Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images

Behavior monitoring and classification is a mechanism used to automatically identify or verify individual based on their human detection, tracking and behavior recognition from video sequences captured by a depth camera. In this paper, we designed a system that precisely classifies the nature of 3D body postures obtained by Kinect using an advanced recognizer. We proposed novel features that are suitable for depth data. These features are robust to noise, invariant to translation and scaling, and capable of monitoring fast human bodyparts movements. Lastly, advanced hidden Markov model is used to recognize different activities. In the extensive experiments, we have seen that our system consistently outperforms over three depth-based behavior datasets, i.e., IM-DailyDepthActivity, MSRDailyActivity3D and MSRAction3D in both posture classification and behavior recognition. Moreover, our system handles subject's body parts rotation, self-occlusion and body parts missing which significantly track complex activities and improve recognition rate. Due to easy accessible, low-cost and friendly deployment process of depth camera, the proposed system can be applied over various consumer-applications including patient-monitoring system, automatic video surveillance, smart homes/offices and 3D games

Contextual game design: from interface development to human activity recognition

A dissertação focasse nos seguintes pontos: Criação de diferentes interfaces usando o sensor Kinect, para reablitação de pacientes com cancro de mama. E, reconhecimento de atividade humana (problema derivado aquando a criação das interfaces)

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

White Matter Plasticity in Dancers and Musicians

This dissertation examined training-related brain plasticity by comparing white matter (WM) structure between dancers and musicians and relating the structural changes to dance and music abilities. We focused on the primary motor pathways, to identify potential structural differences between whole-body dance training and specific-effector music training. To this purpose, highly trained dancers and musicians, matched for years of training, were tested on a novel dance imitation task, melody discrimination, and rhythm reproduction. Participants were scanned using magnetic resonance imaging (MRI). WM was analyzed at a whole-brain level in Study 1, using diffusion tensor imaging (DTI). Study 2 used probabilistic tractography to examine the descending motor pathways from the hand, leg, trunk and head regions. In Study 1, dancers showed increased diffusivity and reduced anisotropy in comparison to musicians in regions including the descending motor pathways, the superior longitudinal fasciculus and the corpus callosum, predominantly in the right hemisphere. Consistent with this, in Study 2, dancers had increased diffusivity and greater volume in all portions of the right descending motor pathways, whereas musicians had increased anisotropy, especially in the right hand and trunk/arm tracts. Importantly, in both studies, DTI metrics were positively related with dance and negatively with melody performance. In Study 2, DTI metrics also were negatively associated with age of training start, indicating a direct relation between the structural changes observed and training. Our findings indicate that different types of long-term training have distinct effects on brain structure. In particular, dance training, which engages the whole body, appears to enhance connectivity among a broad range of cortical regions, possibly by increasing axonal diameter and the heterogeneity of fiber orientation. In contrast, music training seems to increase the coherence and packing of the connections linked to the trained effector(s). This dissertation is novel in comparing brain structure between two groups of highly trained performers and in examining multiple DTI metrics concurrently. Further, in Study 2, we developed a novel methodology to segregate the motor cortex into regions corresponding to four main body parts, which could be used by other researchers interested in motor connectivity

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Moving On:Measuring Movement Remotely after Stroke

Most persons with stroke suffer from motor impairment, which restricts mobility on one side, and affects their independence in daily life activities. Measuring recovery is needed to develop individualized therapies. However, commonly used clinical outcomes suffer from low resolution and subjectivity. Therefore, objective biomechanical metrics should be identified to measure movement quality. However, non-portable laboratory setups are required in order to measure these metrics accurately. Alternatively, minimal wearable systems can be developed to simplify measurements performed at clinic or home to monitor recovery. Thus, the goal of the thesis was ‘To identify metrics that reflect movement quality of upper and lower extremities after stroke and develop wearable minimal systems for tracking the proposed metrics’. Section Upper Extremity First, we systematically reviewed literature ( Chapter II ) to identify metrics used to measure reaching recovery longitudinally post-stroke. Although several metrics were found, it was not clear how they differentiated recovery from compensation strategies. Future studies must address this gap in order to optimize stroke therapy. Next, we assessed a ‘valid’ measure for smoothness of upper paretic limb reaching ( Chapter III ), as this was commonly used to measure movement quality. After a systematic review and simulation analyses, we found that reaching smoothness is best measured using spectral arc length. The studies in this section offer us a better understanding of movement recovery in the upper extremity post-stroke. Section Lower Extremity Although metrics that reflect gait recovery are yet to be identified, in this section we focused on developing minimal solutions to measure gait quality. First, we showed the feasibility of 1D pressure insoles as a lightweight alternative for measuring 3D Ground Reaction Forces (GRF) ( Chapter IV ). In the following chapters, we developed a minimal system; the Portable Gait Lab (PGL) using only three Inertial Measurement Units (IMUs) (one per foot and one on the pelvis). We explored the Centroidal Moment Pivot (CMP) point ( Chapter V ) as a biomechanical constraint that can help with the reduction in sensors. Then, we showed the feasibility of the PGL to track 3D GRF ( Chapters VI-VII ) and relative foot and CoM kinematics ( Chapter VIII-IX ) during variable overground walking by healthy participants. Finally, we performed a limited validation study in persons with chronic stroke ( Chapter X ). This thesis offers knowledge and tools which can help clinicians and researchers understand movement quality and thereby develop individualized therapies post-stroke