Collart, J, Fitch, R & Alempijevic, A 2017, 'Motion States Inference through 3D Shoulder Gait Analysis and Hierarchical Hidden Markov Models', Australasian Conference on Robotics and Automation 2017, Australasian Conference on Robotics and Automation, ARAA, Sydney, Australia, pp. 1-8.
Automatically inferring human intention from
walking movements is an important research
concern in robotics and other fields of study.
It is generally derived from temporal motion
of limb position relative to the body. These
changes can also be reflected in the change of
stance and gait. Conventional systems relying
on gait are usually based on tracking the lower
body motion (hip, foot) and are extracted from
monocular camera data. However, such data
can be inaccessible in crowded environments
where occlusions of the lower body are prevalent.
This paper proposes a novel approach to
utilize upper body 3D-motion and Hierarchical
Hidden Markov Models to estimate human ambulatory
states, such as quietly standing, starting
to walk (gait initiation), walking (gait cycle),
or stopping (gait termination). Methods
have been tested on real data acquired through
a motion capture system where foot measurements
(heels and toes) were used as ground
truth data for labeling the states to train and
test the models. Current results demonstrate
the feasibility of using such a system to infer
lower-body motion states and sub-states
through observations of 3D shoulder motion online.
Our results enable applications in situations
where only upper body motion is readily
Collart, J, Gateau, T, Fabre, E & Tessier, C 2015, 'Human-robot systems facing ethical conflicts: a preliminary experimental protocol', Artificial Intelligence and Ethics: Papers from the 2015 AAAI Workshop, TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-15), AAAI, Austin, Texas USA, pp. 38-44.
Collart, J, Alempijevic, A, Kirchner, N & Zeibots, M 2015, 'Foundation technology for developing an autonomous Complex Dwell-time Diagnostics (CDD) Tool', Australasian Transport Research Forum 2015 Proceedings, Australasian Transport Research Forum, ATRF, Sydney, Australia, pp. 1-13.
As the demand for rail services grows, intense pressure is placed on stations at the centre of rail networks where large crowds of rail passengers alight and board trains during peak periods. The time it takes for this to occur — the dwell-time — can become extended when high numbers of people congest and cross paths. Where a track section is operating at short headways, extended dwell-times can cause delays to scheduled services that can in turn cause a cascade of delays that eventually affect entire networks. Where networks are operating at close to their ceiling capacity, dwell-time management is essential and in most cases requires the introduction of special operating procedures.
This paper details our work towards developing an autonomous Complex Dwell-time Diagnostics (CDD) Tool — a low cost technology, capable of providing information on multiple dwell events in real time. At present, rail operators are not able to access reliable and detailed enough data on train dwell operations and passenger behaviour. This is because much of the necessary data has to be collected manually. The lack of rich data means train crews and platform staff are not empowered to do all they could to potentially stabilise and reduce dwell-times. By better supporting service providers with high quality data analysis, the number of viable train paths can be increased, potentially delaying the need to invest in high cost hard infrastructures such as additional tracks.
The foundation technology needed to create CDD discussed in this paper comprises a 3D image data based autonomous system capable of detecting dwell events during operations and then create business information that can be accessed by service providers in real time during rail operations. Initial tests of the technology have been carried out at Brisbane Central rail station. A discussion of the results to date is provided and their implications for next steps.