Witchel HJ, Needham R, Healy A, Guppy JH, Bush J, Oberndorfer C, Herberz C, Westling CE, Kim D, Roggen D, Barth J, Eskofier B, Rashid W, Chockalingam N, Klucken J (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Association for Computing Machinery
Book Volume: Part F131193
Pages Range: 150-157
Conference Proceedings Title: ACM International Conference Proceeding Series
ISBN: 9781450352567
The dual task paradigm (DTP), where performance of a walking task co-occurs with a cognitive task to assess performance decrement, has been controversially mooted as a more suitable task to test safety from falls in outdoor and urban environments than simple walking in a hospital corridor. There are a variety of different cognitive tasks that have been used in the DTP, and we wanted to assess the use of a secondary task that requires mental tracking (the alternate letter alphabet task) against a more automatic working memory task (counting backward by ones). In this study we validated the x-io x-IMU wearable inertial sensors, used them to record healthy walking, and then used dynamic time warping to assess the elements of the gait cycle. In the timed 25 foot walk (T25FW) the alternate letter alphabet task lengthened the stride time significantly compared to ordinary walking, while counting backward did not. We conclude that adding a mental tracking task in a DTP will elicit performance decrement in healthy volunteers.
APA:
Witchel, H.J., Needham, R., Healy, A., Guppy, J.H., Bush, J., Oberndorfer, C.,... Klucken, J. (2017). Using wearable inertial sensors to compare different versions of the dual task paradigm during walking. In ACM International Conference Proceeding Series (pp. 150-157). Umea, SE: Association for Computing Machinery.
MLA:
Witchel, Harry J., et al. "Using wearable inertial sensors to compare different versions of the dual task paradigm during walking." Proceedings of the 35th Annual Conference of the European Association of Cognitive Ergonomics, ECCE 2017, Umea Association for Computing Machinery, 2017. 150-157.
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