Concept: Gait analysis
The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.
Human locomotion through natural environments requires precise coordination between the biomechanics of the bipedal gait cycle and the eye movements that gather the information needed to guide foot placement. However, little is known about how the visual and locomotor systems work together to support movement through the world. We developed a system to simultaneously record gaze and full-body kinematics during locomotion over different outdoor terrains. We found that not only do walkers tune their gaze behavior to the specific information needed to traverse paths of varying complexity but that they do so while maintaining a constant temporal look-ahead window across all terrains. This strategy allows walkers to use gaze to tailor their energetically optimal preferred gait cycle to the upcoming path in order to balance between the drive to move efficiently and the need to place the feet in stable locations. Eye movements and locomotion are intimately linked in a way that reflects the integration of energetic costs, environmental uncertainty, and momentary informational demands of the locomotor task. Thus, the relationship between gaze and gait reveals the structure of the sensorimotor decisions that support successful performance in the face of the varying demands of the natural world. VIDEO ABSTRACT.
Gait recognition can potentially provide a noninvasive and effective biometric authentication from a distance. However, the performance of gait recognition systems will suffer in real surveillance scenarios with multiple interacting individuals and where the camera is usually placed at a significant angle and distance from the floor. We present a methodology for view-invariant monocular 3-D human pose tracking in man-made environments in which we assume that observed people move on a known ground plane. First, we model 3-D body poses and camera viewpoints with a low dimensional manifold and learn a generative model of the silhouette from this manifold to a reduced set of training views. During the online stage, 3-D body poses are tracked using recursive Bayesian sampling conducted jointly over the scene’s ground plane and the pose-viewpoint manifold. For each sample, the homography that relates the corresponding training plane to the image points is calculated using the dominant 3-D directions of the scene, the sampled location on the ground plane and the sampled camera view. Each regressed silhouette shape is projected using this homographic transformation and is matched in the image to estimate its likelihood. Our framework is able to track 3-D human walking poses in a 3-D environment exploring only a 4-D state space with success. In our experimental evaluation, we demonstrate the significant improvements of the homographic alignment over a commonly used similarity transformation and provide quantitative pose tracking results for the monocular sequences with a high perspective effect from the CAVIAR dataset.
- Journal of the American Veterinary Medical Association
- Published almost 7 years ago
Objective-To document the caregiver placebo effect in owners and veterinarians of dogs with lameness from osteoarthritis. Design-Prospective, randomized, double-blinded, placebo-controlled, multicenter clinical trial. Animals-58 dogs with lameness secondary to osteoarthritis. Procedures-Dogs enrolled in the placebo arm of an FDA-approved study were evaluated to determine the relationship between subjective (caregiver responses) and objective (force platform gait analysis) patient outcome measures. Results-A caregiver placebo effect for owners evaluating their dog’s lameness occurred 39.7% of the time. A caregiver placebo effect occurred 44.8% of the time when veterinarians examined dogs for lameness at a walk, 44.8% of the time when veterinarians examined dogs for lameness at a trot, and 43.1% of the time when veterinarians evaluated dogs for signs of pain on palpation of the joint. This effect was significantly enhanced with time. Mean ground reaction forces (GRFs) remained unchanged for dogs during treatment with the placebo. Individually, of 58 dogs, 5 had GRFs that worsened by ≥ 5% over 42 days, 7 had GRFs that improved by ≥ 5% over 42 days, and 46 had GRFs that remained unchanged. Conclusions and Clinical Relevance-A caregiver placebo effect was common in the evaluation of patient response to treatment for osteoarthritis by both pet owners and veterinarians. Force platform gait analysis was an unbiased outcome measure for dogs with lameness from osteoarthritis. A caregiver placebo effect should be considered when interpreting owner and veterinary reports of patient response to treatment.
Gait recovery after neurological disorders requires remastering the interplay between body mechanics and gravitational forces. Despite the importance of gravity-dependent gait interactions and active participation for promoting this learning, these essential components of gait rehabilitation have received comparatively little attention. To address these issues, we developed an adaptive algorithm that personalizes multidirectional forces applied to the trunk based on patient-specific motor deficits. Implementation of this algorithm in a robotic interface reestablished gait dynamics during highly participative locomotion within a large and safe environment. This multidirectional gravity-assist enabled natural walking in nonambulatory individuals with spinal cord injury or stroke and enhanced skilled locomotor control in the less-impaired subjects. A 1-hour training session with multidirectional gravity-assist improved locomotor performance tested without robotic assistance immediately after training, whereas walking the same distance on a treadmill did not ameliorate gait. These results highlight the importance of precise trunk support to deliver gait rehabilitation protocols and establish a practical framework to apply these concepts in clinical routine.
Residual impairments and gait deviations post-stroke may lead to secondary musculoskeletal complications such as arthritis. This study explored the prevalence of arthritis and associated functional limitations in community-dwelling Canadians with and without stroke.
A new tool (OpenGo, Moticon GmbH) was introduced to continuously measure kinetic and temporospatial gait parameters independently through an insole over up to 4 weeks. The goal of this study was to investigate the validity and reliability of this new insole system in a group of healthy individuals.
This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.
Autologous cell transplantation is a promising strategy for repair of the injured spinal cord. Here we have studied the repair potential of mesenchymal stromal cells isolated from the human olfactory mucosa after transplantation into a rodent model of incomplete spinal cord injury. Investigation of peripheral type remyelination at the injury site using immunocytochemistry for P0, showed a more extensive distribution in transplanted compared with control animals. In addition to the typical distribution in the dorsal columns (common to all animals), in transplanted animals only, P0 immunolabelling was consistently detected in white matter lateral and ventral to the injury site. Transplanted animals also showed reduced cavitation. Several functional outcome measures including end-point electrophysiological testing of dorsal column conduction and weekly behavioural testing of BBB, weight bearing and pain, showed no difference between transplanted and control animals. However, gait analysis revealed an earlier recovery of co-ordination between forelimb and hindlimb stepping in transplanted animals. This improvement in gait may be associated with the enhanced myelination in ventral and lateral white matter, where fibre tracts important for locomotion reside. Autologous transplantation of mesenchymal stromal cells from the olfactory mucosa may therefore be therapeutically beneficial in the treatment of spinal cord injury. GLIA 2017.
The Timed Up and Go (TUG) test is widely used to assess locomotion in patients with stroke and is considered to predict the risk of falls. The analysis of locomotor trajectories during the TUG appears pertinent in stroke patients. The aims of this study were i) to analyze locomotor trajectories in patients with stroke during the walking and turning sub-tasks of the TUG, and to compare them with healthy subjects, ii) to determine whether trajectory parameters provide additional information to that provided by the conventional measure (performance time), iii) to compare the trajectory parameters of fallers and non-fallers with stroke and of patients with right and left hemisphere stroke, and iv) to evaluate correlations between trajectory parameters and Berg Balance Scale scores.