@article{turner_hayes_2019, 
  title={The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning}, 
  volume={66}, 
  DOI={10.1109/tbme.2019.2900863}, 
  abstractNote={This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom-by-symptom basis, irrespective of other neuromuscular movement disorders the patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders.}, 
  number={11}, 
  publisher={Institute of Electrical and Electronics Engineers (IEEE)}, 
  author={Turner and Hayes}, 
  year={2019}, 
  month={Feb}
  }