149,240 research outputs found
Feature space analysis for human activity recognition in smart environments
Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets
Feature space analysis for human activity recognition in smart environments
Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets
A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation
Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback
A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation
Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback
A knowledge-based approach towards human activity recognition in smart environments
For many years it is known that the population of older persons is on the rise. A recent report estimates that globally, the share of the population aged 65 years or over is expected to increase from 9.3 percent in 2020 to around 16.0 percent in 2050 [1]. This point has been one of the main sources of motivation for active research in the domain of human
activity recognition in smart-homes. The ability to perform ADL without assistance from
other people can be considered as a reference for the estimation of the independent living
level of the older person. Conventionally, this has been assessed by health-care domain
experts via a qualitative evaluation of the ADL. Since this evaluation is qualitative, it can
vary based on the person being monitored and the caregiver\u2019s experience. A significant
amount of research work is implicitly or explicitly aimed at augmenting the health-care
domain expert\u2019s qualitative evaluation with quantitative data or knowledge obtained from
HAR. From a medical perspective, there is a lack of evidence about the technology readiness
level of smart home architectures supporting older persons by recognizing ADL [2]. We
hypothesize that this may be due to a lack of effective collaboration between smart-home
researchers/developers and health-care domain experts, especially when considering HAR.
We foresee an increase in HAR systems being developed in close collaboration with caregivers
and geriatricians to support their qualitative evaluation of ADL with explainable quantitative
outcomes of the HAR systems. This has been a motivation for the work in this thesis. The
recognition of human activities \u2013 in particular ADL \u2013 may not only be limited to support
the health and well-being of older people. It can be relevant to home users in general. For
instance, HAR could support digital assistants or companion robots to provide contextually
relevant and proactive support to the home users, whether young adults or old. This has also
been a motivation for the work in this thesis.
Given our motivations, namely, (i) facilitation of iterative development and ease in collaboration between HAR system researchers/developers and health-care domain experts in ADL,
and (ii) robust HAR that can support digital assistants or companion robots. There is a need
for the development of a HAR framework that at its core is modular and flexible to facilitate
an iterative development process [3], which is an integral part of collaborative work that involves develop-test-improve phases. At the same time, the framework should be intelligible
for the sake of enriched collaboration with health-care domain experts. Furthermore, it
should be scalable, online, and accurate for having robust HAR, which can enable many
smart-home applications. The goal of this thesis is to design and evaluate such a framework.
This thesis contributes to the domain of HAR in smart-homes. Particularly the contribution can be divided into three parts. The first contribution is Arianna+, a framework to develop
networks of ontologies - for knowledge representation and reasoning - that enables smart
homes to perform human activity recognition online. The second contribution is OWLOOP,
an API that supports the development of HAR system architectures based on Arianna+. It
enables the usage of Ontology Web Language (OWL) by the means of Object-Oriented
Programming (OOP). The third contribution is the evaluation and exploitation of Arianna+
using OWLOOP API. The exploitation of Arianna+ using OWLOOP API has resulted in four
HAR system implementations. The evaluations and results of these HAR systems emphasize
the novelty of Arianna+
Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe
In order to build better human-friendly human-computer interfaces,
such interfaces need to be enabled with capabilities to perceive
the user, his location, identity, activities and in particular his interaction
with others and the machine. Only with these perception capabilities
can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the
development of novel techniques for the visual perception of humans and
their activities, in order to facilitate perceptive multimodal interfaces,
humanoid robots and smart environments. My work includes research
on person tracking, person identication, recognition of pointing gestures,
estimation of head orientation and focus of attention, as well as
audio-visual scene and activity analysis. Application areas are humanfriendly
humanoid robots, smart environments, content-based image and
video analysis, as well as safety- and security-related applications. This
article gives a brief overview of my ongoing research activities in these
areas
Multioccupant Activity Recognition in Pervasive Smart Home Environments
been the center of lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multi-occupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey paper provides an overview of existing approaches and current practices for activity recognition in multi-occupant smart homes. It presents the latest developments and highlights the open issues in this field
HUMAN ACTIVITY RECOGNITION IN SMART-HOME ENVIRONMENTS FOR HEALTH-CARE APPLICATIONS
With a growing population of elderly people, the number of subjects at risk of cognitive
disorders is rapidly increasing. Many research groups are studying pervasive solutions to
continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care
costs and supporting the medical diagnosis.
Clinicians are interested in monitoring several behavioral aspects for a wide variety of
applications: early diagnosis, emergency monitoring, assessment of cognitive disorders,
etcetera. Among the several behavioral aspects of interest, anomalous behaviors while
performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies
can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The
recognition of such abnormal behaviors relies on robust and accurate ADLs recognition
systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring,
environmental sensors in charge of unobtrusively capturing the interaction of the subject with
the home infrastructure should be preferred.
This thesis presents several contributions on this topic. The major ones are two novel hybrid
ADLs recognition algorithms. The former is supervised while the latter is unsupervised.
Preliminary results, which still need to be confirmed, show that the recognition rate of the
unsupervised method is comparable to the one obtained by the supervised one, with the
great advantage of not requiring the acquisition of an annotated dataset.
Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are
presented. Regarding unobtrusive sensing, we propose a machine learning technique to
detect fine-grained manipulations performed by the inhabitant on household objects
instrumented with tiny accelerometer sensors.
Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors
is presented. Experimental results on several datasets show the effectiveness of all the
proposed techniques
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