ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics

Beyond classical aspects related to locomotion (biomechanics), it has been hypothesized that walking pattern is influenced by a combination of distinct computations including online sensory/perceptual sampling and the processing of expectations (neuromechanics). Here, we aimed to explore the potential impact of contrasting scenarios (“risky and potentially dangerous” scenario; “safe and comfortable” scenario) on walking pattern in a group of healthy young adults. Firstly, and consistently with previous literature, we confirmed that the scenario influences gait pattern when it is recalled concurrently to participants’ walking activity (motor interference). More intriguingly, our main result showed that participants’ gait pattern is also influenced by the contextual scenario when it is evoked only before the start of walking activity (motor expectation). This condition was designed to test the impact of expectations (risky scenario vs. safe scenario) on gait pattern, and the stimulation that preceded walking activity served as prior. Noteworthy, we combined statistical and machine learning (Support-Vector Machine classifier) approaches to stratify distinct levels of analyses that explored the multi-facets architecture of walking. In a nutshell, our combined statistical and machine learning analyses converge in suggesting that walking before steps is not just a paradox.

Link of Video Abstract: https://youtu.be/91dLGOg7wjc Background: Treatment that can be done to prevent ulceration in diabetic feet is reducing high pressure in the foot area. One strategy that is currently being developed is biofeedback on gait patterns. Scientific evidence regarding the use of biofeedback in cases of diabetic neuropathy is still sparse, but it is a promising rehabilitation strategy to improve biomechanical deficits in gait patterns and allow subjects to initiate new gait strategies using visual and/or auditory information. Methods: This study is a pre-experimental research design before and after the intervention of giving biofeedback on walking patterns to 20 subjects with diabetic neuropathy who met the inclusion criteria and did not have exclusion criteria, measured plantar pressure at the beginning of the examination, and immediately after giving biofeedback on gait patterns. Results: The initial value of peak plantar pressure in the total area was high (more than 200 kPa). The initial value of peak plantar pressure in the 4 areas analyzed (heel, midfoot, forefoot, and big toe) was found to be in the range of 100-200 kPa, while the initial value of peak plantar pressure in the toes area was found to be <100 kPa. Immediately after administering biofeedback, the walking pattern showed a significant decrease in peak plantar pressure values in the total area, heel, and big toe compared to the initial value (p-value= 0.001, 0.012, 0.00). There was no significant decrease in peak plantar pressure values in the midfoot and toe areas. Conclusions: A significant reduction was achieved in the average plantar pressure in the heel, big toe, and overall area. This supports the recommendation to provide walking pattern biofeedback in diabetic neuropathy patients.

This paper presents an interactive control for our assistive robotic walker, the JAIST Active Robotic Walker (JARoW), developed for elderly people in need of walking assistance. The focus of our paper is placed on how to estimate the user's walking parameters by sensing the locations of lower limbs and to predict his or her walking patterns. For this purpose, a particle-filter-based prediction technique and a motion controller are developed to help JARoW smoothly generate the direction and velocity of its movements in a way that reflects the prediction. The proposed scheme and its implementation are described in detail, and outdoor experiments are performed to demonstrate its effectiveness and feasibility in everyday environments.

In this paper, we describe the design procedure of an above-knee powered prosthetic leg and an algorithm to generate appropriate gait patterns that are synchronized with the movement of the user. The developed prosthetic leg has powered knee and ankle joints for transfemoral amputees, and its weight and dimensions were determined on the basis of human body data. In particular, two degrees of freedom (roll and pitch axes) were adopted in the ankle joint to achieve dynamic balance control on uneven ground, and passive toe joints using a crank-rocker mechanism and torsional springs were attached at the foot to increase the walking stability. In addition, we developed a walking pattern simulator that is able to test the walking patterns of the powered prosthetic leg in the air. By attaching two inertial sensors on both thighs of the user and measuring both thigh motions, the per cent of gait cycle is suitably calculated from the proposed algorithm, and smooth gait motions are generated according to the gait cycle percent. Finally, walking patterns of the powered prosthetic leg were successfully generated by synchronizing to the human gait, and the generated gaits were analyzed by comparing them to the human gait.

