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. 2014 Mar 28;14(4):6229-46.
doi: 10.3390/s140406229.

A wearable system for gait training in subjects with Parkinson's disease

Filippo Casamassima  1 , Alberto Ferrari  2 , Bojan Milosevic  3 , Pieter Ginis  4 , Elisabetta Farella  5 , Laura Rocchi  6
Affiliations

A wearable system for gait training in subjects with Parkinson's disease

Filippo Casamassima et al. Sensors (Basel). .

Abstract

In this paper, a system for gait training and rehabilitation for Parkinson's disease (PD) patients in a daily life setting is presented. It is based on a wearable architecture aimed at the provision of real-time auditory feedback. Recent studies have, in fact, shown that PD patients can receive benefit from a motor therapy based on auditory cueing and feedback, as happens in traditional rehabilitation contexts with verbal instructions given by clinical operators. To this extent, a system based on a wireless body sensor network and a smartphone has been developed. The system enables real-time extraction of gait spatio-temporal features and their comparison with a patient's reference walking parameters captured in the lab under clinical operator supervision. Feedback is returned to the user in form of vocal messages, encouraging the user to keep her/his walking behavior or to correct it. This paper describes the overall concept, the proposed usage scenario and the parameters estimated for the gait analysis. It also presents, in detail, the hardware-software architecture of the system and the evaluation of system reliability by testing it on a few subjects.

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Figures

Figure 1.
Figure 1.
Scheme of the scenario for the use and the components of the system.
Figure 2.
Figure 2.
The EXLs1 sensing unit.
Figure 3.
Figure 3.
Energy consumption evaluations of the node's overall energy consumption (per sample).
Figure 4.
Figure 4.
(a) Comparison of orientation estimation as computed by Xsens MTw (red) and EXLs1 (blue) sensor nodes using the Kalman filter. (b) Zoom of the roll plot.
Figure 5.
Figure 5.
Angular velocity and the detected initial contact (IC) and foot-off (FO) events.
Figure 6.
Figure 6.
Bland–Altman plots for percentage differences for (a) the step duration and (b) the step length estimated with inertial measurement units (IMUs) and GAITRite (GR).
Figure 7.
Figure 7.
Block diagram of the audio feedback application.
See this image and copyright information in PMC

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