Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems.

ALD: adaptive layer distribution for scalable video

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.

ALD: adaptive layer distribution for scalable video

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality. In this paper, we propose Adaptive Layer Distribution (ALD) as a novel scalable media delivery technique that optimises the tradeoff between the streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data is spread amongst all datagrams thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the scalable video, while providing increased resilience to the highest quality layers. Our experimental results show that ALD improves the perceived quality and also reduces the bandwidth demand by up to 36% in comparison to the well-known Multiple Description Coding (MDC) technique.

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SCOPUS: ch.b