Gait Identification Research Articles

Human Posture Detection Method Based on Wearable Devices

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

Early identification of toe walking gait in preschool children - Development and application of a quasi-automated video screening procedure

PurposeTo develop and test the application of a quasi-automated screening procedure identifying probable toe walking in a large population of preschool children. MethodsThe proposed screening procedure was designed to identify children exhibiting signs of toe walking in a previously recruited cohort of preschool children (MiPS cohort). The procedure combines parent observation (step 1), objective parameters of foot contact during gait by an automated screening of 3-D video recordings (step 2), and clinical video screening of the children identified in step 1 and/or 2 (step 3). FindingsFrom 879 children, gait trials were obtained from 87% (n = 766). Step 1 (parent observation) identified 34 children with potential toe walking, step 2 (automated screening) 122. Fourteen were identified in both step 1 and 2. Thus, 142 children were selected for step 3 (clinical video screening), from which 41 children were classified as showing symmetric signs of toe walking, and five children were identified with asymmetrical signs of toe walking. Of the 41, five had been identified by step 1 only, 32 by step 2 only and four by both steps. InterpretationApplication of a quasi-automated screening algorithm was feasible and may assist in early detection of toe walking. Disagreements found between parent reported toe walking and video screening, indicate added value in quasi-automated video screening. However, thresholds of heel lift and clinical criteria of toe walking in the algorithm and video screening need to be addressed and validated to confidently identify toe walking gait.

Progression of Gait Changes in Older Adults With Mild Cognitive Impairment: A Systematic Review.

The identification of altered gait and its progression over time is important to gaining a better understanding of the clinical aspects of mild cognitive impairment (MCI) in older adults. The aim of the present systematic review was to determine changes in gait variables over time among older adults with MCI. The PubMed, Web of Science, Scopus, and Science Direct databases were searched for relevant articles using the following keywords and Medical Subject Headings: Aged AND "Mild cognitive impairment" AND (gait OR locomotion). A hand search was also performed of the reference lists of the selected articles in an attempt to find additional records. The following were the inclusion criteria: longitudinal studies and clinical trials involving a control group without intervention; samples of individuals 65 years or older; and characterization of gait using a single or dual task. The initial search led to the retrieval of 6979 studies, 9 of which met the inclusion criteria. The duration of follow-up among the studies ranged from 6 months to 2 years. Most trials investigated gait speed. Other gait variables were step length, time required to walk a given distance, and mean weekly gait speed. Altered gait progressed in older adults with MCI. The main alterations were gait speed and variability in daily number of steps in follow-up periods lasting more than 1 year. No significant changes in gait variables were found in shorter follow-up periods (up to 6 months). The progression of gait changes in older adults with MCI has been underinvestigated. MCI leads to reduced gait speed in longer follow-up periods. Such information can contribute to the determination of motor interventions for older adults with MCI, especially in the early stages.

TransferSense: towards environment independent and one-shot wifi sensing

WiFi has recently established itself as a powerful medium for radio frequency (RF) sensing due to its low cost and convenience. Many tasks, such as gesture recognition, activity recognition, and fall detection, can be implemented by measuring and calculating how the propagation of WiFi signals is affected by human activities. However, current WiFi-based sensing solutions have limited scales as they are designed for only a few activities and need to collect data and create training models in the same domain because the model established in a deployment environment is usually not applicable to new objects in the target domain. This paper presents TransferSense, an environment independent and one-shot WiFi sensing method based on deep learning. Firstly, amplitude and phase information of channel state information (CSI) are combined to increase the number of features to solve the problem of insufficient features due to single-source information. Secondly, TransferSense converts RF sensing tasks to image classification tasks and fuses low-level and high-level semantic features extracted from a pre-trained convolutional neural network to achieve an end-to-end high-precision sensing for activity recognition. Finally, TransferSense applies a transfer learning method with a small number of labeled samples in the target domain to perform high-precision cross-domain sensing, which can reduce the data collection cost in the target domain. We verified the effectiveness of TransferSense using two representative WiFi sensing applications, gait identification and sign recognition. In a single deployment environment, TransferSense achieved more than 97% human gait identification accuracy for 44 users and more than 81% sign language recognition for 100 isolated sign language words. In the case of new object recognition in the cross-domain sensing, TransferSense achieved more than 77% human gait identification accuracy for 10 new users, more than 88% sign language recognition for 10 new isolated sign language words, and more than 81% gesture identification for 2 new gestures.

