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The humanoid robot, WABIAN-2R, has achieved human-like walking with heel contact and toe off motions with a foot mechanism with a passive toe joint. However, the foot structure is different from a human΄s. In this paper, we describe a new foot mechanism mimicking the human΄s foot arch structure to figure out its function. Especially, the developed foot mimics the elastic properties of the arch and the change of the arch height during walking. We conducted several walking experiments by using WABIAN-2R, and we confirmed that the arch elasticity could absorb a foot-landing impact force at the plantar contact phase and the change of the arch height contributed to a strong thrust at the push-off phase. [ABSTRACT FROM AUTHOR]

A novel crutch-like device compensates for weak hip abductors in gait. Many teenagers with weak hip abductors, an important group being individuals with low level spina bifida, who are able to walk with crutches, abandon community ambulation in favor of a wheelchair because walking takes more energy than using a wheelchair. The health benefits to standing and walking as opposed to using a wheelchair include improved bone growth, improved blood circulation, reduced bladder infections, reduced pressure sores and prevention of contractures. The increased energy cost, which is characterized by excessive motions of the pelvis and torso, is attributed to weakened or paralyzed hip abductor muscles. A novel crutch, designed by the authors, lowers the energy cost of walking which encourages patients with weak hip abductors to continue ambulation. Published research shows that significant work is done in the frontal plane by the hip to stabilize the pelvis in normal gait. In the absence of hip abductors, other motions and muscles are used to inefficiently stabilize the pelvis. The new crutch compensates for weak or absent hip abductor torques by applying a load on the side of the upper torso. A 3D model of crutch walking validates the device and determines the forcing and fitment guidelines. The novel 3D gait model is capable of capturing crutch gait incorporating pelvic tilt, pelvic rotation and pelvic obliquity. The 3D model predicts a reduction in the hip abductor moment required during a gait cycle when using the crutch compared to forearm crutches if the user has very weak hip abductor muscles. The decrease in the required hip abductor moment is attributed to the forcing caused by the crutch, which may reduce excessive lateral displacement of the pelvis and torso. The decrease in the required hip abductor moment reduces the compensating motions that would otherwise be necessary. [ABSTRACT FROM AUTHOR]

We show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). Based on an extensive corpus of motion capture data, with physical and perceptual ground truth, we analyze the inference of subtle biologically-inspired attributes from cyclic gait data. It is shown that inference is also possible with partial observations of the body, and with motions as short as a single gait cycle. Learning models from small amounts of noisy video pose data is, however, prone to over-fitting. To mitigate this we formulate learning in terms of domain adaptation, for which mocap data is uses to regularize models for inference from video-based data. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis, Parkinson΄s disease and Huntington΄s disease are three neuro-degenerative diseases. In all these diseases, severe disturbances of gait and gait initiation are frequently reported. In this paper, we explore the feasibility of using machine learning and statistical approaches to support the discrimination of these three diseases based on gait analysis. A total of three supervised classification methods, namely support vector machine, KStar and Random Forest, were evaluated on a publicly-available gait dataset. The results demonstrate that it is feasible to apply computational classification techniques in characterise these three diseases with the features extracted from gait cycles. Results obtained show that using selected 4 features based on maximum relevance and minimum redundancy strategy can achieve reasonably high classification accuracy while 5 features can achieve the best performance. The continual increase of the number of features does not significantly improve classification performance. [ABSTRACT FROM AUTHOR]

In this paper we investigate how spring-like leg behavior in human running is represented at joint level. We assume linear torsion springs in the joints and between the knee and the ankle joint. Using experimental data of the leg dynamics we compute how the spring parameters (stiffness and rest angles) change during gait cycle. We found that during contact the joints reveal elasticity with strongly changing parameters and compare the changes of different parameters for different spring arrangements. The results may help to design and improve biologically inspired spring mechanisms with adjustable parameters. [ABSTRACT FROM AUTHOR]

