Classification of sporting activities using smartphone accelerometers

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

Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network

Open Access funding provided by the IReL Consortium. This research was partly funded by Huawei Technologies, Finland. RA and BC are partly funded by Science Foundation Ireland (12/RC/2289_P2). PMG and FBO are supported by grants from the MINECO/FEDER (DEP2016-79512-R) and from the University of Granada, Plan Propio de Investigacion 2016, Excellence actions: Units of Excellence; Scientific Excellence Unit on Exercise and Health (UCEES); Junta de Andalucia, Consejeria de Conocimiento, Investigacion y Universidades and European Regional Development Funds (ref. SOMM17/6107/UGR). JT and JS are partly funded by the Research Council of Norway (249932/F20). AG is supported by a European Research Council Grant (grant number 716657).Background Consumer wearables and smartphone devices commonly offer an estimate of energy expenditure (EE) to assist in the objective monitoring of physical activity to the general population. Alongside consumers, healthcare professionals and researchers are seeking to utilise these devices for the monitoring of training and improving human health. However, the methods of validation and reporting of EE estimation in these devices lacks rigour, negatively impacting on the ability to make comparisons between devices and provide transparent accuracy. Objectives The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The network was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables and smartphones in the estimation of EE. Methods The recommendations were developed through (1) a systematic literature review; (2) an unstructured review of the wider literature discussing the potential factors that may introduce bias during validation studies; and (3) evidence-informed expert opinions from members of the INTERLIVE network. Results The systematic literature review process identified 1645 potential articles, of which 62 were deemed eligible for the final dataset. Based on these studies and the wider literature search, a validation framework is proposed encompassing six key domains for validation: the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. Conclusions The INTERLIVE network recommends that the proposed protocol, and checklists provided, are used to standardise the testing and reporting of the validation of any consumer wearable or smartphone device to estimate EE. This in turn will maximise the potential utility of these technologies for clinicians, researchers, consumers, and manufacturers/ developers, while ensuring transparency, comparability, and replicability in validation.IReL ConsortiumHuawei TechnologiesScience Foundation IrelandEuropean Commission 12/RC/2289_P2Spanish Government DEP2016-79512-RUniversity of Granada, Plan Propio de Investigacion 2016, Excellence actions: Units of Excellence; Scientific Excellence Unit on Exercise and Health (UCEES)Junta de AndaluciaEuropean Commission SOMM17/6107/UGRResearch Council of Norway 249932/F20European Research Council (ERC) 71665

Smartphone App (2kmFIT-App) for Measuring Cardiorespiratory Fitness: Validity and Reliability Study