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation comprises technological devices for gait analysis, such as dedicated force sensors, cameras, and wearable sensor-based solutions; however, they are limited by insufficient gait parameter recognition, postprocessing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and nonintrusive gait recognition method from impulse radio ultrawideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3-D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by a statistical graphical approach Bland and Altman (B&A) analysis.

Retraction: Analysis and identification of body features in Alzheimer’s patients for gait recognition and gait attractiveness (IOP Conf. Ser.: Mater. Sci. Eng. 1022 012009)

This article has been retracted by IOP Publishing following an allegation that this article may contain tortured phrases [1]. IOP Publishing has investigated in line with the COPE guidelines and have found tortured phrases to be masking overlap of other work [2-3] and agrees this work should be retracted. IOP Publishing wishes to credit PubPeer commenters [4] and the Problematic Paper Screener for bringing this issue to our attention. The authors have neither confirmed whether they agree or disagree to this retraction. 1. Cabanac G, Labbe C, Magazinov A, 2021, Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals, arXiv:2107.06751vv1 2. Jie Hong, 2012, Human Gait Identification Thesis, Brunel University 3. Hong J, Kang J & Price ME, 2014, Extraction of bodily features for gait recognition and gait attractiveness evaluation. Multimed Tools Appl 71, 1999–2013 4. https://pubpeer.com/publications/C555DA27762FFD3284E9345F56FBCC Retraction published: 18 January 2023

A Wearable Pedestrian Localization and Gait Identification System Using Kalman Filtered Inertial Data

In this article, we introduce a pedestrian dead reckoning (PDR)-based navigation device that does not require global navigation satellite system (GNSS) signals or beacons and works with an inertial measurement unit (IMU) mounted on its waist belt. The system identifies the individual by their walking pattern to use the proper gains in the computations, estimates the attitude by applying an unscented Kalman filter, and finally derives the position in three dimensions with the help of a step detection algorithm. The experimental results show an outdoor 4.7-km walk resulting in an error of 0.96%.

Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks.

Dynamic vision sensors (event cameras) have recently been introduced to solve a number of different vision tasks such as object recognition, activities recognition, tracking, etc. Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and much larger dynamic range. However, these cameras only produce noisy and asynchronous events of intensity changes, i.e., event-streams rather than frames, where conventional computer vision algorithms can't be directly applied. In our opinion the key challenge for improving the performance of event cameras in vision tasks is finding the appropriate representations of the event-streams so that cutting-edge learning approaches can be applied to fully uncover the spatio-temporal information contained in the event-streams. In this paper, we focus on the event-based human gait identification task and investigate the possible representations of the event-streams when deep neural networks are applied as the classifier. We propose new event-based gait recognition approaches basing on two different representations of the event-stream, i.e., graph and image-like representations, and use graph-based convolutional network (GCN) and convolutional neural networks (CNN) respectively to recognize gait from the event-streams. The two approaches are termed as EV-Gait-3DGraph and EV-Gait-IMG. To evaluate the performance of the proposed approaches, we collect two event-based gait datasets, one from real-world experiments and the other by converting the publicly available RGB gait recognition benchmark CASIA-B. Extensive experiments show that EV-Gait-3DGraph achieves significantly higher recognition accuracy than other competing methods when sufficient training samples are available. However, EV-Gait-IMG converges more quickly than graph-based approaches while training and shows good accuracy with only few number of training samples (less than ten). So image-like presentation is preferable when the amount of training data is limited.

