Gait Change Detection Using Parameters Generated from Microsoft Kinect Coordinates (original) (raw)
Related papers
Quantifying Gait Changes Using Microsoft Kinect and Sample Entropy
ArXiv, 2019
This study describes a method to quantify potential gait changes in human subjects. Microsoft Kinect devices were used to provide and track coordinates of fifteen different joints of a subject over time. Three male subjects walk a 10-foot path multiple times with and without motion-restricting devices. Their walking patterns were recorded via two Kinect devices through frontal and sagittal planes. A modified sample entropy (SE) value was computed to quantify the variability of the time series for each joint. The SE values with and without motion-restricting devices were used to compare the changes in each joint. The preliminary results of the experiments show that the proposed quantification method can detect differences in walking patterns with and without motion-restricting devices. The proposed method has the potential to be applied to track personal progress in physical therapy sessions.
Exploratory studies of human gait changes using depth cameras and considering measurement errors
arXiv (Cornell University), 2019
This research aims to quantify human walking patterns through depth cameras to (1) detect walking pattern changes of a person with and without a motion-restricting device or a walking aid, and to (2) identify distinct walking patterns from different persons of similar physical attributes. Microsoft Kinect™ devices, often used for video games, were used to provide and track coordinates of 25 different joints of people over time to form a human skeleton. Two main studies were conducted. The first study aims at deciding whether motion-restricted devices such as a knee brace, an ankle brace, or walking aids-walkers or canes affect a person's walking pattern or not. This study collects gait data from ten healthy subjects consisting of five females and five males walking a 10-foot path multiple times with and without motion-restricting devices. Their walking patterns were recorded in a form of time series via two Microsoft Kinect™ devices through frontal and sagittal planes. Two types of statistics were generated for analytic purposes. The first type is gait parameters converted from Microsoft Kinect™ coordinates of six selected joints. Then Sample Entropy (SE) measures were computed from the gait parameter values over time. The second method, on the other hand, applies the SE computations directly on the raw data derived from Microsoft Kinect™ devices in terms of (X, Y, Z) coordinates of 15 selected joints over time. The SE values were then used to compare the changes in each joint with and without motion-restricting devices. The experimental results show that both types of statistics are capable of detecting differences in walking patterns with and without motionrestricting devices for all ten subjects.
Iranian Rehabilitation Journal (IRJ), 2023
Although the Microsoft Kinect has compelling potential for gait analysis in medicine, data available to compare it with observational gait analysis (OGA) is scarce. This study compared the Microsoft Kinect and the OGA in assessing the gait parameters of apparently healthy adults. Methods: Ninety-seven apparently healthy young male adults participated in this comparative study. First, the participant's age, height, weight, and body mass index were obtained. Afterward, gait parameters involving the number of steps, cadence, stride length, and step length were assessed concurrently following OGA standard procedures and the Microsoft Kinect during a 6-m walk down the hallway. The obtained data were analyzed using descriptive and inferential statistics. The significance level was set at P<0.05.
Iranian Rehabilitation Journal
Objectives: Although the Microsoft Kinect has compelling potential for gait analysis in medicine, data available to compare it with observational gait analysis (OGA) is scarce. This study compared the Microsoft Kinect and the OGA in assessing the gait parameters of apparently healthy adults. Methods: Ninety-seven apparently healthy young male adults participated in this comparative study. First, the participant’s age, height, weight, and body mass index were obtained. Afterward, gait parameters involving the number of steps, cadence, stride length, and step length were assessed concurrently following OGA standard procedures and the Microsoft Kinect during a 6-m walk down the hallway. The obtained data were analyzed using descriptive and inferential statistics. The significance level was set at P<0.05. Results: The Mean±SD walk time, steps, cadence, velocity, and stride length were 8.07±1.39 s, 14.0±2.96 counts, 72.9±11.9 steps/min, 0.8±0.13 m/s, and 0.77±0.13m, respectively. Step...
