Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Abstract

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

“…To the best of our knowledge, this is one of the few reviews [1,52] investigating the clinical use of the main gait analysis technologies in neurological patients. From the literature, data emerges that WS, 3D motion capture systems, and IMUs are the most used gait assessment tools in the neurological population thanks to their smarter characteristics, and present less disadvantages than the NWS and/or laboratory systems (see Figure 3).…”

“…Wearable devices are still quite new, at least for clinical neurorehabilitation practice. WS are used to track motion outside the "obsolete" laboratory for gait analysis since they can recognize motor behavior in real-time, as well as plantar pressure and step frequency [1,52]. Thus, WS allows daily gait monitoring under different conditions.…”

“…In fact, the objective nature of gait assessment through WS and its practical advantages (Figure 2) in the clinical rehabilitation setting should motivate its adoption. Nevertheless, it should be considered that WS have some limitations that may be related to (i) the amount of time requested to analyze and review all the collected data, (ii) privacy and security issues, and (iii) technological equipment that requires high skilled professional figures [1,52].…”

“…Gait and postural impairments are the most frequent symptoms of neurological diseases (ND), including acquired brain injury (stroke and traumatic brain injury (TBI)) as well as neurodegenerative disorders, including Parkinson disease (PD), cerebellar ataxia (CA), and multiple sclerosis (MS). These impairments often reduce the quality of life of people affected by ND, limiting the activities of daily living [1]. Altered gait patterns in stroke survivors include a decreased stance phase and prolonged swing phase on the paretic side, in addition to a reduction of walking speed and shorter stride length [2].…”

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

“…Currently, standardized clinical measures for profiling movement deficits in post-stroke patients are subjective and imprecise [11]. The future of research in the area of assistive technologies design lies in the increased use of robotic devices, personalised by 3D printing; Internet of Things wearable sensors for personalised training of limb use, gait and balance, and activities of daily living and health monitoring [17][18][19][20][21]; and even novel brain-computer interfaces [22,23] and associated computational models [24]. Further, more advanced studies may additionally use data from postural and gait analysis [25,26].…”