Human gait identification using two dimensional multi-resolution analysis

Application of wearable devices like as inertial sensors has grown rapidly in various daily living applications such as, in wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Many methods have been suggested to extract various heuristic and high-level features of motion sensor data to identify discriminative gait signatures to distinguish the target individual from others. In this study, we propose an identification method for human gait identification using spectro-temporal two dimensional (2D) expansion of human gait cycles, and then implement a 2D multi-resolution analysis to decompose the expanded 2D space to different levels of resolution. We propose a systematic methodology for processing motion signals for the purpose of human gait identification by first extracting the Gait cycles. Then, we perform 2D spectro-temporal representation of each cycle. After that, we conduct multi-resolution decomposition and analysis of the 2D spectrograms to extract useful information for the predictive task. We collect raw motion data from five inertial sensors placed on the chest, lower back, right hand wrist, right knee and right ankle. After pre-processing the raw recordings, we propose an effective heuristic segmentation method to extract the gait cycles from the processed data. Spectro-temporal features are extracted by merging key instantaneous spectral descriptors in a gait cycle that characterize the non-stationarities in each gait cycle inertial data in two dimensions. The 2D spectro-temporal expansion of the gait cycles extracted from inertial sensor data from a population of 10 subjects are decomposed to several levels of resolutions. Based on the extracted features from each level of decomposed space, the identification task is accomplished. Based on our experimental results, 93.36% subject identification accuracy was achieved.

Human Gait Cycle Analysis Using Kinect V2 Sensor

Examining a human movement can provide a wealth of information about a patient’s medical condition. The examination process can be used to diagnose abnormal changes (lesions), ability development and monitor the rehabilitation process of people with reduced mobility. There are several approaches to monitor people, among other things with sensors and various imaging and processing devices. In this case a Kinect V2 sensor and a self-developed LabView based application was used, to examine the movement of the lower limbs. The ideal gait pattern was recorded in the RoboGait training machine and the measured data was used to identify the phases of the human gait. During the evaluation, the position of the skeleton model, the associated body joints and angles can be calculated. The pre-recorded ideal and natural gait cycle can be compared.With the self-developed method the pre-recorded ideal and natural gait cycle can be compared and processed for further evaluation. The evaluated measurement data confirm that a reliable and mobile solution for gait analysis has been created.

Functional inference on rotational curves under sample‐specific group actions and identification of human gait

AbstractInspired by the problem of gait reproducibility (reidentifying individuals across doctor's visits) we develop two‐sample permutation tests under a sample‐specific group action on Lie groups with a bi‐invariant Riemannian metric. These tests rely on consistent estimators and pairwise curve alignment. To this end, we propose Gaussian perturbation models and for the special case of curves on the group of 3D rotations we provide asymptotic consistency and, employing a quaternion point of view, fast spatial alignment of pointwise extrinsic mean curves. In our application to rotations of the tibia versus the femur at the knee joint under the spatial action of marker placement and the temporal action of different walking speeds, obtained from an experiment, we solve the problem of gait reproducibility.

Horse Gait Identification Using Distributed Acoustic Sensing

In this paper field measurements are carried out using a distributed acoustic sensing system (DAS) for the identification of horse gait. The DAS system has a spatial resolution of 1 m, frequency range of 1-5 kHz and the fiber length is of about 70 m. The optical fiber cable lays down into trench of 15 cm of depth. Several measurements are also carried out in order to fully understand the measuring capacity of the system, including a 2 kg calibrated impact hammer and a small controlled blast of 12.6 Pa of air pressure. Results demonstrate the capacity to measure soil mechanical waves speed of 171 m/s for the impact hammer tests and sound speeds in air of about 366 m/s for the controlled blast tests. The experiments involving horses allowed the identification of three distinct horse gait patterns: step, trot and gallop, as well the horse speed in each gait with SNR about 30 dB.