An algorithm is created, which performs human gait analysis using spatial data and amplitude images from a Time-of-flight camera. For each frame in a sequence the camera supplies cartesian coordinates in space for every pixel. By using an articulated model the subject pose is estimated in the depth map in each frame. The pose estimation is based on likelihood, contrast in the amplitude image, smoothness and a shape prior used to solve a Markov random field. Based on the pose estimates, and the prior that movement is locally smooth, a sequential model is created, and a gait analysis is done on this model. The output data are: Speed, Cadence (steps per minute), Step length, Stride length (stride being two consecutive steps also known as a gait cycle), and Range of motion (angles of joints). The created system produces good output data of the described output parameters and requires no user interaction. [ABSTRACT FROM AUTHOR]

Quantitative models of whole body expressive movement can be developed by combining methods form biomechanics, psychology, and statistics. The purpose of this paper was to use motion capture data to assess emotion-related gait kinematics of hip and shoulder sagittal plane movement to evaluate the feasibility of using functional data analysis (FDA) for developing quantitative models. Overall, FDA was an effective method for comparing gait waveforms and emotion-related kinematics were associated with emotion arousal level. [ABSTRACT FROM AUTHOR]

Many advances have been done in the area of prosthetic devices for transfemoral amputees. One of the approaches in this field has been the use of EMG signals from the muscles, in order to identify the instant of gait cycle, as well as to generate a control signal for the prosthesis actuator. This kind of signals has a huge potential for controlling a prosthesis, but at the same time has several problems associated [1]. Until now, some of these problems remained unsolved and as a matter of fact, it has not been possible to use EMG signals for the control of a commercial prosthesis. Nowadays, research is being done at the Simón Bolívar University in Venezuela which aims at developing an A/K prosthesis. At this point, the idea is to try different approaches to the control problem, regarding specifically what kind of signals to use. One of the signals studied are the EMG signals. The present work pretends to show a methodology to make an algorithm that allows generating a control signal for a knee prosthesis. At present time, EMG signals from leg muscles of healthy persons have been captured, processed and analyzed. A threshold has been identified from which it is possible to generate a control signal of two states of activity ON/OFF. In other words, knowing this threshold it is possible to say when to block and unblock the prosthetic knee. The research will be continued, trying to generate a more complete algorithm which gives as output not a two-states control signal, but a continuous one. This would allow to position the knee in different angular positions and surely, would be an important outcome for the A/K prosthesis being developed. [ABSTRACT FROM AUTHOR]

The project relies on an algorithm that combine two technologies, one involved Tekscan pressure sensors, and the other Biometrics electrogoniometers. Eight male transfemoral amputees subjects were assessed on the treadmill during five minutes in three series in their own comfortable walking speed. The pressure was measured dividing the stump in four areas two anterior, and two posterior. The two anterior areas were identified as the peak pressure, however depending on the gait cycle the pressure distribution changes at several sites at the stump-socket interface. The pressure-goniometry system algorithm could improve the dynamic stump pressures within the socket evaluation and become useful for dynamic transfemoral prosthesis assessment. [ABSTRACT FROM AUTHOR]

This paper presents an approach to people identification using gait based on floor pressure data. By using a large area high resolution pressure sensing floor, we were able to obtain 3D trajectories of the center of foot pressures over a footstep which contain both the 1D pressure profile and 2D position trajectories of the COP. Based on the 3D COP trajectories a set of features are then extracted and used for people identification together with other features such as stride length and cadence. The Fisher linear discriminant is used as the classifier. Encouraging results have been obtained using the proposed method with an average recognition rate of 94% and false alarm rate of 3% using pair-wise footstep data from 10 subjects. [ABSTRACT FROM AUTHOR]

Ankle equinus, with contracture of the Achilles tendon, could have a devastating effect on foot and ankle function and morphology in the long term if left unevaluated and untreated. The gastroc-soleus complex easily overpowers the other musculotendinous units and ligamentous constraints in the foot when pathologically tight. This can cause ulceration, midfoot breakdown, ankle pathology, and potential gait derangement (Fig. 24.1).1-20 With repetitive contact between the ground and the foot, any structure responsible for abnormal loading across the foot during the gait cycle, most often the Achilles tendon, hastens foot breakdown. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective approach to detect the directional bias of walking four-legged robots. We found that light weight four-legged robots tend to have unstable directional bias even when walking on flat ground, thus this approach is designed to be able to dynamically and continuously detect the directional bias. By observing and analyzing data from acceleration sensors of AIBO robots, we found the relational model between the acceleration data and the walking gaits by which we may utilize to develop an ideal gait based approach to detect directional bias. In order to make sure the correctness of our approach, several experiments were designed and executed. As a result, we can correctly detect the direction of bias within five gaits, and the correct rate of single gait can be up to 90%. [ABSTRACT FROM AUTHOR]

