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Abstract: Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors' view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

Intro -- Preface -- Organization -- Contents -- Smart Homes, Smart Urban Spaces and New Assistive Living Space Concepts in the Smart City -- Multi-resident Location Tracking in Smart Home through Non-wearable Unobtrusive Sensors -- 1 Introduction -- 2 Smarter Safer Home Platform -- 2.1 Overview of SSH Platform -- 2.2 Ground Truth Collection Through Bluetooth Localisation -- 3 Probabilistic Models for Multi-resident Location Tracking -- 3.1 Multi-tracker with Naive Bayes -- 3.2 Multi-tracker with Hidden Markov Models -- 4 Experiments -- 5 Conclusion and Future Work -- References -- People Tracking in Ambient Assisted Living Environments Using Low-Cost Thermal Image Cameras -- 1 Introduction -- 2 Related Work -- 3 System Overview -- 4 Detailed System Description -- 4.1 Sensorial Equipment -- 4.2 Preprocessing of Raw Images -- 4.3 Data Association -- 4.4 Monte-Carlo Particle Filter Tracking -- 5 Experimental Evaluation -- 6 Conclusions and Future Work -- References -- A Preprocessing Algorithm to Increase OCR Performance on Application Processor-Centric FPGA Architec ... -- Abstract -- 1 Introduction -- 2 Related Works -- 3 The Specification -- 4 The Algorithm -- 5 Results -- 6 Conclusion -- Acknowledgement -- References -- E-Health for Future Smart Cities -- DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Deep Learning, Convolutional Neural Network (CNN), and Their Applications to Visual-Based Food Image Recognition -- 3.2 Proposed CNN-Based Approach for Visual-Based Food Image Recognition -- 4 Experimental Results -- 4.1 Experimental Results on UEC-256 -- 4.2 Experimental Results on Food-101 -- 4.3 The Employment of Bounding Box -- 4.4 Running Time -- 5 Conclusion -- Acknowledgments -- References.

INTRODUCTION: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device. METHODS AND ANALYSIS: This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs. After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment. ETHICS AND DISSEMINATION: The study has been granted ethics approval by the centre's committees (London—Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv ...