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

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

Recognizing activities of daily living from patterns and extraction of web knowledge

The ability to infer and anticipate the activities of elderly individuals with cognitive impairment has made it possible to provide timely assistance and support, which in turn allows them to lead an independent life. Traditional non-intrusive activity recognition approaches are dependent on the use of various machine learning techniques to infer activities given the collected object usage data. Current activity recognition approaches are also based on knowledge driven techniques that require extensive modelling of the activities that needs to be inferred. These models can be seen as too restrictive, prescriptive and static as they are based on a finite set of activities. In this paper, we propose a novel “top down” approach to recognising activities based on object usage data, which detects patterns associated with the activity-object relationship and utilizes web knowledge in order to build dynamic activity models based on the objects used to perform the activity. Experimental results using the Kasteren dataset shows it is comparable to existing approaches

Symmetric 3dB filtering power divider with equal output power ratio for communication systems

This paper presents a two-way filtering power divider (FPD) with an equal output power ratio of 1:1. This implies that each of the FPD output port would receive 50% of the power at the input port. To achieve miniaturisation, a common square open-loop resonator is used to distribute energy between the two integrated Chebyshev bandpass filters. In addition to distributing energy, the common resonator also contributes one pole to each integrated bandpass filter (BPF), hence, reducing the number of individual resonating elements used in achieving the integrated FPD. To demonstrate the proposed design technique, a prototype FPD centred at 2.6 GHz with a 3 dB fractional bandwidth of 3% is designed, simulated and presented. The circuit model and microstrip layout results of the FPD show good agreement. The microstrip layout simulation responses show that a less than 1.1dB insertion loss and a greater than 16.5dB in-band return loss were achieved. The overall footprint of the integrated FPD is 37mm by 13mm (i.e. 0.32λg x 0.11λg, for λg = guided-wavelength of the 50Ω microstrip line at 2.6 GHz). The integrated FPD reported in this paper shows some promising merits when compared to similar devices recently reported in literature

Real-time gesture recognition with virtual glove markers

Due to the universal non-verbal natural communication approach that allows for effective communication between humans, gesture recognition technology has been steadily developing over the previous few decades. Many different strategies have been presented in research articles based on gesture recognition to try to create an effective system to send non-verbal natural communication information to computers, using both physical sensors and computer vision. Hyper accurate real-time systems, on the other hand, have only recently began to occupy the study field, with each adopting a range of methodologies due to past limits such as usability, cost, speed, and accuracy. A real-time computer vision-based human-computer interaction tool for gesture recognition applications that acts as a natural user interface is proposed. Virtual glove markers on users hands will be created and used as input to a deep learning model for the real-time recognition of gestures. The results obtained show that the proposed system would be effective in real-time applications including social interaction through telepresence and rehabilitation