A survey of human motion analysis using depth imagery

Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, but recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions.

Tracking-as-recognition for articulated full-body human motion analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Time-frequency analysis of human motion during rhythmic exercises

Biomechanical signals due to human movements during exercise are represented in time-frequency domain using Wigner Distribution Function (WDF). Analysis based on WDF reveals instantaneous spectral and power changes during a rhythmic exercise. Investigations were carried out on 11 healthy subjects who performed 5 cycles of sun salutation, with a body-mounted Inertial Measurement Unit (IMU) as a motion sensor. Variance of Instantaneous Frequency (I.F) and Instantaneous Power (I.P) for performance analysis of the subject is estimated using one-way ANOVA model. Results reveal that joint Time-Frequency analysis of biomechanical signals during motion facilitates a better understanding of grace and consistency during rhythmic exercise.

"I might be a duck, but I'm human" : an analysis of clothing in Disney cartoons

Mickey Mouse, one of the world's most recognizable cartoon characters, did not wear a shirt in his earliest incarnation in theatrical shorts and, for many years, Donald Duck did not wear pants and still rarely does so. Especially when one considers the era in which these figures were first created by the Walt Disney Studio, in the 1920s and 1930s, why are they portrayed without full clothing? The obvious answer, of course, is that they are animals, and animals do not wear clothes. But these are no ordinary animals: in most cases, they do wear clothing - some clothing, at least - and they walk on two legs, talk in a more or less intelligible fashion, and display a number of other anthropomorphic traits. If they are essentially animals, why do they wear clothing at all? On the other hand, if these characters are more human than animal, as suggested by other behavioral traits - they walk, talk, work, read, and so on - why are they not more often fully clothed? To answer these questions I undertook three major research strategies used to gather evidence: interpretive textual analysis of 321 cartoons; secondary analysis of interviews conducted with the animators who created the Disney characters; and historical and archival research on the Disney Company and on the times and context in which it functioned. I was able to identify five themes that played a large part in what kind of clothing a character wore; first, the character's gender and/or sexuality; second, what species or "race" the character was; third, the character's socio-economic status; fourth, the degree to which the character was anthropomorphized; and, fifth, the context in which the character and its clothing appeared in a particular scene or narrative. I concluded that all of these factors played a part in determining, to some extent, the clothing worn by particular characters at particular times. However, certain patterns emerged from the analysis that could not be explained by these factors alone or in combination. Therefore, my analysis also investigates the individual and collective attitudes and desires of the men in the Disney studio who were responsible for creating these characters and the cultural conditions under which they were created. Drawing on literature from the psychoanalytic approach to film studies, I argue that the clothing choices spoke to an idealized fantasy world to which the animators (most importantly, Walt Disney himself), and possibly wider society, wanted to return.

Toward a broader appreciation of human motion in education

Motion is a fundamental activity for the healthy functioning human organism. Its importance, however, is increasingly de-valued in Western cultures as they speed toward adopting technologies and virtual experiences as adjuncts to, and even replacements for7 traditional educational structures and processes that involve physical activity. Organised and reflective experience of human motion is becoming increasingly marginalised in teaching methodologies and learning programs in educational institutions at all levels around the globe. This inquiry sets out to gain a greater understanding of why people and human motion become disconnected, particularly during periods of formal education. A central question and two sub-questions form the basis of the inquiry. The central question asks why human motion is not valued and more utilised in education. In particular, why do learning areas that directly represent involvement with human motion, such as physical education, continually struggle in education programs. It directs the investigation to focus on the causes rather than the symptoms of the disuse and devaluation of human motion in Australian education. The two sub-questions split the praxis of the study. The first seeks to understand how the causes of devaluation work in the educational context lo affect the lack of acknowledgement; and the second considers ways to counter the disuse of human movement in education programs. To address these questions, the research focuses on rebutting the notion of a mind-body dualism. Rather, it seeks to better understand how humans learn and function as monists - integrated beings, acquiring self-knowledge in their 'world of being' in which bodily and emotional experiences, and reasoning are inextricably intertwined. I have approached this qualitative research as an ethnographic sociologist examining the issues from a critical high modernist perspective in order to demonstrate the pervading influence in Australian education of strong beliefs and values from the era of Enlightenment. Narrative analysis of 'memoir' in the form of self-defining memories was selected to gain a sensibility of the connectedness between human emotion, motion and reasoning in the lived experiences of students in three primary and three secondary schools across Years 2-12. An opportunity for human movement to be more valued and utilised in emerging educational frameworks that have life knowledge, dispositions and capabilities at their core is identified. The inquiry proposes a conceptualisation of human motion in education for new times characterised by the need for people to develop personal resources and strong positive identities in order to cope with a world of rapid change and uncertainty.

