CPS) is the emerging systems of intelligence that seeks to exploit the combination of sensor elements, embedded and wearable sensors systems, computing, artificial intelligence, robotics, networking and actuation to monitor and control physical systems [1]. These features can make CPS capable of autonomously functioning based on the monitoring and interaction with their physical surrounding. This means that CPS have great potential for the development of intelligent devices capable of safely, reliably and efficiently assisting individual at home and to perform activities of daily living [2,3,4].

This project will investigate and develop novel and robust CPS architectures for smart, safe, efficient, and timely assistive technology at home for the elderly. The aspects that will be investigated for these CPS include the following:

  • Optimal sensing modalities located at home and worn by the user.
  • Machine learning methods for activity recognition using multimodal data.
  • Wearable and comfortable sensor arrays for biometric sleep detection and fall detection.
  • Smart control of appliances for safe and optimised usage at home by the elderly.
  • Real-time data systems for monitoring, IoT and fast response in emergency events.

The outcomes from this research project will contribute to deliver smart healthcare systems that allow the elderly to stay healthy and independent at home, make the elderly feeling safe at home and recovering their quality of life. This work will contribute to the development of smarter control system at home based on the current state of the user. Furthermore, this research will also contribute and open research opportunities to transform how care and assistance is delivered currently.

The PhD student will have access to the facilities of the Multimodal Interaction and Robotic Active Perception lab (Inte-R-action), the Centre for Autonomous Robotics (CENTAUR) and the Centre for Bioelectronics, Biosensors and Biodevices (C3Bio).

Candidate Requirements

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous.

Non-UK applicants must meet our English language entry requirement.

Enquiries and Applications

Informal enquiries are welcomed and should be directed to Dr Uriel Martinez-Hernandez -

Formal applications should be made via the University of Bath’s online application form for a PhD in Electronic and Electrical Engineering.

When completing the form, please identify your application as being for the URSA studentship competition in Section 3 Finance (question 2) and quote the project title and lead supervisor’s name in the ‘Your research interests’ section. 

More information about applying for a PhD at Bath may be found on our website.

Equality, Diversity and Inclusion

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.


Artificial Intelligence; Chemical Engineering; Computer Vision; Control Systems; Cybernetics; Electronic Engineering; Integrated Engineering; Machine Learning; Mechatronics; Robotics


[1] Haque, S. A., Aziz, S. M., & Rahman, M. (2014). Review of cyber-physical system in healthcare. international journal of distributed sensor networks, 10(4), 217415.
[2] Dimitrov, V., Jagtap, V., Wills, M., Skorinko, J., & Padır, T. (2015, July). A cyber physical system testbed for assistive robotics technologies in the home. In 2015 International Conference on Advanced Robotics (ICAR) (pp. 323-328). IEEE.
[3] Martinez-Hernandez, U., & Dehghani-Sanij, A. A. (2018). Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors. Neural Networks, 102, 107-119.
[4] Martinez-Hernandez, U., & Dehghani-Sanij, A. A. (2019). Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. Pattern Recognition Letters, 118, 32-41.

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