44,971 research outputs found
Recognising Activities of Daily Life Using Hierarchical Plans
The introduction of the smart home has been seen as a way of
allowing elderly people to lead an independent life for longer, making sure they
remain safe and in touch with their social and care communities. The assistance
could be in the form of helping with everyday tasks, e.g. notifying them when
the milk in the fridge will be finished or institute safeguards to mitigate risks. In
order to achieve this effectively we must know what the elderly person is doing
at any given time. This paper describes a tiered approach to deal with
recognition of activities that addresses the problem of missing sensor events
that can occur while a task is being carried out
Activities of daily life recognition using process representation modelling to support intention analysis
Purpose
– This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge.
Design/methodology/approach
– This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients.
Findings
– A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.
Originality/value
– The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features
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Device-Free, Activity during Daily Life, Recognition Using a Low-Cost Lidar
Device-free or off-body sensing methods, such as Lidar, can be used for location-driven Activities during Daily Life (ADL) recognition without the need for a mobile host such as a human or robot to use on-body location sensors. Because if such an attachment fails, or is not operational (powered up), when such mobile hosts are device free, it still works. Hence, this paper proposes an innovative method for recognizing ADLs using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a low-cost, 360°rotating 2D Lidar device. We researched, developed, deployed and validated the system. The results indicate that it can provide a centimeter-level localization accuracy of 88% when recognizing 17 targeted location-related daily activities
A hierarchal framework for recognising activities of daily life
PhDIn today’s working world the elderly who are dependent can sometimes be
neglected by society. Statistically, after toddlers it is the elderly who are observed
to have higher accident rates while performing everyday activities. Alzheimer’s
disease is one of the major impairments that elderly people suffer from, and leads
to the elderly person not being able to live an independent life due to forgetfulness.
One way to support elderly people who aspire to live an independent life and
remain safe in their home is to find out what activities the elderly person is
carrying out at a given time and provide appropriate assistance or institute
safeguards.
The aim of this research is to create improved methods to identify tasks related to
activities of daily life and determine a person’s current intentions and so reason
about that person’s future intentions. A novel hierarchal framework has been
developed, which recognises sensor events and maps them to significant activities
and intentions. As privacy is becoming a growing concern, the monitoring of an
individual’s behaviour can be seen as intrusive. Hence, the monitoring is based
around using simple non intrusive sensors and tags on everyday objects that are
used to perform daily activities around the home. Specifically there is no use of
any cameras or visual surveillance equipment, though the techniques developed
are still relevant in such a situation.
Models for task recognition and plan recognition have been developed and tested
on scenarios where the plans can be interwoven. Potential targets are people in the
first stages of Alzheimer’s disease and in the structuring of the library of kernel
plan sequences, typical routines used to sustain meaningful activity have been
used. Evaluations have been carried out using volunteers conducting activities of
daily life in an experimental home environment. The results generated from the
sensors have been interpreted and analysis of developed algorithms has been
made. The outcomes and findings of these experiments demonstrate that the
developed hierarchal framework is capable of carrying activity recognition as well
as being able to carry out intention analysis, e.g. predicting what activity they are
most likely to carry out next
Sustaining entrepreneurial business: a complexity perspective on processes that produce emergent practice
This article examines the management practices in an entrepreneurial small firm which sustain the business. Using a longitudinal qualitative case study, four general processes are identified (experimentation, reflexivity, organising and sensing), that together provide a mechanism to sustain the enterprise. The analysis draws on concepts from entrepreneurship and complexity science. We suggest that an entrepreneur’s awareness of the role of these parallel processes will facilitate their approaches to sustaining and developing enterprises. We also suggest that these processes operate in parallel at multiple levels, including the self, the business and inter-firm networks. This finding contributes to a general theory of entrepreneurship. A number of areas for further research are discussed arising from this result
Recognition of activities of daily living from topic model
Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches
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