A probabilistic movement model for shortest path formation in virtual ant-like agents

We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.

A puzzling migratory detour : are fueling conditions in Alaska driving the movement of juvenile sharp-tailed sandpipers?

Making a detour can be advantageous to a migrating bird if fuel-deposition rates at stopover sites along the detour are considerably higher than at stopover sites along a more direct route. One example of an extensive migratory detour is that of the Sharp-tailed Sandpiper (Calidris acuminata), of which large numbers of juveniles are found during fall migration in western Alaska. These birds take a detour of 1500-3400 km from the most direct route between their natal range in northeastern Siberia and nonbreeding areas in Australia. We studied the autumnal fueling rates and fuel loads of 357 Sharp-tailed Sandpipers captured in western Alaska. In early September the birds increased in mass at a rate of only 0.5% of lean body mass day^-1. Later in September, the rate of mass increase was about 6% of lean body mass day^-1, among the highest values found among similar-sized shorebirds around the world. Some individuals more than doubled their body mass because of fuel deposition, allowing nonstop flight of between 7100 and 9800 km, presumably including a trans-oceanic flight to the southern hemisphere. Our observations indicated that predator attacks were rare in our study area, adding another potential benefit of the detour. We conclude that the most likely reason for the Alaskan detour is that it allows juvenile Sharp-tailed Sandpipers to put on large fuel stores at exceptionally high rates.

Movement and force

How – and why – do things move? How do we describe how they move? This chapter looks at ideas and activities concerning movement and force. It deals with two major issues: firstly, ideas children have about motion and the strategies for teaching about motion in the primary school program. This will include some discussion of the different contexts in which movement and force can be studied. Secondly, it looks at the wider context of studying movement and force, linking it with technology and science as a human endeavour.

Reading room: experiments in posture, movement and comprehension

Participants were required to balance on a seesaw while reading texts in the mirror. They read forward, backward, upside-down and mirror texts while seated. They also crouched, twisted and stretched to read texts from floor to ceiling.

Recognition and response to human movement contained in motion capture data using the Self Organising Map

Neural Networks have been used successfully for recognition of human gestures in many applications including analysis of motion capture data. This paper investigates the potential for using the same methods for both recognition and synthesising responses in relation to movement contained in motion capture sequences.

Promoting Fundamental movement skill development and physical activity in early childhood settings : a cluster randomized controlled trial

The aim of this study was to assess the feasibility, acceptability and potential efficacy of a physical activity program for preschool children. A 20-week, 2-arm parallel cluster randomized controlled pilot trial was conducted. The intervention comprised structured activities for children and professional development for staff. The control group participated in usual care activities, which included designated inside and outside playtime. Primary outcomes were movement skill development and objectively measured physical activity. At follow-up, compared with children in the control group, children in the intervention group showed greater improvements in movement skill proficiency, with this improvement statically significant for overall movement skill development (adjust diff. = 2.08, 95% CI 0.76, 3.40; Cohen’s d = 0.47) and significantly greater increases in objectively measured physical activity (counts per minute) during the preschool day (adjust diff. = 110.5, 95% CI 33.6, 187.3; Cohen’s d = 0.46). This study demonstrates that a physical activity program implemented by staff within a preschool setting is feasible, acceptable and potentially efficacious.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Learning people movement model from multiple cameras for behaviour recognition

In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.