5,737 research outputs found

    Classification of sporting activities using smartphone accelerometers

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    In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach

    Anti-Fall: A Non-intrusive and Real-time Fall Detector Leveraging CSI from Commodity WiFi Devices

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    Fall is one of the major health threats and obstacles to independent living for elders, timely and reliable fall detection is crucial for mitigating the effects of falls. In this paper, leveraging the fine-grained Channel State Information (CSI) and multi-antenna setting in commodity WiFi devices, we design and implement a real-time, non-intrusive, and low-cost indoor fall detector, called Anti-Fall. For the first time, the CSI phase difference over two antennas is identified as the salient feature to reliably segment the fall and fall-like activities, both phase and amplitude information of CSI is then exploited to accurately separate the fall from other fall-like activities. Experimental results in two indoor scenarios demonstrate that Anti-Fall consistently outperforms the state-of-the-art approach WiFall, with 10% higher detection rate and 10% less false alarm rate on average.Comment: 13 pages,8 figures,corrected version, ICOST conferenc

    Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

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    Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

    Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

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    The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R) and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy in Madrid, Spain, The foundation de l’avenir, and the Fondation de France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network (TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015– 69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P. Moreno-Muñoz has been supported by FPI grant BES-2016-07762

    The Emerging Wearable Solutions in mHealth

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    The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed

    Internet of Things Enabled Technologies for Behaviour Analytics in Elderly Person Care: A Survey

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    The advances in sensor technology over recent years has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can be worn on the body, potentially creating a network of sensors. Smart phones are also more common in the average household and can also provide some behavioural analysis due to the built in sensors. As a result of this, researchers are able to monitor behaviours in a more natural setting, which can lead to more useful data. This is important for those that may be suffering from mental illness as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However there are various challenges that need to be addressed ranging from issues with sensors to the involvement of human factors. It is vital that these challenges are taken into consideration along with the major behavioural symptoms that can appear in an Elderly Person. For a person suffering with Dementia, the application of sensor technologies can improve the quality of life of the person and also monitor the progress of the disease through behavioural analysis. This paper will consider the behaviours that can be associated with dementia and how these behaviours can be monitored through sensor technology. We will also provide an insight into some sensors and algorithms gathered through survey in order to provide advantages and disadvantages of these technologies as well as to present any challenges that may face future research

    A Review of Physical Human Activity Recognition Chain Using Sensors

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    In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.
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