Human Identification Based on Gait Analysis: A survey

Marker-less analysis of human gait has made considerable progress in recent years. However, developing a gait analysis system capable of extracting reliable and precise kinematic data in a standard and unobtrusive manner remains an open challenge. This narrative review considers the transformation of methods for extracting gait extremity information from videos or images, perceived how analysis methods have improved from arduous manual procedures to semi-objective and objective marker-based systems and then marker-less systems. The gait analysis systems widely used restrict the analysis process with the use of markers, inhibited environmental conditions, and long processing duration. Such limitations can impede the use of a gait analysis system in multiple applications. Advancement in marker-less pose estimation and Q-learning-based techniques are opening the possibility of adopting productive methods for estimating precise poses of humans and information of movement from video frames. Vision-Based gait analysis techniques are capable of providing a cost-effective, unobtrusive solution for estimation of stick images and thus the analysis of the gait. This work provides a comprehensive review of marker-less computer vision and deep neural network-based gait analysis, parameters, design specifications, and the latest trends. This survey provides a birds-eye view of the domain. This review aims to introduce the latest trends in gait analysis using computer vision methods thus provide a single platform to learn various marker-less methods for the analysis of the gait that is likely to have a future impact in bio-mechanics while considering the challenges with accuracy and robustness that are yet to be addressed.KeywordsHuman Gait analysisMarker-less techniquesPose estimationStick imagesAbnormality detection

Objectives:Gait and motion analysis are important tools in assessment of athletic performance and clinical evaluation. Wearable sensor based solutions have advantages over video based systems by their prices and ease. Resistive, magnetic or optic Encoders, flexible resistors and accelerometers are common sensor types unsed in gait analysis systems used in rehabilitation. All these sensors have similar mounting and electronic instrumentation approach but all have separate mechanical joint design. The purpose of this study is to develop a single mechanical joint for multitype angular position sensors used in gait analysis.Methods:Single turn analog encoder, flexible resistor and 3 axis accelerometer was used as angular position sensor. Constant voltage source (7805) was used for instrumentation of analog encoder and 3 axis accelerometer driver circuit. A voltage divider was used for flexible resistor driver circuit. Mechanical design was made to obtain small and light mechanical joint where all sensors can be mounted.Results:Flexible sensor and encoder was mounded on the center of mechanical joint. Encoder is mounted outside and flexible sensor was mounted in side of the mechanical joint center. Accelerometer was mounted on lower (movable) arm of mechanical joint. Encoder has direct connection with upper and lower arms of mechanical joint. Root of flexible sensor is mounted on one side o the mechanical joint and driven by a mechanical dial on the other arm. Range of motion for analog encoder is 0-360 degree in a single plane, flexible is 0-180 degree for single plane and accelerometer is 0-360 degree for tree planes.Conclusion:Most difficult part of the design was to obtain highest range for flexible resistor. This sensor have 3 inclenght and changes its restinte related to its bending angle. It’s possible to obtain measurement by directly paste it on skin and it’s also easy to have measurement on mechanical joints in narrow angles. For a conventional mechanical joint the range can not be lower then 180 degrees. The length of resistor limits the range. This design problem was solved by adding a secondary mechanical shaft to joint center, winding the sensor over it a mechanical dial. This design can be used in training and research purpose where double check is needed. Future studies may focus on adding contless sensors and integrated circuit type angular sensors to mechanical joint.

It is recognized that gait analysis is a powerful tool used to capture human locomotion and quantify the related parameters. PODOSmart® insoles have been designed to provide accurate measurements for gait analysis. PODOSmart® insoles are lightweight, slim and cost-effective. A recent publication presented the characteristics and data concerning the validity of PODOSmart® insoles in gait analysis. In literature, there is still no evidence about the repeatability of PODOSmart® gait analysis system. Such evidence is essential in order to use this device in both research and clinical settings. The aim of the present study was to assess the repeatability of PODOSmart® system. In this context, it was hypothesized that the parameters of gait analysis captured by PODOSmart® would be repeatable. In a sample consisting of 22 healthy male adults, participants performed two walking trials on a six-meter walkway. The ICC values for 28 gait variables provided by PODOSmart® indicated good to excellent test-retest reliability, ranging from 0.802 to 0.997. The present findings confirm that PODOSmart® gait analysis insoles present excellent repeatability in gait analysis parameters. These results offer additional evidence regarding the reliability of this gait analysis tool.

