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Determining human movements from multiple wearable sensors

About This PhD Project

Project Description

Movement analysis and recording has been largely confined to dedicate gait and motion capture facilities, but has proved useful in medical and clinical practice for quantifying the effects of treatments relating to movement disorders. However in medical conditions such as Parkinson’s disease and stroke, there may be key phenomena that would be difficult to collect in a gait lab such as the movements just prior to a fall or a near fall, or the movements of a stroke affected limb in daily use. The University of Reading now has access to data from long term measurements of movements. These data consist of acceleration and gyroscope data (inertial measurements) that have been collected from individuals in their home environments over single day periods. The data represents typical activities of people with conditions such as Parkinson’s disease and stroke.

This studentship will explore this data with the intention of combining biomechanics and data mining techniques to identify key features of movement. The goal will be to build dynamic models of human activities such as walking, sitting, standing as well as transitions such as stand-to-sit. These models will then provide a reference for filtering inertial measurements so as to identify activities, postures and actions. Both bespoke and standard algorithms for robust hierarchical classification of postures, activities and actions will be explored.


Villeneuve, E., Harwin, W., Holderbaum, W., Janko, B. and Sherratt, R. S. (2017) Reconstruction of angular kinematics from wrist-worn inertial sensor data for smart home healthcare. IEEE Access, 5. pp. 2351-2363. doi: 10.1109/ACCESS.2016.2640559

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