Development of a novel evidence-based automated powered mobility device competency assessment

This paper describes the outcomes of a clinical study to assess the validity of a stand-alone sensor package and algorithms to aid the assessment by an occupational therapist (OT) whether a person has the capacity to safely and effectively operate a powered mobility device such as a wheelchair in their daily activities. The proposed solution consists of a suite of sensors capable of inferring navigational characteristics from the platform it is attached to (e.g. trajectories, map of surroundings, speeds, distance to doors, etc). Such information presents occupational therapists with the ability to augment their own observations and assessments with correlated, quantitative, evidence-based data acquired with the sensor array. Furthermore, OT reviews can take place at the therapist's discretion as the data from the trials is logged. Results from a clinical evaluation of the proposed approach, taking as reference the commonly-used Power-Mobility Indoor Driving Assessment (PIDA) assessment, were conducted at the premises of the Prince of Wales (PoW) Hospital in Sydney by four users, showing consistency with the OT scores, and setting the scene to a larger study with wider targeted participation. © 2013 IEEE

An automated mechanism to characterize wheelchair user performance

This paper proposes a mechanism to derive quantitative descriptions of wheelchair usage as a tool to aid Occupational Therapist with their performance assesment of mobility platform users. This is accomplished by analysing data computed from a standalone sensor package fitted on an wheelchair platform. This work builds upon previous propositions where parameters that could assist in the assessment were recommended to the authors by a qualified occupational therapist (OT). In the current scheme however the task-specific parameters that may provide the most relevant user information for the assessment are automatically revealed through a machine learning approach. Data mining techniques are used to reveal the most informative parameters, and results from three typical classifiers are presented based on learnings from manual labelling of the training data. Trials conducted by healthy volunteers gave classifications with an 81% success rate using a Random Forest classifier, a promising outcome that sets the scene for a potential clinical trial with a larger user pool

European black pine (Pinus nigra)

Technical guidelines are targeted to practical forest managers and provide summarized information on the biology and ecology of tree species, distribution ranges, importance and use, genetic knowledge, threats to genetic diversity and recommendations for long-term genetic conservation. For the full list of Technical guidelines produced, please visit http://www.euforgen.org/publications/technical_guidelines.htm

MALNUTRITION IN THE SURGICAL PATIENTS

The term 'malnutrition' is a broad term used to describe any imbalance in the diet. In 2009 it was confirmed that malnutrition is an urgent health problem. The reasons for which malnutrition may develop are different. Loss on cellular, physical and physiological level happens as a consequence of malnutrition. Studies show that in surgical practice there is malnutrition in 50% of patients and that there is an association between inadequate nutritional status and surgical result. It leads to prolonged treatment, increasing of the level of morbidity and mortality, increased hospital costs, etc. Sometimes malnutrition is unrecognised, untreated and worsened in hospitals. For this reason this paper will elaborate: nutrition and a surgical patient, assessment of a nutritional status, assessment of energy requirements, and enteral and parenteral nutrition in order to determine the conditions and procedures that affect the appearance, recognition and treatment of malnutrition