A reliability assessment software using Kinect to complement the clinical evaluation of Parkinson's disease

Abstract

Parkinson's disease is characterized by alterations in the gait pattern that may increase the risk of falls. Variations in the gait pattern cannot be objectively measured in clinical examination, so it is necessary to adapt devices to measure objectively, valid and replicable changes in gait patterns that are part of the evolution of the disease and / or pharmacotherapy. In an interdisciplinary effort, we developed the "e-Motion Capture System" software, which is able to calculate motor (cadence, stride and step length) and spatiotemporal (velocity and acceleration) parameters that affect quality of life in patients with Parkinson's disease. In this paper, we show results of the comparison between our e-Motion software and a benchmark reference, multiple-camera 3D motion capture system to track a gait pattern. This analysis was performed to compare the spatial locations of the ankles of a volunteer under indoor controlled conditions. Our results for the comparison between e-Motion and the 3D motion capture system show excellent agreement.

Exploring Movement Impairments in Patients With Parkinson's Disease Using the Microsoft Kinect Sensor: A Feasibility Study

“…They demonstrated a high accuracy of the Kinect sensor when measuring time and gross spatial characteristic movements relevant to PD and highly appropriate for distinguishing non-PD subjects from PD patients treated with deep brain stimulation. Likewise, the Kinect sensor has shown high validity regarding gait parameters when validated against a multiple-camera 3D motion capture system (41).…”

Age Matters: Objective Gait Assessment in Early Parkinson’s Disease Using an RGB-D Camera

“…Compared to healthy subjects, the stance time values were prolonged in the PD group. Previous studies on gait analysis in PD have also shown a higher stance time phase compared to controls, which they have associated with longer double limb support [19, 22].…”

“…Kinect is able to detect and track 20 different body joints (Figure 1(a)). Comparisons between the Kinect and benchmark references have shown a high agreement [19, 20]. Also, this device has been used in different research areas, like e-health [21, 22], security and surveillance [23–25], and UAV and robot vision [26].…”

“…This system contains a motion sensing device (Kinect™ V1 or V2), a computer with the e-motion software, free interface capture area, and a rater (physician or trained nurse). The e-motion software captures [19] skeleton information from the Kinect and records it in the computer, using an ID to identify the patient in later analysis. From the skeleton information, we can extract information from different joints and analyze it.…”

Sistema open-source para gerenciamento de dados da doença de Parkinson e detecção do sintoma do tremor em desenhos manuscritos pelo uso de histogramas de gradientes orientados

“…A Figura Por fim, a Figura 3. Da mesma forma, os conjuntos de dados resultantes do trabalho de Cunningham et al [41] avaliando a destreza da mão, Pastorino et al [42] que registram os dados de movimento do paciente para detectar a condição de medicação ON/OFF, a avaliação e o monitoramento da marcha propostos pelos autores Paredes et al [44], Pepa et al [50], e Patel et al [51]; o monitoramento da bradicinesia proposto por Eskofier et al [45], e os dados de avaliação da fala descritos pelos autores Dimauro et al [47]. Todos esses dados poderiam ser coletados do mesmo paciente, registrados no sistema (SIDABI), e poderiam ser comparados para uma melhor compreensão da doença, ou para monitorar a evolução do sintoma estudado, buscando proporcionar melhores condições de vida aos pacientes de uma maneira mais eficiente.…”