Ambulatory Gait Behavior in Patients With Dementia: A Comparison With Parkinson’s Disease

Abstract

Accelerometry-based gait analysis is a promising approach in obtaining insightful information on the gait characteristics of patients with neurological disorders such as dementia and Parkinson's disease (PD). In order to improve its practical use outside the laboratory or hospital, it is required to design new metrics capable of quantifying ambulatory gait and their extraction procedures from long-term acceleration data. This paper presents a gait analysis method developed for such a purpose. Our system is based on a single trunk-mounted accelerometer and analytical algorithm for the assessment of gait behavior that may be context dependent. The algorithm consists of the detection of gait peaks from acceleration data and the analysis of multimodal patterns in the relationship between gait cycle and vertical gait acceleration. A set of six new measures can be obtained by applying the algorithm to a 24-h motion signal. To examine the performance and utility of our method, we recorded acceleration data from 13 healthy, 26 PD, and 26 mild cognitive impairment or dementia subjects. Each patient group was further classified into two, comprising 13 members each, according to the severity of the disease, and the gait behavior of the five groups was compared. We found that the normal, PD, and MCI/dementia groups show characteristic walking patterns which can be distinguished from one another by the developed gait measure set. We also examined conventional parameters such as gait acceleration, gait cycle, and gait variability, but failed to reproduce the distinct differences among the five groups. These findings suggest that the proposed gait analysis may be useful in capturing disease-specific gait features in a community setting.

Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges

“…Some studies included and compared several types of disorders: frailty and mild cognitive impairment; Parkinson disease and dementia (Yoneyama et al, 2016); Parkinson disease and peripheral neuropathy (Sejdic et al, 2014); or Parkinson disease and progressive supranuclear palsy (Hatanaka et al, 2016). Most of the other studies aimed at differentiating pathological and healthy participants.…”

A Proposal for New Algorithm that Defines Gait-Induced Acceleration and Gait Cycle in Daily Parkinsonian Gait Disorders

“…The above mathematical method was used to build better fitting regression curves from the data. In other words, this mathematical method for regression curves represents improvement compared to that shown in Figure 4C and D. Figure 6A shows that two regression lines are necessary for better fitting of plots obtained from long-term daily walking, suggesting that a subject usually walks with two types of gait mode [49]. The steep regression line with small accelerations for a particular cycle means quiet walking, for example, walking within the house, whereas the flat regression line with large accelerations for a particular cycle reflects active walking, for example, walking in the street.…”

“…This system consists of two components: the first component is a wearable device with an acceleration sensor, and the second one is an automatic gait detection algorithm. Gait-induced accelerations are deduced from limb and trunk movements using a mathematical algorithm known as the "pattern-matching method" [41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Thus, the PGR can quantitatively identify various movement-induced accelerations and gait-induced accelerations, respectively.…”

“…The present article is a review of our published data on the PGR and gait disorders [41][42][43][44][45][46][47][48][49][50][51][52][53][54] and proposes a new algorithm that defines the relationship between gait-induced acceleration and gait cycle duration so as to quantify comprehensively PD-specific pathophysiological mechanisms underlying PD gait disorders.…”

“…Acceleration signals were measured by a portable PGR equipped with a triaxial accelerometer (Mimamori-gait system, LSI Medience Corporation: size, 75 mm × 50 m × 20 mm; weight, 120 g) ( Figure 1A) [41][42][43][44][45][46][47][48][49][50][51][52][53][54]. The sensor is sensitive not only to dynamic acceleration associated with rapid body movements but also to those with static acceleration due to gravity.…”