FBO research activity was supported by the Spanish Ministry of Economy and Competitiveness—MINECO/FEDER DEP2016-79512-R; the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 667302; the University of Granada, Plan Propio de Investigación 2016, Unit of Excellence on Exercise and Health (UCEES); the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades, and European Regional Development Fund (ERDF), ref. SOMM17/6107/ UGR; the EXERNET Research Network on Exercise and Health in Special Populations (DEP2005-00046/ACTI); and the SAMID III network, RETICS, funded by the PN I+D+I 2017-2021 (Spain), ISCIII Sub-Directorate-General for Research Assessment and Promotion, the European Regional Development Fund (ERDF) (Ref. RD16/002). AN was supported by the Ministry of Economy and Competitiveness and the Instituto de Salud Carlos III through the CIBERFES (CB16/10/00239), the Seneca Foundation through the unit of excellence (Grant 19899/GERM/15), and the Ministry of Science Innovation and Universities RTI2018-093528-B-I00 (all of which are cofinanced by FEDER). CCS is supported by the Spanish Ministry of Science, Innovation and Universities (FJC2018-037925-I). The views expressed are those of the authors and do not reflect the official policy or position of the institutions they belong to.Background: There is strong evidence suggesting that higher levels of cardiorespiratory fitness (CRF) are associated with a healthier metabolic profile, and that CRF can serve as a powerful predictor of morbidity and mortality. In this context, a smartphone app based on the 2-km walk test (UKK test) would provide the possibility to assess CRF remotely in individuals geographically distributed around a country or continent, and even between continents, with minimal equipment and low costs. Objective: The overall aim of this study was to evaluate the validity and reliability of 2kmFIT-App developed for Android and iOS mobile operating systems to estimate maximum oxygen consumption (VO2max) as an indicator of CRF. The specific aims of the study were to determine the validity of 2kmFIT-App to track distance and calculate heart rate (HR). Methods: Twenty participants were included for field-testing validation and reliability analysis. The participants completed the UKK test twice using 2kmFIT-App. Distance and HR were measured with the app as well as with accurate methods, and VO2max was estimated using the UKK test equation. Results: The validity results showed the following mean differences (app minus criterion): distance (& ndash;70.40, SD 51.47 meters), time (& ndash;0.59, SD 0.45 minutes), HR (& ndash;16.75, SD 9.96 beats/minute), and VO2max (3.59, SD 2.01 ml/kg/min). There was moderate validity found for HR (intraclass correlation coefficient [ICC] 0.731, 95% CI & ndash;0.211 to 0.942) and good validity found for VO2max (ICC 0.878, 95% CI & ndash;0.125 to 0.972). The reliability results showed the following mean differences (retest minus test): app distance (25.99, SD 43.21 meters), app time (& ndash;0.15, SD 0.94 seconds), pace (& ndash;0.18, SD 0.33 min/km), app HR (& ndash;4.5, 13.44 beats/minute), and app VO2max (0.92, SD 3.04 ml/kg/min). There was good reliability for app HR (ICC 0.897, 95% CI 0.742-0.959) and excellent validity for app VO2max (ICC 0.932, 95% CI 0.830-0.973). All of these findings were observed when using the app with an Android operating system, whereas validity was poor when the app was used with iOS. Conclusions: This study shows that 2kmFIT-App is a new, scientifically valid and reliable tool able to objectively and remotely estimate CRF, HR, and distance with an Android but not iOS mobile operating system. However, certain limitations such as the time required by 2kmFIT-App to calculate HR or the temperature environment should be considered when using the app.Spanish Ministry of Economy and Competitiveness-MINECO/FEDER DEP2016-79512-REuropean Commission 667302University of Granada, Plan Propio de Investigacion 2016, Unit of Excellence on Exercise and Health (UCEES)Junta de AndaluciaEuropean Commission SOMM17/6107/UGR RD16/002EXERNET Research Network on Exercise and Health in Special Populations DEP2005-00046/ACTISAMID III network, RETICS - PN I+D+I 2017-2021 (Spain)ISCIII Sub-Directorate-General for Research Assessment and PromotionMinistry of Economy and Competitiveness CB16/10/00239Instituto de Salud Carlos III through the CIBERFES CB16/10/00239Fundacion Seneca 19899/GERM/15Ministry of Science Innovation and Universities - FEDER RTI2018-093528-B-I00Spanish Ministry of Science, Innovation and Universities FJC2018-037925-

Rethinking infrastructure design: Evaluating pedestrians and VRUs' psychophysiological and behavioral responses to different roadway designs

The integration of human-centric approaches has gained more attention recently due to more automated systems being introduced into our built environments (buildings, roads, vehicles, etc.), which requires a correct understanding of how humans perceive such systems and respond to them. This paper introduces an Immersive Virtual Environment-based method to evaluate the infrastructure design with psycho-physiological and behavioral responses from the vulnerable road users, especially for pedestrians. A case study of pedestrian mid-block crossings with three crossing infrastructure designs (painted crosswalk, crosswalk with flashing beacons, and a smartphone app for connected vehicles) are tested. Results from 51 participants indicate there are differences between the subjective and objective measurement. A higher subjective safety rating is reported for the flashing beacon design, while the psychophysiological and behavioral data indicate that the flashing beacon and smartphone app are similar in terms of crossing behaviors, eye tracking measurements, and heart rate. In addition, the smartphone app scenario appears to have a lower stress level as indicated by eye tracking data, although many participants don't have prior experience with it. Suggestions are made for the implementation of new technologies, which can increase public acceptance of new technologies and pedestrian safety in the future

Comparing Evaluation Methods for Encumbrance and Walking on Interaction with Touchscreen Mobile Devices

In this paper, two walking evaluation methods were compared to evaluate the effects of encumbrance while the preferred walking speed (PWS) is controlled. Users frequently carry cumbersome objects (e.g. shopping bags) and use mobile devices at the same time which can cause interaction difficulties and erroneous input. The two methods used to control the PWS were: walking on a treadmill and walking around a predefined route on the ground while following a pacesetter. The results from our target acquisition experiment showed that for ground walking at 100% of PWS, accuracy dropped to 36% when carrying a bag in the dominant hand while accuracy reduced to 34% for holding a box under the dominant arm. We also discuss the advantages and limitations of each evaluation method when examining encumbrance and suggest treadmill walking is not the most suitable approach to use if walking speed is an important factor in future mobile studies