Determination of a Total Body Model of Efficiency Applied to a Rowing Movement in Humans

Efficiency represents the ratio of work done to energy expended. In human movement, it is desirable to maximise the work done or minimise the energy expenditure. Whilst research has examined the efficiency of human movement for the lower and upper body, there is a paucity of research which considers the efficiency of a total body movement. Rowing is a movement which encompasses all parts of the body to generate locomotion and is a useful modality to measure total body efficiency. It was the aim of this research to develop a total body model of efficiency and explore how skill level of participants and assumptions of the modelling process affected the efficiency estimates Three studies were used to develop and evaluate the efficiency model. Firstly, the efficiency of ten healthy males was established using rowing, cycling and arm cranking. The model included internal work from motion capture and efficiency estimates were comparable to published literature, indicating the suitability of the model to estimate efficiency. Secondly, the model was developed to include a multi-segmented trunk and twelve novice and twelve skilled participants were assessed for efficiency. Whilst the efficiency estimates were similar to published results, novice participants were assessed as more efficient. Issues such as the unique physiology of trained rowers and a lack of energy transfers in the model were considered contributing factors. Finally the model was redeveloped to account for energy transfers, where skilled participants had higher efficiency at large workloads. This work presents a novel model for estimating efficiency during a rowing motion. The specific inclusion of energy transfers expands previous knowledge of internal work and efficiency, demonstrating a need to include energy transfers in the assessment of efficiency of a total body action.

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.