This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like in modern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user’s physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets.

Objective The aim of the study was to evaluate a technological solution in the form of an App to implement and measure person-centredness in nursing. The focus was to enhance the knowledge transfer of a set of person-centred key performance indicators and the corresponding measurement framework used to inform improvements in the experience of care. Design The study used an evaluation approach derived from the work of the Medical Research Council to assess the feasibility of the App and establish the degree to which the App was meeting the aims set out in the development phase. Evaluation data were collected using focus groups (n = 7) and semi-structured interviews (n = 7) to capture the impact of processes experienced by participating sites. Setting The study was conducted in the UK and Australia in two organizations, across 11 participating sites. Participants 22 nurses from 11 sites in two large health care organizations were recruited on a voluntary basis. Intervention Implementing the KPIs and measurement framework via the APP through two cycles of data collection. Main Outcome Measures The main outcome was to establish feasibility in the use of the App. Results The majority of nurse/midwife participants found the App easy to use. There was broad consensus that the App was an effective method to measure the patient experience and generated clear, concise reports in real time. Conclusions The implementation of the person-centred key performance indicators using the App enhanced the generation of meaningful data to evidence patient experience across a range of different clinical settings.

Nile tilapia (Oreochromis niloticus) is a globally significant aquaculture species rapidly gaining status as a farmed commodity. In West Africa, wild Nile tilapia genetic resources are abundant yet knowledge of fine-scale population structure and patterns of natural genetic variation are limited. Coinciding with this is a burgeoning growth in tilapia aquaculture in Ghana and other countries within the region underpinned by locally available genetic resources. Using 192 single nucleotide polymorphism (SNP) markers this study conducted a genetic survey of Nile tilapia throughout West Africa, sampling 23 wild populations across eight countries (Benin, Burkina Faso, Côte d’Ivoire, Ghana, Togo, Mali, Gambia and Senegal), representing the major catchments of the Volta, Niger, Senegal and Gambia River basins. A pattern of isolation-by-distance and significant spatial genetic structure was identified throughout West Africa (Global FST = 0.144), which largely corresponds to major river basins and, to a lesser extent, sub-basins. Two populations from the Gambia River (Kudang and Walekounda), one from the western Niger River (Lake Sélingué) and one from the upper Red Volta River (Kongoussi) showed markedly lower levels of diversity and high genetic differentiation compared to all other populations, suggesting genetically isolated populations occurring across the region. Genetic structure within the Volta Basin did not always follow the pattern expected for sub-river basins. This study identifies clear genetic structuring and differentiation amongst West African Nile tilapia populations, which concur with broad patterns found in previous studies. In addition, we provide new evidence for fine-scale genetic structuring within the Volta Basin and previously unidentified genetic differences of populations in Gambia. The 192 SNP marker suite used in this study is a useful tool for differentiating tilapia populations and we recommend incorporating this marker suite into future population screening of O. niloticus. Our results form the basis of a solid platform for future research on wild tilapia genetic resources in West Africa, and the identification of potentially valuable germplasm for use in ongoing breeding programs for aquaculture.

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