Questioning Classic Patient Classification Techniques in Gait Rehabilitation: Insights from Wearable Haptic Technology

Georgiou, Theodoros; Holland, Simon; van der Linden, Janet and Donaldson, Glenis (2016). Questioning Classic Patient Classification Techniques in Gait Rehabilitation: Insights from Wearable Haptic Technology. In: eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Giokas, Kostas; Bokor, Laszlo and Hopfgartner, Frank eds.), Springer, Cham, 181 pp. 327–339.

DOI: https://doi.org/10.1007/978-3-319-49655-9_40

Abstract

Classifying stroke survivors based on their walking abilities is an important part of the gait rehabilitation process. It can act as powerful indicator of function and prognosis in both the early days after a stroke and long after a survivor receives rehabilitation. This classification often relies solely on walking speed; a quick and easy measure, with only a stopwatch needed. However, walking speed may not be the most accurate way of judging individual’s walking ability. Advances in technology mean we are now in a position where ubiquitous and wearable technologies can be used to elicit much richer measures to characterise gait. In this paper we present a case study from one of our studies, where within a homogenous group of stroke survivors (based on walking speed classification) important differences in individual results and the way they responded to rhythmic haptic cueing were identified during the piloting of a novel gait rehabilitation technique.

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