324,069 research outputs found
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
A hybrid approach to recognising activities of daily living from object use in the home environment
Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques
A Hybrid Approach to Recognising Activities of Daily Living from Patterns of Objects Use
Over the years the cost of providing assistance and support to the ever-increasing
population of the elderly and the cognitively impaired has become an economic
epidemic. Therefore, the emergence of Ambient Assisted Living (AAL)
has become imperative, as it encourages independent and autonomous living
by providing assistance to the end user by conducting activity and behaviour
recognition. Accurate recognition of Activities of Daily Living (ADL) play
an important role in providing assistance and support to the elderly and cognitively
impaired. Current knowledge-driven and ontology-based techniques
model object concepts from assumptions and everyday common knowledge
of object used for routine activities. Modelling activities from such information
can lead to incorrect recognition of particular routine activities resulting in
possible failure to detect abnormal activity trends. In cases, where such prior
knowledge are not available, such techniques become virtually unemployable.
A significant step in the recognition of activities is the accurate discovery of
the object usage for specific routine activities. This thesis presents a hybrid approach
for automatic consumption of sensor data and associating object usage
to routine activities using Latent Dirichlet Allocation (LDA) topic modelling.
This process enables the recognition of simple activities of daily living from
object usage and interactions in the home environment. In relation to this, the
work in this thesis addresses the problem of discovering object usage as events
and contexts describing specific routine activities, especially where they have
not been predefined. The main contribution is the development of a hybrid
knowledge-driven activity recognition approach which acquires the knowledge
of object usage through activity-object use discovery for the accurate specification
of activities and object concepts. The evaluation of the proposed approach
on the Kasteren and Ordonez datasets show that it yields better results compared
to existing techniques
Unsupervised routine discovery in egocentric photo-streams
The routine of a person is defined by the occurrence of activities throughout
different days, and can directly affect the person's health. In this work, we
address the recognition of routine related days. To do so, we rely on
egocentric images, which are recorded by a wearable camera and allow to monitor
the life of the user from a first-person view perspective. We propose an
unsupervised model that identifies routine related days, following an outlier
detection approach. We test the proposed framework over a total of 72 days in
the form of photo-streams covering around 2 weeks of the life of 5 different
camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted
F-Score for all the users. Thus, we show that our framework is able to
recognise routine related days and opens the door to the understanding of the
behaviour of people
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
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