Recently, the accelerometry has been suggested as a useful method for the gait analysis because of the operational simplicity and economical efficiency. Many studies have described the acceleration characteristics during walking with an accelerometer on the sacrum. But they had considerable difficulties in determination of gait patterns or gait phases especially in patients. Therefore, two accelerometers placed on each posterior superior iliac spine(PSIS) were introduced in the present study to distinguish better. Twenty healthy volunteers who have no injury on the low extremity were selected for the study. Two triaxial accelerometers were attached on the left and right PSIS. Both accelerometers were synchronized with the 3D motion analysis system. In the left and right direction(X), the peak value of PSIS acceleration of contact side was larger than the other approximately 7%. In the anterior-posterior direction(Y), the peak value of contact side was larger than the other approximately 13%. In the vertical direction(Z), the peak value of was larger than the other approximately 54% right after the initial contact. With differences between left and right acceleration, the gait phase detection can be achieved more easily and the peak value differences informed the gait characteristics of the left and right foot in distinguishable. The accuracy of detecting gait phase could be increased by using two acceleration curves as compared to the using one acceleration curve. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine distributions of local plantar shear stress and pressure in diabetic foot patients during walking. Twelve normal subjects who have no surgical history of foot problems in foot and three diabetic foot patients with diabetic neuropathy in the lateral heel participated in the study. An insole-type local shear stress sensor was developed, and both mediolateral and anteroposterior components of local shear stress were measured during level walking. Results showed that shear stress in the lateral heel for diabetic foot patients was relatively smaller than that in the normal. For diabetic foot patients, significantly larger shear stress in the medial direction was observed at the 2nd metatarsal head in pre-swing, comparing with the normal. Anteroposterior shear stress in diabetic foot patients became significantly smaller at the 4th metatarsal head in pre-swing than that in the normal. For patients who had diabetic neuropathy in the lateral heel, the center of pressure started from the medial heel and then moved linearly toward 1st or 2nd metatarsal head. Diabetic foot patients showed relatively smaller pressure in the 2nd metatasal head and the hallux in pre-swing than the normal subjects. Results showed that local shear stress distribution correlated very well with the pressure distribution. Further study with patients of different foot problems would be very helpful to design corresponding foot orthoses. [ABSTRACT FROM AUTHOR]

It is of utmost importance that the characteristics and the differences between the normal and toe-walking individuals be analyzed using gait analysis. Through this analysis, the underlying physiology and the biomechanics of normal and abnormal gait may be established and used in various comparative gait analyses, especially those studies involving human locomotion. The purpose of this preliminary study is to analyze the differences that may exist between normal and toe-walking performed by the same normal and healthy individuals. These data may later be used in analyzing the gait of pathological toe-walkers, which may be useful as part of their rehabilitation process. The current study focuses on the kinetics and kinematics parameters, namely the ground reaction forces (GRF), centre of pressure (COP), and range of motion (ROM); at the hip, knee, and ankle joints. The kinetics and kinematics data of normal and toe-walking data from four normal and healthy subjects were collected and analyzed. The overall gait data were obtained simultaneously using a combination of Peak Motus® 2000 software suite for the kinematics data and Kistler force plates for the kinetics data. It was found that the parameters of choice for the kinetics and kinematics analysis did not produce a large range of difference between the two types of walking; except for the right ankle joint, which produces a completely different range of motion. In addition, there was also an absence of dorsiflexion at the initial contact of the foot and a decrease in peak ankle plantar flexion at the terminal stance phase of toe-walking. The latter may suggest that less peak ankle plantar flexion strength is needed for toe-walking as compared to normal walking. [ABSTRACT FROM AUTHOR]

