Nigel H. Lovell

TL;DR: Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.

TL;DR: An integrated approach is described in which a single, waist-mounted accelerometry system is used to monitor a range of different parameters of human movement in an unsupervised setting.

TL;DR: The importance of tactile sensor technology was recognized in the 1980s, along with a realization of the importance of computers and robotics, despite this awareness, tactile sensors failed to be strongly adopted in industrial or consumer markets as discussed by the authors.

TL;DR: Metal electrode materials used in active implantable devices are often associated with poor long-term stimulation and recording performance and modification of these materials with conducting polymer coatings has been suggested as an approach for improving the neural tissue-electrode interface and increasing the effective lifetime of these implants.

TL;DR: A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced and a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer is developed.