Microsoft Kinect-based differences in lower limb kinematics during modified timed up and go test phases between men with and without Parkinson’s disease

J Ambient Intell Human Comput

Votta²,

Nerino³

et al. 2023

Postural instability is one of the most disabling symptoms of Parkinson’s Disease, with important impacts on people safety and quality of life since it increases the risk of falls and injuries. Home monitoring of changes in postural stability, as a consequence of therapies and disease progression, is highly desirable for the safety of the patient and better disease management. In this context, we present a system for the automatic evaluation of postural stability that is suitable for self-managing by people with motor impairment directly at home. The system is based on an optical RGB-Depth device, which tracks the body movements both for system’s interaction, thanks to a gesture-based human-machine interface, and the automated assessment of postural stability. A set of tasks, based on standard clinical scales, has been designed for the assessment. The user controls the delivery of the tasks through the system interface. A machine learning approach is adopted, and some kinematic parameters that characterize the user’s performance during each task execution are estimated and used by supervised classifiers for the automatic assessment. Data collected during experimental clinical trials were used to train the classifiers. This approach supports the compliance of the classifier assessments with respect to the clinical ones. The system prototype and the preliminary results on its accuracy in the assessment of postural stability are presented and discussed.

Section: Introductionmentioning

confidence: 99%

At-home assessment of postural stability in parkinson’s disease: a vision-based approach

J Ambient Intell Human Comput

Votta²,

Nerino³

et al. 2023

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Cerfoglio

Vismara

et al. 2022

Sensors

The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.

“…The Microsoft Kinect v2 is more robust and accurate as compared to Microsoft Kinect v1 [ 25 ], and it has been recognized a viable tool for tracking human movement in clinical applications [ 26 ], standing balance and postural stability [ 27 ], gait [ 28 ], body sway [ 29 ] and clinical motor functions [ 30 ]. In the specific context of neuro-degenerative diseases, it has been used for time up and go test [ 31 ], in assessing different types of PD patients [ 32 ], to classify gait disorders [ 33 ] and in neurological rehabilitation [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson’s Disease

Nerino

Chimienti

et al. 2019

Sensors

A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.