The present study investigated the biomechanical effects of weight variation for side loaded carriage during walking upon joint moments and the center of body mass by using the inverse dynamic analysis. The motion data which were used as input data for inverse dynamic simulation were measured by the 3D motion analysis system. Six infrared cameras and two force plates were utilized and thirty nine reflective markers were attached to the subject to capture the walking motion with side loaded carriage. The healthy male subjects were participated in the experiment, and they walked with side loaded carriage which weighs 0, 5, 10 and 15kg, respectively. For each walking, the weight of side loaded carriage was selected randomly. As a result, the COM(Center Of Mass) was more inclined to the opposite side of side loaded carriage as the weight increased. This is the result of the balance mechanism of human in walking with side load. In other words, the human kept the total balance, leaning to the side of unloading when the human walked with one heavy side loaded. In addition, the ankle plantar-flexion moment of loading side was larger than that of unloading side when the weight increased. In knee joint, the knee extension moment of unloading side was smaller than that of loading side when the weight increased. The magnitude of hip extension moment had no significant difference. The hip extension moment of loading side was larger than that of unloading side. These results showed that the human used the ankle and hip joint of the loading side to support the body and the knee joint of the unloading side to progress the walking with keeping the balance of the body. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a portable gait-event detection system which is necessary for the cycle-to-cycle FES(functional electrical stimulation) control of locomotion. Proposed gait-event detection system consists of a signal measurement part and gait event detection part. To make the system portable, we made following modifications from the previous wired system. That is, 1) to make the system wireless using Bluetooth communication, 2) to make the system small-sized and battery-powered by using low power consumption microprocessor. We also used a video camera system to get the reference gait events. The gait-events were detected offline at the main computer using ANN(Artificial Neural Network) of Machine learning technique. The proposed system showed no mis-detection of the gait-events of normal subject and hemiplegia subjects. The performance of the system was better than the previous wired-system, probably due to the reduction of unnatural movement during gait signal measurement. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller. [ABSTRACT FROM AUTHOR]

Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. In this paper, an improved method for gait recognition is proposed. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (KPCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Classic linear feature extraction approaches, such as PCA, LDA, and FLDA, only take the 2-order statistics among gait patterns into account, and are not sensitive to higher order statistics of data. Therefore, KPCA is used to extract higher order relations among gait patterns for future recognition. Fast Fourier Transform (FFT) is employed as a preprocessing step to achieve translation invariant on the gait patterns accumulated from silhouette sequences which are extracted from the subjects walk in different speed and/or different time. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches. [ABSTRACT FROM AUTHOR]

Gait along with body structure has been recognized as a potential biometric feature for identifying human beings. The spatial and temporal shape of motion of an individual is usually the same for all gait cycles and is considered to be unique to that individual. In this paper we introduce a Temporal Slice based approach for gait recognition. Temporal Slices are a set of two-dimensional images extracted along the time dimension of an image volume. They encode a rich set of visual patterns for similarity measure and have been widely used for motion detection. We show that the features obtained from tensor histogram of these temporal slices can be efficiently used as gait features for recognition of human beings. [ABSTRACT FROM AUTHOR]

Inflammatory synovitis leads to the weakening of the stabilizing structures of the foot, resulting in pain and deformity and in turn leads to biomechanical abnormalities (121). However, these functional abnormalities also enhance deformity, thus a vicious circle is created (fig. 1). [ABSTRACT FROM AUTHOR]

This paperThis work is suported by KTU (Grant No: KTU-2002.112.009.1). presents a novel view independent approach on silhouette based human motion analysis for gait recognition applications. Spatio-temporal 1-D signals based on the differences between the outer of binarized silhouette of a motion object and a bounding box placed around silhouette are chosen as the basic image features called the distance vectors. The distance vectors are extracted using four view directions to silhouette. Gait cycle estimation and motion analysis are then performed by using normalized correlation on the distance vectors. Initial experiments for human identification are finally presented. Experimental results on the different test image sequences demonstrate that the proposed algorithm has an encouraging performance with relatively robust, low computational cost, and recognition rate for gait-based human identification. [ABSTRACT FROM AUTHOR]