Gait analysis systems are essential for rehabilitation but are often time-consuming and less accessible in low- and middle-income countries. GAITWell was developed to address these challenges with its portable and modular design for automated gait data collection and analysis. This study evaluates its methodological properties. GAITWell uses discrete binary sensors on interconnected plates to capture gait data, analyzed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which identifies key reference points like foot contact and toe-off. DBSCAN detects clusters of arbitrary shapes and sizes, separates noise from data, and identifies natural patterns within the data space without prior group knowledge. Each plate measures 44 cm × 37 cm and has an 11 × 7 sensor array with 4 cm spacing. Test–retest reliability was evaluated using the intraclass correlation coefficient (ICC), standard error of the mean (SEM), and Bland–Altman plots. Concurrent validity was assessed by comparing GAITWell measurements to those from the Qualisys Pro-Reflex system. Results: 38 healthy adults participated (average age 33.2 years, SD 13.0). Correlations between GAITWell and Qualisys ranged from moderate (right step length) to very high (gait speed, cycle time, right and left step time, left step length, stance time, swing time, right and left cadence, and base of support). Moderate to good agreement was found for gait speed, cycle time, stride length, right and left step length, right and left step time, and stance and swing time, but poor agreement was observed for double support times, right and left cadence, and base of support. Preliminary analysis suggests that increasing sensor resolution could reduce measurement error by 70%. Conclusions: GAITWell is a promising tool, with future research focusing on enhancing sensor accuracy for improved reliability.

6: Treadmill training with partial body weight support in children with cerebral palsy

Dynamic and Functional Gait Analysis of Severely Displaced Intra-Articular Calcaneus Fractures Treated with a Hinged External Fixator or Internal Stabilization

Gait analysis is an important research field in neurodegenerative disease diagnosis. Various IMU-based and vision-based methods have been developed for measuring joint angles during walking process. Dynamic Vision Sensor (DVS) is a neuromorphic engineering product which outputs sequences of events describing the level of brightness change at the pixel level in an asynchronized manner, instead of discrete frames at a predefined frame rate. Due to its microsecond level resolution, DVS is regarded possessing a huge potential to dramatically increase the speed of sensing pipeline for various applications which traditional CMOS camera cannot fulfill. Hence, in this paper we introduce a novel system for gait analysis application; DVS and special markers are used to detect the ankle joint during walking and a method is developed to calculate the desired angle. Further, the robust locally weighted regression is employed as signal smoothing method to reduce the amount of noise. In order to evaluation its performance, an Inertial Measurement Unit (IMU)-based sensory system is also examined and compared in the same experiment and goniometer is used for providing the ground truth. By comparing the captured ankle joint angle trajectories using Dynamic Time Warping (DTW), DVS-based system appears to have higher accuracy in terms of ankle joint angle detection than IMU-based system.

Robotic walkers are assistive robotic devices that provide mobility assistance, in a domestic or clinical scenario, to individuals suffering from a gait disorder, being age related or due to injuries, surgery, or diseases. Walkers also provide a significant potential for lower limb rehabilitation. In this paper, we present a novel multimodal robotic walker platform, the ISR-AIWALKER, where innovative contributions were made both in the human- machine interface (HMI) and in a gait analysis system placed on board the platform. Taking into account the application potential of these devices, an effort was made to use low-cost sensors without sacrificing the overall performance of the system. A change was made in the HMI paradigm, moving from a force-sensing to a vision-based approach, while maintaining a natural user interaction and adding complementary safety features like correct gripping enforcement. To cope with the close proximity of the user's body, a multimodal sensor setup was considered. Using both RGB and depth map data, a kinematic model of the user's lower limbs is obtained, allowing the identification of a set of features that are used in a machine learning approach to discriminate gait asymmetries. Experiments made with several subjects revealed that the proposed HMI is able to correctly estimate the user intention in a natural and intuitive way. The gait analysis system was also evaluated and evidenced a good discrimination capability to distinguish between different gait patterns.