Use of the image and depth sensors of the Microsoft Kinect for the detection of gait disorders
Neural Computing and Applications, 2015
This paper presents a novel method of gait recognition that uses the image and depth sensors of the Microsoft (MS) Kinect to track the skeleton of a moving body and allows for simple human-machine interaction. While video sequences acquired by complex camera systems enable very precise data analyses and motion detection, much simpler technical devices can be used to analyze video frames with sufficient accuracy in many cases. The experimental part of this paper is devoted to gait data acquisition from 18 individuals with Parkinson's disease and 18 healthy age-matched controls via the proposed MS Kinect graphical user interface. The methods designed for video frame data processing include the selection of gait segments and data filtering for the estimation of chosen gait characteristics. The proposed computational algorithms for the processing of the matrices acquired by the image and depth sensors were then used for spatial modeling of the moving bodies and the estimation of selected gait features. Normalized mean stride lengths were evaluated for the individuals with Parkinson's disease and those in the control group and were determined to be 0.38 and 0.53 m, respectively. These mean stride lengths were then used as features for classification. The achieved accuracy was[90 %, which suggests the potential of the use of the image and depth sensors of the MS Kinect for these applications. Further potential increases in classification accuracy via additional biosensors and body features are also discussed.
The Use of Microsoft Kinect for Human Movement Analysis
International Journal of Sports Science, 2015
The purpose of this study was to provide evidence of reliability and validity for the use of a Microsoft Kinect system to measure displacement in human movement analysis. Three dimensional (3D) video motion systems are commonly used to analyze human movement kinematics of body joints and segments for many diverse applications related to gait analysis, rehabilitation, sports performance, medical robotics, and biofeedback. These systems, however, have certain drawbacks pertaining to the use of markers, calibration time, number of cameras, and high cost. Microsoft Kinect systems create 3D images and are low cost, portable, not markers required, and easy to set up. They lack, however, evidence of reliability and validity for human movement kinematics analysis. Twenty-six participants were recruited for this study. Peak Motus version 9 and Microsoft Kinect system with customized skeleton software were used to collect data from each subject sitting on a platform moving horizontally at the...
Biomechanical application: exploitation of kinect sensor for gait analysis
2017
Human gait recognition is an important indicator and are extensively studied research area especially with the aging population and rehabilitation applications. Application of gait analysis ranges from diagnosis, monitoring and early detection of potential hazards such as human fall. There are various types of approaches used in gait analysis including wearable, ambient and vision based devices. Microsoft Kinect sensor is well-known among researchers since it can give depth and normal colour images as well. This paper presents a preliminary study on gait analysis of lower body parts. The measurement taken includes step width, step lengths, stride lengths and angles of knee respective hip and ankle while walking. The results showed that the algorithms implemented were able to accurately measure the lengths with low error rate.
A comparative study of the clinical use of motion analysis from Kinect skeleton data
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1score between 0.9-1.0). Indicating its potential application in the detection of a range of age-related motion impairments.
Archives of Physiotherapy and Global Researches, 2016
From a cibernetic approach, the body system can be defined like a net of structural and functional related subsystems with motor equifinality inside the concept of balance, energetic economy and comfort: therefore the ideal posture is the one that allows the maximum effectiveness of motor gesture, in absence of pain with the maximum energetic economy. The present study research is based on the necessity to individuate a real objective evaluation system of the postural parameters, inexpensive and of simple use compared to the evaluation instruments in use already scientifically validated. The instrument used in this study is the Microsoft Kinect®, gaming platform combined with the Xbox console. Created by Microsoft in the field of play, the Microsoft Kinect® for years has entertained millions of consumers through the Motion Capture System, the recording of movement through cameras and instant or deferred replay. The primary aim of this randomized controlled single-blind research is the demonstration that, despite being commonly defined objective the evaluation systems that utilize markers, is essential to look for an alternative evaluation method to minimize the systematic human error. The results demonstrate the real validity of Kinect®, and have verified the reliability of the data obtained from the assessment, showing the scientific reliability of this innovative objective evaluation method in rehabilitation-clinical field.
2022
Alterations of gait and balance are a significant cause of falls, injuries, and consequent hospitalizations in the elderly. In addition to age-associated motor decline, other factors can impact gait and stability, including the motor dysfunctions caused by neurological diseases such as Parkinson's disease or hemiplegia after stroke. Monitoring changes and deterioration in gait patterns and balance is crucial for activating rehabilitation treatments and preventing serious consequences. This work presents a Kinect-based solution, suitable for domestic contexts, for assessing gait and balance in individuals at risk of falling. The system captures body movements during home acquisition sessions scheduled by clinicians at definite times of the day and automatically estimates specific functional parameters to objectively characterize the subjects' performance. The system includes a graphical user interface designed to ensure usability in unsupervised contexts: the human-computer interaction mainly relies on natural body movements to support the self-management of the system, if the motor conditions allow it. This work presents the system's features and facilities, and the preliminary results on healthy volunteers' trials.