Fully funded PhD Scholarship - Machine learning and wearable activity recognition

Wearable motion sensor data can be interpreted with AI and machine learning techniques to infer human activities and provide contextual assistance, for instance in a wearable fitness coach or for industrial or elderly assistance. Generally, a large amount of data must be available to train machine learning models to successfully recognise human activities.

Within this project, you will seek to make it easier to "teach" wearable systems to recognise a wider range of human activities. You will research advanced machine learning and AI techniques to recognise an "open-ended" set of activities from wearable sensors and reduce the effort associated with acquiring training data. Depending on your interests, different approaches can be followed: crowd-sourcing data acquisition, developing new interactive machine learning approaches, or you might exploit the growing availability of multimedia datasets (e.g. Google AVA dataset, Youtube data, the Sussex-Huawei Locomotion dataset, and other) which can be reused and translated across modalities or exploited using recent deep learning techniques.

== Qualifications ==

The ideal candidates will have a master’s degree in computer science, computer engineering, physics, mathematics, electrical engineering, or equivalent, with prior experience in machine learning and/or embedded and wearable systems.

The ideal candidates will have a passion to contribute to the development of novel wearables which can improve quality of life. They will have outstanding technical skills and a strong interest in research at the crossroads of signal processing, machine learning, embedded systems, sensor technologies and their applications.

Applicants should be committed to pursue leading research and publish results in top venues. Additionally, we expect mastery of written and spoken English, self-motivation, an inquiring mind, be able to work independently and in an interdisciplinary environment.

== About the Lab ==

The Wearable Technologies Lab, led by Dr. Daniel Roggen, has been established in 2014. Since then, it has acquired funding from Google, Huawei, EPSRC, the Austrian FFG, etc.

The focus of our lab is to advance AI techniques to automatically recognise and understand human activities, gestures or daily routines from wearable and mobile sensors. We have developed several wearable sensing platforms and software frameworks for this, including deep learning and ASIC-friendly approaches.

The lab has created numerous dataset for activity recognition research, the most recent is a massive transportation dataset: the Sussex-Huawei Locomotion dataset (www.shl-dataset.org).

Some of our applications are in the fields of sports performance, industrial assistance, mobility monitoring, crowd behaviour analytics and healthcare.

The members of the lab have an international outlook, with a mix of computer scientists, computer engineers, and electronic engineers.

The lab has state of the art computing and electronics facilities with a wide range of technologies at hand: augmented reality glasses, smartwatches, a vast array of datasets and ad-hoc software tools to support research, numerous novel sensor technologies and sensing platforms, etc.

== How to apply ==
Contact Dr. Daniel Roggen ([Email Address Removed]) with your CV to discuss your application.

Applications are handled online via the University of Sussex Online Application System.

More informations about the Wearable Technologies Lab and ongoing research:
http://www.sussex.ac.uk/strc/research/wearable
http://scholar.google.co.uk/citations?user=JGjtLtYAAAAJ
http://www.shl-dataset.org