Purpose: The aim of this study was to quantify the accuracy of 3D trajectory reconstructions performed from two planar video recordings, using three different reconstruction methods. Additionally, the recordings were carried out using easily available equipment, like built-in cellphone cameras, making the methods suitable for low-cost applications. Methods: A setup for 3D motion tracking was constructed and used to acquire 2D video recordings subsequently used to reconstruct the 3D trajectories by 1) merging appropriate coordinates, 2) merging coordinates with proportional scaling, and 3) calculating the 3D position based on markers’ projections on the viewing plane. As experimental verification, two markers moving at a fixed distance of 98.9 cm were used to assess the consistency of results. Next, gait analysis in five volunteers was carried out to quantify the differences resulting from different reconstruction methods. Results: Quantitative evaluation of the investigated 3D trajectories reconstruction methods showed significant differences between those methods, with the worst reconstruction approach resulting in a maximum error of 50% (standard deviation 13%), while the best resulting in a maximum error of 1% (standard deviation 0.44%). The gait analysis results showed differences in mean angles obtained with each reconstruction method reaching only 2°, which can be attributed to the limited measurement volume. Conclusions: Reconstructing 3D trajectory from 2D views without accounting for the “perspective error” results in significant reconstruction errors. The third method described in this study enables a significant reduction of this issue. Combined with the proposed setup, it provides a functional, low-cost gait analysis system.

Although previous studies have evidenced the value of three-dimensional gait analysis (3DGA) for evaluating gait disorder, the time-consuming measurement process and space requirement has hampered its use in the clinical setting. The aim of this study was to examine the feasibility of a simplified 3DGA system for stroke patients. Thirteen pairs of stroke patients and age- (± 1 year), gender-, and gait speed- (± 0.5 m/s) matched controls were drawn from the Fujita Health University gait analysis database. 3DGA was performed using the KinemaTracer® treadmill gait analysis system. Comparisons of the spatiotemporal and kinematic parameters were performed between stroke patients and matched controls. The correlations between items from the Wisconsin Gait Scale (WGS) and 3DGA data in stroke patients were also investigated. 3DGA measurements clearly showed reduced toe clearance, hip flexion, and knee flexion in stroke patients compared with the matched controls. In contrast, significant increases were observed in hip elevation, shoulder elevation, shoulder lateral shift, and step width in stroke patients. For the four items drawn from the WGS, a significant correlation with three 3DGA parameters was observed: stance time on the impaired side, stance width, and knee flexion from toe off to midswing. In this study, significant differences in gait parameters of stroke patients and age-, gender-, and speed-matched controls were found using a simplified 3DGA system. A significant correlation with WGS was also observed. These results support the validity of the clinical measurement of gait parameters using a simplified 3DGA system.

Scientific gait analysis methods aim to offer objective measurements, to assist physicians towards an accurate diagnosis or pre-diagnosis of ailments before they actually manifest through noticeable symptoms. This paper reviews selected gait analysis system technologies, trends, applications and discusses errors and precision in spatial and angular readings. Furthermore, we propose a novel test and calibration method using a biomimetic rig. To illustrate this, we conduct three tests on an optical single-camera gait analysis system based on a mobile android smart-phone with specially developed software.

