Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

This would seem to be extremely useful in objectively measuring gait in stroke. Then we could map stroke protocols that solve those gait problems. It will never occur. 

Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

• An NguyenEmail authorView ORCID ID profile,
• Nils Roth,
• Nooshin Haji Ghassemi,
• Julius Hannink,
• Thomas Seel,
• Jochen Klucken,
• Heiko Gassner and
• Bjoern M. Eskofier

Journal of NeuroEngineering and Rehabilitation201916:77

https://doi.org/10.1186/s12984-019-0548-2

©  The Author(s) 2019

• Received: 18 December 2018
• Accepted: 6 June 2019
• Published: 26 June 2019

Abstract

Background

Gait symptoms and balance impairment are characteristic indicators for the progression in Parkinson’s disease (PD). Current gait assessments mostly focus on straight strides with assumed constant velocity, while acceleration/deceleration and turning strides are often ignored. This is either due to the set up of typical clinical assessments or technical limitations in capture volume. Wearable inertial measurement units are a promising and unobtrusive technology to overcome these limitations. Other gait phases such as initiation, termination, transitioning (between straight walking and turning) and turning might be relevant as well for the evaluation of gait and balance impairments in PD.

Method

In a cohort of 119 PD patients, we applied unsupervised algorithms to find different gait clusters which potentially include the clinically relevant information from distinct gait phases in the standardized 4x10 m gait test. To clinically validate our approach, we determined the discriminative power in each gait cluster to classify between impaired and unimpaired PD patients and compared it to baseline (analyzing all straight strides).

Results

As a main result, analyzing only one of the gait clusters constant, non-constant or turning led in each case to a better classification performance in comparison to the baseline (increase of area under the curve (AUC) up to 19% relative to baseline). Furthermore, gait parameters (for turning, constant and non-constant gait) that best predict motor impairment in PD were identified.

Conclusions

We conclude that a more detailed analysis in terms of different gait clusters of standardized gait tests such as the 4x10 m walk may give more insights about the clinically relevant motor impairment in PD patients.