Computation of Gait Parameters in Post Stroke and Parkinson’s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems

Abstract

The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.

Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

“…In recent years, several studies have investigated the accuracy and effectiveness of Microsoft Kinect for the assessment of posture, gesture, lower limbs and gait performance in several pathological states, such as stroke, Parkinson's disease [26,[65][66][67][68][69][70] and other pathologies [71][72][73][74]. Different studies have reported its reliability for the assessment of spatiotemporal gait parameters (e.g., step length and gait speed) and kinematic variables (e.g., trunk angle) in healthy individuals, with results comparable to those of laboratorygrade systems [25,[75][76][77][78][79][80] using both the first and second model of the device. The last version of the device, the Azure Kinect DK, was released in 2019 and, thanks to its new body tracking algorithm based on deep learning and convolutional neural networks [81],…”

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach based on Azure Kinect

“…For PoS, the sway of the body center of mass (COMBODY) is estimated from the skeletal model, as in [85,86,52], as the weighted average of the 3D centroids of some body segments, considering their mass and length as indicated by the anthropometric tables related to the anatomy of the human body [87]. For G, the estimated parameters are a subset of traditional spatio-temporal measures and arm swing parameters [56,58]: arm swing is a crucial element of walking and its impairment is often evident in individuals with PD.…”

“…Optical approaches leverage video recording devices (e.g., cameras) and vision techniques to implement completely non-invasive and easily manageable solutions for tracking and analyzing human body movement. Several studies have proposed optical approaches to characterize upper limb motor function [52,53], analyze lower limb dysfunctions and postural control [54], estimate gait features [55,56], evaluate arms swing [57,58] and analyze balance disorders [59,60]. Optical approaches have also been widely used in motor rehabilitation for specific pathologies [61,62], including PD [15,63], due to their portability, versatility, high usability, and easy integration into virtual environments.…”

“…However, the strong correlation between COM sways and Postural Instability and Gait Difficulty (PIGD) score has been previously verified [54].  Gait (G): UPDRS task to assess gait disorders through some spatiotemporal and arm swing parameters on a short walking path, as in [84,56,58].…”

Wearables for Movement Analysis in Healthcare

“…In clinical applications, wearable sensors were used in several pathological states, such as stroke [ 4 , 5 , 6 ], obese [ 7 , 8 ], and elderly [ 9 ] patients; patients with lower limb amputation [ 10 ]; patients with Parkinson’s disease [ 4 , 11 , 12 ]; and patients hospitalized for knee joint rehabilitation [ 13 ]. In particular, wearable systems were used both to quantify the functional limitations of the patients, during several movements (gait [ 5 , 7 , 8 , 9 ], upper limb [ 6 , 11 ], time up and go test [ 10 ], and unconstraint activities at home [ 12 ]) and to evaluate their accuracy and precision in comparison with the gold standard [ 4 ]. These papers support the clinical usability of wearable technology for clinical movement assessment.…”