Automated Gait Analysis using a Kinect Camera and Wavelets

Munoz, Beatriz; Castaño-Pino, Yor Jaggy; Paredes, Juan David Arango; Navarro, Andrés

doi:10.1109/healthcom.2018.8531161

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…In this analysis, only the ankle data (left and right) were considered. To generate spatiotemporal variables, we select the best wavelet performance, which was obtained by a comparison between multiple wavelet decomposition and the clinical expert judgment [41].…”

Section: Gait Analysis With Waveletmentioning

confidence: 99%

Using Wavelets for Gait and Arm Swing Analysis

Castaño-Pino¹,

Navarro²,

Muñoz³

et al. 2019

Wavelet Transform and Complexity

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The human walking pattern can be affected by different factors such as accidents, transplants, or diseases, like Parkinson's disease, which affects motor and mental functions. In motor terms, this disease can generate alterations such as tremors, festination, rigidity, unbalance, slowness, and freezing of gait. Additionally, it is estimated that for the year 2040, the number of people with Parkinson's in the world will be between 12.9 and 14.2 million people. These alarming figures make Parkinson's disease an important focus of attention. In this chapter, we present contributions that suggest wavelet techniques as a useful tool to perform a gait and arm swing analysis; this represents an important approximation that can contribute to describe and differentiate people with Parkinson's disease in early stages of the disease.

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Section: Gait Analysis With Waveletmentioning

confidence: 99%

Using Wavelets for Gait and Arm Swing Analysis

Castaño-Pino¹,

Navarro²,

Muñoz³

et al. 2019

Wavelet Transform and Complexity

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“…Cameras are also often used in a motion capture approach, with markers reflecting infrared light, which are tracked in three dimensions with a very high precision and sample rate. Adding a spatial dimension to the camera image is possible with RGB-D cameras (Red-Green-Blue-Depth), like the Microsoft Kinect ® , which was for example used previously for gait analysis in a classification problem in patients with Parkinson’s disease [ 21 , 22 ] or Multiple Sclerosis [ 23 ]. Further research using Kinect sensor was conducted in gait analysis of children with ataxia [ 24 ] or cerebral palsy [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Hoffmann

Brodowski

Steinhage³

et al. 2021

Sensors

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Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

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“…El uso de sensores de profundidad también le permite capturar patrones del movimiento en 3D, con lo que puede ser utilizado para la medición de la marcha. Algunos resultados a favor del uso del Kinect® han reportado: la utilidad para diferenciar pacientes y controles [189,190]; una elevada concordancia con el laboratorio de marcha en la articulación del tobillo, que podría ser útil en el cálculo de las variables clínicas de la marcha [191]; la identificación de pasos en movimiento [192], control postural [193], variables como velocidad, longitud del paso y del ciclo de la marcha [188] en sujetos sanos, la evaluación dinámica de la marcha [194], los movimientos gruesos de extremidades superiores, la lentificación de los movimientos en personas con EP [195] y el coeficiente de asimetría en el balanceo de brazos en personas con EP en estadios tempranos [196].…”

Section: Cualificación Clínica Y Cuantificación De La Marcha Usando Nunclassified