Gait and balance disturbances are common in young urbanites and associated with cognitive impairment. Air pollution and the historical development of Alzheimer's disease in the young

To determine whether gait and balance dysfunction are present in young urbanites exposed to fine particular matter PM2.5 ≥ annual USEPA standard, we tested gait and balance with Tinetti and Berg tests in 575 clinically healthy subjects, age 21.0 ± 5.7 y who were residents in Metropolitan Mexico City, Villahermosa and Reynosa. The Montreal Cognitive Assessment was also applied to an independent cohort n:76, age 23.3 ± 9.1 y. In the 575 cohort, 75.4% and 34.4% had abnormal total Tinetti and Berg scores and high risk of falls in 17.2% and 5.7% respectively. BMI impacted negatively Tinetti and Berg performance. Gait dysfunction worsen with age and males performed worse than females. Gait and balance dysfunction were associated with mild cognitive impairment MCI (19.73%) and dementia (55.26%) in 57/76 and 19 cognitively intact subjects had gait and balance dysfunction. Seventy-five percent of urbanites exposed to PM2.5 had gait and balance dysfunction. For MMC residents-with historical documented Alzheimer disease (AD) and CSF abnormalities, these findings suggest Alzheimer Continuum is in progress. Early development of a Motoric Cognitive Risk Syndrome ought to be considered in city dwellers with normal cognition and gait dysfunction. The AD research frame in PM2.5 exposed young urbanites should include gait and balance measurements. Multicity teens and young adult cohorts are warranted for quantitative gait and balance measurements and neuropsychological and brain imaging studies in high vs low PM2.5 exposures. Early identification of gait and balance impairment in young air pollution-exposed urbanites would facilitate multidisciplinary prevention efforts for modifying the course of AD.

Quaternion statistics applied to the classification of motion capture data

Unit quaternions give quite new possibilities in an analysis of motion capture data. They provide a compact, holistic axis-angle representation of 3D rotations. An application of descriptive statistics – measures of location and dispersion – is common in numerous problems related to an assessment of joint movements. For those reasons, the paper proposes new approaches to the extraction of motion descriptors on the basis of descriptive statistics of 3D rotations represented by unit quaternions as well as the appropriate classification schemes operating on motion descriptors. Mean and median values and standard deviations are calculated for time series with raw rotational data as well as angular velocities and accelerations. The problem of human gait identification is addressed in the numerical validation of the introduced methods and highly precise marker-based motion capture data are utilized. The results obtained – the accuracy of gait recognition – are compared to the ones achieved by descriptive statistics calculated for time series of Euler angles. The general conclusion is that unit quaternions are effective in the calculation of descriptive statistics. They preserve robust discriminative features of joint movements and they can be applied in numerous challenges of expert and intelligent systems.

Human Identification System Based on Spatial and Temporal Features in the Video Surveillance System

Human identification is the most significant topic in the bioinformatics field. Various human gait identification methods are available to identify humans, but detecting the objects based on the human gait is still a challenging task in the video surveillance system. Thus, an effective hybrid Bayesian approach is proposed for identifying the humans. The proposed hybrid Bayesian approach involves two stages as follows: the first stage is the human identification based on the object features, and the second stage is the human identification based on the spatial features. Initially, the videos are fed into the first stage, where the object detection is performed using the Viola Jones algorithm. Once the objects are detected, the feature extraction process is carried out by using a hierarchical skeleton to effectively extract the selective features. The object skeleton provides an effective and intuitive abstraction, which offers object recognition and object matching. The Bayesian network is adapted in the object-based features to identify humans. In the spatial-based human identification stage, only the spatial features are extracted and are passed into the gait-based Bayesian network to identify the humans. Finally, the resulted output is obtained using the fuzzy holoentropy for identifying the humans. The experimentation of the proposed hybrid Bayesian approach is performed using the dataset named UCF-Crime, and the performance is evaluated by considering the average value of the metrics, namely F1-score, precision, and recall which acquired 0.8820, 0.8770, and 0.9203, respectively.