A proper gait assessment in patients with knee or hip injuries strongly determines the diagnosis and consequently the evolution of the pathology, the quality of life of implanted patients, and the overall costs involved. Among the different strategies to clinically assess gait, 3D optical tracking provides a reliable and objective evaluation. This method involves state-of-the-art image analysis that performs anatomical measurements upon bony landmarks identified by markers attached to the patient. We show how this technology can be used to perform patients diagnosis and follow-up by grouping the results of gait measurement with a competitive neural network where the number of clusters is automatically determined. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel approach for learning view-invariant gait representation that does not require synthesizing particular views or any camera calibration. Given walking sequences captured from multiple views for multiple people, we fit a multilinear generative model using higher-order singular value decomposition which decomposes view factors, body configuration factors, and gait-style factors. Gait-style is a view-invariant, time-invariant, and speed-invariant gait signature that can then be used in recognition. In the recognition phase, a new walking cycle of unknown person in unknown view is automatically aligned to the learned model and then iterative procedure is used to solve for both the gait-style parameter and the view. The proposed framework allows for scalability to add a new person to already learned model even if a single cycle of a single view is available. [ABSTRACT FROM AUTHOR]

Cartilage functions as a low-friction, wear-resistant, load-bearing tissue. During a normal gait cycle, one cartilage surface rolls and slides against another, all the while being loaded and unloaded. The durability of the tissue also makes for an impressive material to study. However, when cartilage is damaged or diseased, the tissue has little capacity to repair itself. The goal of cell-based repair strategies to replace damaged or diseased tissue requires that the functional biomechanical properties of normal (developing or mature), diseased, and repair cartilage be restored. This chapter addresses some of the major methods used to assess the biomechanical properties of native and tissue-engineered cartilage. First, the traditional methods of testing by compression, tension, shear, and indentation are reviewed. Next, additional methods to evaluate interfacial mechanics and lubrication are described. Thus, a variety of mechanical tests may be used to assess functional properties for normal, diseased, and tissue-engineered cartilage. [ABSTRACT FROM AUTHOR]

Intelligent interaction with an environment, other IVAs, and human users requires a system that identifies subtle expressive cues and behaves naturally using modalities such as body, face, and voice to communicate. Although research on individual affective channels has increased, little is known about expressive qualities in whole body movement. This study has three goals: (1) to determine rates of observer recognition of emotion in walking, (2) to use kinematic analysis to quantify how emotions change gait patterns in characteristic ways, and (3) to describe the concurrence of facial and bodily expression of emotion. Twenty-six undergraduate students recalled an experience from their own lives in which they felt angry, sad, content, joy, or no emotion at all (neutral). After recalling a target emotion, participants walked across the lab. Whole body motion capture data were acquired using a video-based, 6-camera system. Side view video was also recorded. Ten participants wore a special head mounted camera designed to record video of facial expression. After each trial, participants rated the intensity of eight emotions (4 target and 4 non-target). After blurring the faces in the side view video so that facial expressions were not observable, randomized composite videos were shown to untrained observers. After viewing each video clip, observers selected one of ten responses corresponding to the emotion that they thought the walker felt during the trial. FACS coding was used to evaluate the face video for evidence of emotion and timing of facial expressions with respect to the gait cycle. Self-report data indicated that the walkers felt the target emotions at levels corresponding to "moderately" or above in all trials. Validation data were collected from five observers on gait trials from a subset of subjects (n=16). Recognition rates for sad, anger, neutral and content were 45%, 25%, 20% and 16%, respectively. Joy was recognized at chance levels (10%). Normalized velocity, normalized stride length, cycle duration and velocity were significantly affected by emotion. This study is unique in describing the effects of specific emotions on gait. The preliminary results indicate that gait kinematics change with emotion. Although temporal-spatial kinematics were related to arousal levels, angular kinematics are needed to distinguish emotions with similar levels of arousal. [ABSTRACT FROM AUTHOR]