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Entry to the poster competition ran during Northumbria's Open Research and Reproducibility Conference 2023. Poster entries showcase open research practice in student work.

Entries to the poster competition ran during Northumbria's Open Research and Reproducibility Conference as part of Open Research Week 2023. Poster entries showcase open research practice in student and academic work. Titles WINNER Positive expressive writing interventions, subjective health and wellbeing: A systematic review, Lauren Hoult, Dr Michael Smith, Prof Mark Wetherell & Dr Trudi Edginton WINNER Micro-detectives: Forensic profiling with microbes, Nengi Ogbanga, Andrew Nelson, Darren Smith & Noemi Procopio WINNER Cementochronology: About the “tree rings” in our teeth, Valentina Perrone, Patrick Randolph-Quinney & Noemi Procopio Larger, more powerful studies: More work. But big rewards!, David Smailes Promote (or learn about) open research via a Reproducibilitea Journal Club, David Smailes To explore how a Breastfeeding Closed Facebook group administered by volunteers with additional breastfeeding training influences women’s experiences, particularly for those women for whom breastfeeding is not their cultural norm, Deborah Wade Impact of symptoms and disease severity on digital mobility outcomes in COPD, D. Megaritis, J. Buekers, T. Bonci, E. Hume, L. Alcock, A. Yarnall, E. M. Amigo, P. Brown, C. Buckley, S. Del Din, C. Echevarria, C. Mazzà, L. Rochester, J. Garcia Aymerich & I. Vogiatzis Growing my research village, Fiona Crouch Dying to Care. A constructivist grounded theory study identifying what factors prepare student nurses to care for dying patients, Helen Merlane Development of Innovative MODular Building System with Enhanced Fire, Environmental, Structural and Thermal Performance (MOD-FEST), Heshachanaa Rajanayagam How does garment cut influence the perception of attractiveness in the male somatotype? A comparative study of the focus of attraction on specific areas of the male body and its adaptation to inform garment cut in the UK, Jenni Moore The more-than-digital scrapmap: Exploring the generative possibilities of qualitative digital data, Joanna Hume Technological visuo-cognitive training in Parkinson’s disease: Preliminary findings from a pilot randomised controlled trial, Julia Das, Gill Barry, Rodrigo Vitorio, Richard Walker, Claire McDonald, Rosie Morris & Samuel Stuart Library support for open research: How the University Library can support you to make your work more open…, Leah Maughan & Rachel Branson A Phenomenological study into the experience of training to perform Intermittent Self Catheterisation (ISC) from the perspective of the Patient and the Nurse, Marion Haskin Be thankful to be joyful: Gratitude writing for wellbeing, Michael A. Smith & Yasmin Colborne Improving cultural understanding and 21st century skills with COIL, Ngoc D. Nguyen, Vilma B. Liwan & Thao T. P. Mai ‘It helps make the fuzzy go away’: Autistic adults’ reflections upon nature and wellbeing during the Covid-19 pandemic and across the life course, Dr Samantha Friedman Diagnosing relativistic electron distributions in the Van Allen radiation belts, S. Killey, I.J. Rae, S. Chakraborty, A.W. Smith, S.N. Bentley, M. R. Bakrania, R. Wainwright, C.E.J. Watt, & J. K. Sandhu

Introduction: The increased use of wearable devices (WDs) for monitoring daily-life activities has led to the development of different location-driven applications. The first fundamental distinction is between indoor and outdoor environments. The most intuitive approach is the analysis of GPS coordinates or Wi-Fi signals [1] but both solutions are power consuming. In this study, we proposed and tested the use of deep learning techniques for indoor/outdoor discrimination based on local magnetic field properties of the specific environment during free-living activities. Methods:Eight participants were recruited in four different centres (Turin, Italy; Sheffield, UK; Newcastle upon Tyne, UK; Tel Aviv, Israel) and were equipped with the INDIP system [2] (including four magneto-inertial units attached to each foot, lower back, and non-dominant wrist), a smartphone (running the Aeqora mobile application) and were monitored during 2.5-hours of daily free-living activities. Magnetometer data was used to train a deep learning model, while indoor/outdoor probability based on GPS coordinates was provided by the Aeqora app and used as a reference. For each WD, the following features were extracted: x, y, z components and norm of the magnetometer and the 10-sample moving average (0.1s window) of the latter features as a “contextual” rating. A bi-layer long short-term memory structure with a linear layer as a tail and with a gaussian error linear unit as activation unit has been implemented [3]. To achieve a lower-bias training and a more robust model, the network has been validated by exploiting a leave one subject out validation approach. In addition, the classification is based on two different observation timeframes: windows of length equal to the magnetometer data acquisition period (0.01s) and 1s windows. Results: The average accuracy of the model, across participants, in the classification of indoor/outdoor environments while using as input one WD at a time and all WDs together is reported in Table 1. Discussion: Based on this preliminary analysis, the model seems suitable for discerning indoor from outdoor environments with an average accuracy score higher than 88.3% in participants spread through three countries (different morphology of the territory, culture, lifestyle, etc.). With this respect, considering a longer observation time (1s vs. 0.01s) has resulted in increasing of the accuracy for all conditions. Overall, the best performances were obtained by using the whole INDIP system, with a 94.1% score. However, very similar performances were obtained when only one WD is considered. The effect of the experimental setup (e.g., the number and position of the WDs) and the input of the model (e.g., the length of the time-observation window) on the performance metrics require further investigations.

Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson’s disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth lumbar vertebrae and two on the ankles. The aims of this study were: (i) to apply deep learning (DL) techniques on wearable sensor data for gait segmentation and quantification in older adults and in people with PD; (ii) to validate the proposed technique for measuring gait against traditional gold standard laboratory reference and a widely used algorithm based on wavelet transforms (WT); (iii) to assess the performance of DL methods in assessing high-level gait characteristics, with focus on stride, stance and swing related features. The results showed a high reliability of the proposed approach, which achieves temporal errors considerably smaller than WT, in particular for the detection of final contacts, with an inter-quartile range below 70 ms in the worst case. This study showes encouraging results, and paves the road for further research, addressing the effectiveness and the generalization of data-driven learning systems for accurate event detection in challenging conditions.