This paper describes experiments to understand how well individuals can recognize an impaired walking pattern. The gait patterns are generated using a passive dynamic walker (PDW) model to allow a systematic change in gait patterns. The changed gait parameters include gait cadence, knee height asymmetry, step length and step time asymmetry, roll-over-shape (ROS) asymmetry, and knee damping asymmetry. Twenty participants rated twenty-four unimpaired and impaired walking patterns on a 7-point Likert scale. Results revealed that although a walking pattern may deviate dynamically from normal, there is a quantifiable range of gait impairments that may be dismissed as unimpaired. Particularly, we found that a knee height asymmetry range of roughly +10% (up) and −20%, could be dismissed as unimpaired. Findings show that the impairment perception of a damped knee joint can be countered by attaching a mass to the opposite lower limb.

We investigated the influence of viewing angle on performance in recognising the identity of one's own person and familiar individuals such as friends or colleagues from walking patterns. Viewpoint-dependent recognition performance was tested in two groups of twelve persons who knew each other very well. Participants' motion data were acquired by recording their walking patterns in three-dimensional space with the use of a motion capture system. Size-normalised point-light displays of biological motion of these walking patterns, including one's own, were presented to the same group members on a computer screen in frontal view, half-profile view, and profile view. Observers were requested to assign the person's name to the individual gait pattern. No feedback was given. Whereas recognition performance of one's own walking patterns was viewpoint independent, recognition rate for other familiar individuals was better for frontal and half-profile view than for profile view. These findings are discussed in the context of the theory of common coding of motor and visual body representations.

Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder related gait abnormalities. Traditional marker-based systems are well known for tracking gait parameters for gait analysis, however, it requires long set up time therefore very difficult to be applied in everyday realtime monitoring. Nowadays, there is ever growing of interest in developing portable devices and their supporting software with novel algorithms for gait pattern analysis. The aim of this research is to investigate the possibilities of novel gait pattern detection algorithms for accelerometer-based sensors. In particular, we have used e-AR sensor, an ear-worn sensor which registers body motion via its embedded 3-D accelerom-eter. Gait data was given semantic annotation using pressure mat as well as real-time video recording. Important time stamps within a gait cycle, which are essential for extracting meaningful gait parameters, were identified. Furthermore, advanced signal processing algorithm was applied to perform automatic feature extraction by signal decomposition and reconstruction. Analysis on real-word data has demonstrated the potential for an accelerometer-based sensor system and its ability to extract of meaningful gait parameters.

Human gait analysis plays a significant role in clinical domain for diagnosis of musculoskeletal disorders. It is an extremely challenging task for detecting abnormalities (unsteady gait, stiff gait, etc.) in human walking if the prior information is unknown about the gait pattern. A low-cost Kinect sensor is used to obtain promising results on human skeletal tracking in a convenient manner. A model is created on human skeletal joint positions extracted using Kinect v2 sensor in place using Kinect-based color and depth images. Normal gait and abnormal gait are collected from different persons on treadmill. Each trial of gait is decomposed into cycles. A convolutional neural network (CNN) model was developed on this experimental data for detection of abnormality in walking pattern and compared with state-of-the-art techniques.

To explore clinical findings in men with chronic pain after falanga torture as compared with controls, and to try to understand the nature of the pain mechanisms responsible. Eleven male torture victims from the Middle East with chronic pain after falanga, and 11 age, sex, and ethnically matched controls with no history of torture were recruited. All participants were interviewed regarding pain characteristics in the feet and lower legs at rest and when walking. Structural changes and motor and sensory function were clinically assessed according to a standardized protocol. The walking pattern was observed for compensatory gait patterns. The torture victims had pain in their feet and lower legs and a compensated gait pattern, usually with severe pain during walking. Reduced light touch and thermal sensation, tactile dysesthesia, allodynia, and tenderness on palpation were common findings. Structural changes in the feet were found in more than half of the victims, but did not correlate with pain reports. These clinical findings were nonexistent or seen only rarely in controls. We found clear clinical signs of nerve injury in the feet. The sensory findings indicated 2 neuropathic pain mechanisms, one dominated by a peripheral pain generator and other by irritative phenomena (dysesthesia, allodynia), indicating central sensitization. It is reasonable to assume that these changes are due to the falanga exposure. A nociceptive contribution cannot be excluded. It is important to perform an individual diagnostic analysis to facilitate adequate treatment.