Upper Limb Portable Motion Analysis System Based on Inertial Technology for Neurorehabilitation Purposes

Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

Time-motion analysis in women’s team handball: importance of aerobic performance

Women’s handball is a sport, which has seen an accelerated development over the last decade. Data on movement patterns in combination with physiological demands are nearly nonexistent in the literature. The aim of this study was twofold: first, to analyze the horizontal movement pattern, including the sprint acceleration profiles, of individual female elite handball players and the corresponding heart rates (HRs) during a match and secondly to determine underlying correlations with individual aerobic performance. Players from one German First League team (n = 11) and the Norwegian National Team (n = 14) were studied during one match using the Sagit system for movement analysis and Polar HR monitoring for analysis of physiological demands. Mean HR during the match was 86 % of maximum HR (HRmax). With the exception of the goalkeepers (GKs, 78 % of HRmax), no position-specific differences could be detected. Total distance covered during the match was 4614 m (2066 m in GKs and 5251 m in field players (FPs)). Total distance consisted of 9.2 % sprinting, 26.7 % fast running, 28.8 % slow running, and 35.5 % walking. Mean velocity varied between 1.9 km/h (0.52 m/s) (GKs) and 4.2 km/h (1.17 m/s) (FPs, no position effect). Field players with a higher level of maximum oxygen uptake (V̇O2max) executed run activities with a higher velocity but comparable percentage of HRmax as compared to players with lower aerobic performance, independent of FP position. Acceleration profile depended on aerobic performance and the field player’s position. In conclusion, a high V̇O2max appears to be important in top-level international women’s handball. Sprint and endurance training should be conducted according to the specific demands of the player’s position.

RF MIMO Systems for Wide-Area Indoor Human Motion Monitoring

Human motion monitoring is an important function in numerous applications. In this dissertation, two systems for monitoring motions of multiple human targets in wide-area indoor environments are discussed, both of which use radio frequency (RF) signals to detect, localize, and classify different types of human motion. In the first system, a coherent monostatic multiple-input multiple-output (MIMO) array is used, and a joint spatial-temporal adaptive processing method is developed to resolve micro-Doppler signatures at each location in a wide-area for motion mapping. The downranges are obtained by estimating time-delays from the targets, and the crossranges are obtained by coherently filtering array spatial signals. Motion classification is then applied to each target based on micro-Doppler analysis. In the second system, multiple noncoherent multistatic transmitters (Tx's) and receivers (Rx's) are distributed in a wide-area, and motion mapping is achieved by noncoherently combining bistatic range profiles from multiple Tx-Rx pairs. Also, motion classification is applied to each target by noncoherently combining bistatic micro-Doppler signatures from multiple Tx-Rx pairs. For both systems, simulation and real data results are shown to demonstrate the ability of the proposed methods for monitoring patient repositioning activities for pressure ulcer prevention.

Constraints on the complete optimization of human motion

In sport and exercise biomechanics, forward dynamics analyses or simulations have frequently been used in attempts to establish optimal techniques for performance of a wide range of motor activities. However, the accuracy and validity of these simulations is largely dependent on the complexity of the mathematical model used to represent the neuromusculoskeletal system. It could be argued that complex mathematical models are superior to simple mathematical models as they enable basic mechanical insights to be made and individual-specific optimal movement solutions to be identified. Contrary to some claims in the literature, however, we suggest that it is currently not possible to identify the complete optimal solution for a given motor activity. For a complete optimization of human motion, dynamical systems theory implies that mathematical models must incorporate a much wider range of organismic, environmental and task constraints. These ideas encapsulate why sports medicine specialists need to adopt more individualized clinical assessment procedures in interpreting why performers' movement patterns may differ.