Human gait is an important indicator of health, with applications ranging from diagnosis, monitoring, and rehabilitation. In practice, the use of gait analysis has been limited. Existing gait analysis systems are either expensive, intrusive, or require well-controlled environments such as a clinic or a laboratory. We present an accurate gait analysis system that is economical and non-intrusive. Our system is based on the Kinect sensor and thus can extract comprehensive gait information from all parts of the body. Beyond standard stride information, we also measure arm kinematics, demonstrating the wide range of parameters that can be extracted. We further improve over existing work by using information from the entire body to more accurately measure stride intervals. Our system requires no markers or battery-powered sensors, and instead relies on a single, inexpensive commodity 3D sensor with a large preexisting install base. We suggest that the proposed technique can be used for continuous gait tracking at home.

Computerized treadmill gait analysis in models of toxicant exposure and neurodegenerative disorders holds much potential for detection and therapeutic intervention in these models, and researchers must validate the technology that assists in that data collection and analysis. The present authors used a commercially available computerized gait analysis system that used (a) a motorized treadmill on retired breeder male C57BL/6J mice, (b) the toxicant-induced (1-methyl-1-, 2-, 3-, 6-tetrahydropyridine) MPTP mouse model of Parkinson's disease (PD), and (c) the superoxide dismutase 1 (SOD1) G93A transgenic mouse model of amyotrophic lateral sclerosis (ALS). The authors compared the detection of deficits by computerized treadmill gait analysis in MPTP-treated mice with inked-paw stride length and correlated these measures to dopamine (DA) loss. The authors found that the computerized treadmill gait analysis system did not distinguish MPTP-treated mice from vehicle controls, despite a nearly 90% deficit of striatal DA. In contrast, decreases in inked-paw stride length correlated strongly with DA losses in these same animals. Computerized treadmill gait analysis could neither reliably distinguish SOD1 G93A mutant mice from controls from 6 to 12 weeks of age nor detect any consistent early motor deficits in these mice. On the basis of the authors' findings, they inferred that computerized gait analysis on a motorized treadmill is not suited to measuring motor deficits in either the MPTP mouse model of PD or the SOD1 G93A mouse model of ALS.

Tracking the motions and positions in three-dimensional space is an exciting human gait analysis approach for healthcare and clinical examination. The high-end motion capture systems, such as Vicon cameras, could automate the gait analysis process, but the system is too costly for a clinical setting. We developed a cost-effective motion capture and gait analysis system using an existing commercial camera found on a mobile device. We take advantage of the Artificial Intelligence and Machine Learning technique in mobile devices for human pose estimation and detection to keep track of body motion in the environment. This paper presents our mobile application case study to measure three-dimensional kinematics of lower-body gait of the hip, knee, and ankle during walking tasks using the Apple Augmented reality toolkit. We evaluated our application by comparing it with the measurements observed by a Vicon T40s motion-tracking system. The results showed that the gait movement can be measured in real-time. The lower-gait angles of hip and knee agreed with those from the Vicon system, whereas the motions of small joint sections were not captured as accurately. The proposed application is a cost-effective, easy-to-use, and mobile gait analysis system that can be used to construct three-dimensional gait scores to improve the examination of gait and decision making in various clinical populations.

Changes in the gait characteristics, such as walking speed and stride length, of a person living at home can be used to presage cognitive decline, predict fall potential, monitor long-term changes in cognitive impairment, test drug regimens, and more. This thesis presents a novel approach to gait analysis in a smart-home environment by leveraging new advances in inexpensive sensors and embedded systems to create novel solutions for in-home gait analysis. Using a simple, non-invasive hardware system consisting entirely of wall-mounted infrared and radio frequency sensor arrays, data is collected on the gait of subjects as they pass by. This data is then analyzed and sent to a clinician for further study. The system is non-invasive in that it does not use cameras and could be built into the molding of a home so that it would be nearly invisible. In a finished prototype version, the system presented in this thesis could be used to analyze the gait characteristics of one or more subjects living in a home environment while ignoring the data of visitors and other non-subject cohabitants. The ability to constantly collect data from a home environment could provide thousands of observations per year for clinical analysis. Providing such a robust data set may allow people with gait impairment to live at home longer and more safely before transitioning to a care facility, have a reduced fall risk due to better prediction, and live a healthier life in old age.