The effects of different carrying methods on human gait parameters

Selection of broiler strains for high body weight has changed the anatomical characteristics and in connection to that, the pattern of locomotion. In addition, rapid growing broilers show a high incidence of leg disorders which compromise the walking ability of the birds. Differences in the patterns of locomotion between laying hens and broilers and between broilers with and without leg disorders can be demonstrated and quantified by gait analysis. The gait pattern was recorded by videotracking. Three points of the body - the cloacal region and both feet joints - were marked by small patches of reflecting foils. The vertical and horizontal movements of the marked points were recorded by a camera in posterior position, while the bird was walking on a treadmill The camera was connected with a PC-operated videotracking system. The vertical and horizontal movements of the 3 marked points were recorded simultaneously and plotted against the time axis. Kinetograms show clearly the differences in the walking pattern between broilers and layers. Layers place the legs directly under the centre of gravity and, therefore, the body moves in a straight line. Broilers, in contrast, move the centre of gravity step by step laterally towards the position of the supporting leg. This pattern may be caused by the anatomical characteristics of broilers. Limping in broilers with leg problems can be measured by differences in the lateral and vertical movements of the right and left leg.

Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is designed to estimate the real-time posture of user's lower limb. This system can be used both for monitoring the user's walking status and for realizing the relative localization. A belt of multiple sonar sensors worn by the user can detect the obstacle of unknown environment. Simultaneously, this sonar belt can also be used as the absolute localization device in a similar way as the mobile robot. An EKF is applied to realize the data fusion of different sensor systems. The abilities of obstacle avoidance and indoor localization of the proposed ETA are both verified through experiments.

Gait patterns have been used widely in recent years to authenticate users. Because it doesn’t require user intrusion, it is often used as a biometric to make authentication processes easier and hassle free. But there are various issues with this process. To start with, the viewing angle has to be constant which is quite difficult to achieve with limited number of cameras. Speed too can alter the way a person walks and cause inconsistencies in identification. Furthermore, complications might arise if the subject is carrying something. The weight can affect his walking pattern. Besides, a recent accident could also transform a person’s walking pattern and lead to wrong identification. Other biometrics such as face detection can be combined with this technique to reduce the issues leading to erroneous identification. In this paper, we propose a system to overcome the viewing angle discrepancies. The system takes in walking sequences as input and processes them to create images. This is converted into 3D images by means of stereovision algorithms. Using which, we can effectively match the real-time image with various image sequences in the database. Side face detection can enhance the accuracy further..

We apply the reinforcement learning to acquire a gait pattern of a quadruped locomotion robot. The advantage of the reinforcement learning for such a problem is that no exact robot model is needed for calculating the prescribed teaching signals, but one simply needs to evaluate the results of trials and generates the reinforcement signals. As a result, the robot can acquire by itself a walking pattern suitable to its structure, dynamics and environments. We use here a tightly coupled modular actor-critic structure with stochastic gradient ascent. The computer simulations show that it could generate various stable walking pattern suitable to the environment and dynamics of the robot. We also apply the proposed method to an experimental real robot and deal with the learning process for getting the walking pattern.

When people are getting old, some gait abnormalities may have happened in their walking patterns. It means, there may be slight differences in their physical performance. Due to the complexity of that evaluation, a machine learning algorithm can be used to cluster the gait patterns. Kohonen Maps (KM) and mini-batch k-means (MBKM) have been combined to cluster the gait parameters according to the age groups to identify the principal gait characteristics which are affected to the walking pattern. Dataset is consisting of 180 gait data based on the data which have been gained through the inertial measurement unit (IMU). When analysing the results, the proposed algorithm is showing low computational cost and time which is more efficient. As well the results have been proved that the cadence is the most important and affected gait parameter when caused to a walking pattern of a person when he or she is getting older. These results provide clues for the health professionals to identify and evaluate the difficulties of walking patterns of patients according to age.