A new technique for foot-off and foot contact detection in a gait cycle based on the knee joint angle using microsoft kinect v2

Amini, Amin; Banitsas, Konstantinos; Hosseinzadeh, Salaheddin

doi:10.1109/bhi.2017.7897228

Cited by 21 publications

(18 citation statements)

References 14 publications

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“…To the best of our knowledge, all studies on gait cycle detection using Kinect relied on the first version of the sensor (Kinect v1) [17, 18, 21, 30, 31], with the exception of one that used its second version [38]. In the latter study, the error of estimating gait events/parameters was not reported.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of gait analysis, different methods for gait cycle detection using the Kinect were proposed [17, 18, 21, 29–31, 38]. With the aim of gait rehabilitation in Parkinson’s disease, Cancela et al implemented a finite-state machine that detects different gait cycles phases [29], based on the left and right foot position provided by the first version of the Kinect (Kinect v1).…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Amini et al used the Kinect v2 for detecting foot-off and foot contact events [38]. The foot-off events were detected by finding the instants for which the knee angle decreases to less than a given threshold.…”

Section: Related Workmentioning

confidence: 99%

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System for automatic gait analysis based on a single RGB-D camera

et al. 2018

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Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable, portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 ± 25 ms and 1 ± 29 ms (walking towards the sensor), and 12 ± 23 ms and 2 ± 24 ms (walking away from the sensor). Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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System for automatic gait analysis based on a single RGB-D camera

et al. 2018

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“…Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detection, including (a) wearable accelerometer and/or gyroscope sensors [2,9,10], (b) smartphone-based sensors [11][12][13], (c) electromyograpy sensors [14,15], (d) pressure/force-based sensors [16,17], and (e) vision-based sensors [18,19]. However, such systems suffer from various drawbacks.…”

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confidence: 99%

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

et al. 2019

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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.China [3]. Every 1 in 37 people in the United Kingdom are diagnosed with PD at some stage of their life [1]. PD is caused by the neuronal loss and damage in the motor area of the brain, particularly the centers of the limbic, visceromotor, and somatomotor systems [4]. Most PD patients experience difficulty in walking, which indicates an onset of gait, balance, and other disabilities [5]. Half of the PD patients experience Freezing of Gate (FOG) symptoms, which represent an episodic absence of forward feet movement, despite of the intention for forward progress [6]. The frequency as well as the duration of FOG episodes increases with the progression of PD. FOG and falls are both interconnected phenomena, and FOG is known to hinder daily activities and increase fall risk [7], resulting in severe consequences for older adults.Conventional clinical methods to validate FOG occurrences and their severity are based on self-report questionnaires [8] and doctors' reports based on direct observation. Due to the impact of FOG on population in general and falls in older adults in particular, there is a high demand for automating the process of detection and recognition of FOG events. Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detectio...

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“…The Kinect was found to be a valid tool for assessing spatiotemporal components of gait [5,6] but it is unable to accurately assess lower limb kinematics [7,8]. In order to bypass the lower extremities inaccuracy a technique that relies on knee joint relative angle has been proposed to detect foot-off and foot contact during the gait cycle [9]. The Kinect was successfully used for the classification of human movement during active video game play in relationship to fundamental movement skill [10].…”

Section: Introductionmentioning

confidence: 99%

Gesture based Human Computer Interaction for Athletic Training

Khazaeli¹,

Kari²,

Baizer³

et al. 2017

Int J Appl Exerc Physiol

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ABSTRACT:The invention of depth sensors for mobile devices, has led to availability of relatively inexpensive high-resolution depth and visual (RGB) sensing for a wide range of applications. The complementary nature of the depth and visual information opens up new opportunities to solve fundamental problems in object and activity recognition, people tracking, 3D mapping and localization, etc. One of the most i nteresting challenges that can be tackled by using these sensors is tracking the body movements of athletes and providing natural interaction as a result. In this study depth sensors and gesture recognition tools will be used to analyze the position and angle of an athlete's body parts thought out an exercise. The goal is to assess the training performance of an athlete and decrease injury risk by giving warnings when the trainer is performing a high risk activity.

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A new technique for foot-off and foot contact detection in a gait cycle based on the knee joint angle using microsoft kinect v2

Cited by 21 publications

References 14 publications

System for automatic gait analysis based on a single RGB-D camera

System for automatic gait analysis based on a single RGB-D camera

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

Gesture based Human Computer Interaction for Athletic Training

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