A smart wireless inertial measurement unit system: simplifying & encouraging usage of WIMU technology

Wireless Inertial Measurement Units (WIMUs) combine motion sensing, processing & communications functionsin a single device. Data gathered using these sensors has the potential to be converted into high quality motion data. By outfitting a subject with multiple WIMUs full motion data can begathered. With a potential cost of ownership several orders of magnitude less than traditional camera based motion capture, WIMU systems have potential to be crucially important in supplementing or replacing traditional motion capture and opening up entirely new application areas and potential markets particularly in the rehabilitative, sports & at-home healthcarespaces. Currently WIMUs are underutilized in these areas. A major barrier to adoption is perceived complexity. Sample rates, sensor types & dynamic sensor ranges may need to be adjusted on multiple axes for each device depending on the scenario. As such we present an advanced WIMU in conjunction with a Smart WIMU system to simplify this aspect with 3 usage modes: Manual, Intelligent and Autonomous. Attendees will be able to compare the 3 different modes and see the effects of good andbad set-ups on the quality of data gathered in real time.

Clinical autonomy and nurse/physician collaboration in emergency nurses

Aim: To investigate clinical autonomy and Nurse/Physician collaboration among emergency nurses and the relationship between these concepts, personal characteristics and organisational influences. Background: Nurses have been identified as having a significant role in addressing the challenges of providing modern healthcare. Emergency nurses have reported competence in a wide range of emergency care skills. However, there is evidence that Emergency Department (ED) nurses may have lower levels of clinical autonomy than other areas of practice. Levels of clinical autonomy appear to be influenced by levels of collaboration with physicians and the organisations in which nurses work Methods: A descriptive correlational study using a survey design with a purposive convenience sample of 141 ED staff nurses (response 70.9%) from 3 EDs in Ireland. Data were collected using the Dempster Practice Behaviours Scale (DPBS) the Nurse/Physician Collaboration Scale (NPCS) and the newly developed Organisational Influences on Nursing Scale. Demographic information was also sought from participants. Results: Participants were largely female (87%), relatively young (mean age 35.57, SD=7.83) and educated to degree level (48%) or higher (31%) with 40% posessing specialist emergency nursing qualifications. Participants reported moderate levels of clinical autonomy and Nurse/Physician collaboration. No relationships were found between sample characteristics and clinical autonomy and Nurse/Physician collaboration among emergency nurses. Relationships were found between levels of clinical autonomy and Nurse/Physician collaboration (r=-0.395, n=100, p<0.001), and organisational influence on nursing (r=0.455, p<0.001) and also between Nurse/Physician collaboration and organisational influence on nursing (r=-0.413, p<0.001). Discussion: Clinical autonomy of nurses has been linked with quality outcomes in healthcare. The quest for quality in modern healthcare in a challenging environment should acknowledge that strategies need to focus beyond education and skills provision and include essential elements such as Nurse/Physician collaboration and the organisational influence on nursing to ensure the greater involvement of nurses in patient care.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.