112 research outputs found
Routine Modeling with Time Series Metric Learning
version éditeur : https://rd.springer.com/chapter/10.1007/978-3-030-30484-3_47International audienceTraditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines
Exploring Natural Language Processing Methods for Interactive Behaviour Modelling
Analysing and modelling interactive behaviour is an important topic in
human-computer interaction (HCI) and a key requirement for the development of
intelligent interactive systems. Interactive behaviour has a sequential
(actions happen one after another) and hierarchical (a sequence of actions
forms an activity driven by interaction goals) structure, which may be similar
to the structure of natural language. Designed based on such a structure,
natural language processing (NLP) methods have achieved groundbreaking success
in various downstream tasks. However, few works linked interactive behaviour
with natural language. In this paper, we explore the similarity between
interactive behaviour and natural language by applying an NLP method, byte pair
encoding (BPE), to encode mouse and keyboard behaviour. We then analyse the
vocabulary, i.e., the set of action sequences, learnt by BPE, as well as use
the vocabulary to encode the input behaviour for interactive task recognition.
An existing dataset collected in constrained lab settings and our novel
out-of-the-lab dataset were used for evaluation. Results show that this natural
language-inspired approach not only learns action sequences that reflect
specific interaction goals, but also achieves higher F1 scores on task
recognition than other methods. Our work reveals the similarity between
interactive behaviour and natural language, and presents the potential of
applying the new pack of methods that leverage insights from NLP to model
interactive behaviour in HCI
A review of computer vision-based approaches for physical rehabilitation and assessment
The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered
Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming
Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management
Learning Agile Bipedal Motions on a Quadrupedal Robot
Can a quadrupedal robot perform bipedal motions like humans? Although
developing human-like behaviors is more often studied on costly bipedal robot
platforms, we present a solution over a lightweight quadrupedal robot that
unlocks the agility of the quadruped in an upright standing pose and is capable
of a variety of human-like motions. Our framework is with a bi-level structure.
At the low level is a motion-conditioned control policy that allows the
quadrupedal robot to track desired base and front limb movements while
balancing on two hind feet. The policy is commanded by a high-level motion
generator that gives trajectories of parameterized human-like motions to the
robot from multiple modalities of human input. We for the first time
demonstrate various bipedal motions on a quadrupedal robot, and showcase
interesting human-robot interaction modes including mimicking human videos,
following natural language instructions, and physical interaction
Exploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639
Modeling Time-Series and Spatial Data for Recommendations and Other Applications
With the research directions described in this thesis, we seek to address the
critical challenges in designing recommender systems that can understand the
dynamics of continuous-time event sequences. We follow a ground-up approach,
i.e., first, we address the problems that may arise due to the poor quality of
CTES data being fed into a recommender system. Later, we handle the task of
designing accurate recommender systems. To improve the quality of the CTES
data, we address a fundamental problem of overcoming missing events in temporal
sequences. Moreover, to provide accurate sequence modeling frameworks, we
design solutions for points-of-interest recommendation, i.e., models that can
handle spatial mobility data of users to various POI check-ins and recommend
candidate locations for the next check-in. Lastly, we highlight that the
capabilities of the proposed models can have applications beyond recommender
systems, and we extend their abilities to design solutions for large-scale CTES
retrieval and human activity prediction. A significant part of this thesis uses
the idea of modeling the underlying distribution of CTES via neural marked
temporal point processes (MTPP). Traditional MTPP models are stochastic
processes that utilize a fixed formulation to capture the generative mechanism
of a sequence of discrete events localized in continuous time. In contrast,
neural MTPP combine the underlying ideas from the point process literature with
modern deep learning architectures. The ability of deep-learning models as
accurate function approximators has led to a significant gain in the predictive
prowess of neural MTPP models. In this thesis, we utilize and present several
neural network-based enhancements for the current MTPP frameworks for the
aforementioned real-world applications.Comment: Ph.D. Thesis (2022
- …