FEEDBACK-BASED GAIT IDENTIFICATION USING DEEP NEURAL NETWORK CLASSIFICATION

Identification of gait plays a major role in the healthcare industry, recognition of a gait having different angles, identification of abnormalities is a challenging task, to detect the abnormal person identification contains improper pattern style, human limbs, walking pattern, etc... A normal person has a correct pattern, an abnormal person has an irregular pattern. This paper provides the identification of the lean angle and ramp angle [19] of irregular patterns on three abnormalities such as Parkinson gait, Hemiplegic gait, and Neuropathic gait [18] by using deep neural network (DNN) without clinical observation by using DNN classification with feedback-based verification of trained features with query features of abnormal identification of trained features with query features. This paper concludes the gait abnormalities based on lean angle and ramp angle.

Gait identification using a new time-warped similarity metric based on smartphone inertial signals

The evolution of smart devices and ubiquitous computing have paved the way for more intelligent user verification options. Gait pattern recognition has gained prominence due to their noninvasive and seamless verification capabilities without requiring dedicated attention from the user. This paper proposes a new gait identification approach by proposing a new time-warped similarity metric for memory-based gait pattern analysis. The proposed metric is incorporated into a computationally-efficient template matching framework for gait identification. The proposed approach outperforms the conventional approaches by achieving a user recognition error rate of 7.7% and equal error rate of 11.2% based on samples from 50 subjects in two benchmark datasets.

Gait identification and authentication using LSTM based on 3-axis accelerations of smartphone

Abstract In this study, we apply recurrent neural networks and Long Short-Term Memory (LSTM) to 3-axis accelerations of walking acquired by a smartphone for gait identification and authentication. First, the accelerations during walking for 21 subjects are recorded in two holding situations, namely, while placing the smartphone in the pocket or looking at the screen of the smartphone. We then identify and authenticate the user by inputting the accelerations directly to the recurrent neural networks and LSTM. We have confirmed that the performance of this method is better than the conventional method of extracting features from the accelerations and using random forests.

A High-Fidelity Wearable System for Measuring Lower-Limb Kinetics and Kinematics

There are many important challenges in gait analysis, which has many applications in healthcare, rehabilitation, therapy, and exercise training. However, gait analysis is typically performed in a gait laboratory, which is inaccessible to the general population and is not available in natural gait environments (e.g., outdoors). In this paper, we discuss the development of a high-fidelity, cost-effective, wireless sensor network to address the challenge of efficient gait monitoring in real-world walking scenarios. The sensor network is designed in a modular way to capture plantar forces and knee angle, angular velocity, and angular acceleration. A force module called a smart insole is designed to measure the plantar forces. The module is comprised of force sensitive resistors (FSRs) and a signal conditioning circuit. Various signal conditioning techniques, including a novel technique called transfrequency, are investigated to provide a linear mapping for FSR measurements and to provide data acquisition fidelity. The motion module includes a low-cost inertial measurement unit (IMU) augmented with a Kalman filter to provide filtered knee kinematics. A qualitative evaluation of the sensor network communication module is achieved by considering the internal communication protocols between the modules and the external wireless transmission protocol used to deliver data to an end-point terminal PC. Experiments are conducted to validate the motion and force modules. Then, the overall, integrated system is compared to gold standard laboratory results, demonstrating a successful application for gait identification. The results show that the sensor network accurately captures important gait parameters and features.

Identifying biometrics in the wild – A time, erosion and neural inspired framework for gait identification

This paper evaluates the performance of contemporary gait identification systems. A time, erosion and neural inspired framework (TEN-FE) for gait identification was proposed to augment the performance of gait identification systems. Performance of TEN-FE framework was evaluated using CASIA and OU-ISIR large population dataset. Proposed framework relies on CNN and Reinforcement Learning to restrict the impact of confounding factors like baggage and bulky clothing on the accuracy of gait identification systems. Difference in gait signature due to time was also considered and normalized. The results observed a clear increase in system’s performance with minimal complexity and least hardware requirements.