{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:10:16Z","timestamp":1760242216266,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,2,1]],"date-time":"2017-02-01T00:00:00Z","timestamp":1485907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: \u201cQuantifying movement demands of AFL football using GPS tracking\u201d). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.<\/jats:p>","DOI":"10.3390\/a10010023","type":"journal-article","created":{"date-parts":[[2017,2,1]],"date-time":"2017-02-01T10:53:53Z","timestamp":1485946433000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5571-644X","authenticated-orcid":false,"given":"James","family":"Lee","sequence":"first","affiliation":[{"name":"Physiolytics Laboratory, School of Psychological and Clinical Sciences, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0810, Australia"}]},{"given":"David","family":"Rowlands","sequence":"additional","affiliation":[{"name":"Sport and Biomedical Engineering Laboratories (SABEL), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia"}]},{"given":"Nicholas","family":"Jackson","sequence":"additional","affiliation":[{"name":"Sport and Biomedical Engineering Laboratories (SABEL), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia"}]},{"given":"Raymond","family":"Leadbetter","sequence":"additional","affiliation":[{"name":"Sport and Biomedical Engineering Laboratories (SABEL), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1246-3638","authenticated-orcid":false,"given":"Tomohito","family":"Wada","sequence":"additional","affiliation":[{"name":"Information Technology Center for Sports Sciences, National Institute of Fitness and Sports, Kanoya 891-2393, Japan"}]},{"given":"Daniel","family":"James","sequence":"additional","affiliation":[{"name":"Sport and Biomedical Engineering Laboratories (SABEL), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia"},{"name":"Centre of Excellence for Applied Sport Science Research, Queensland Academy of Sport, Queensland Sport and Athletics Centre, Kessels Road, Nathan, QLD 4111, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,1]]},"reference":[{"key":"ref_1","unstructured":"James, D.A. (2006). The Engineering of Sport 6, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.jsams.2009.09.002","article-title":"Quantifying movement demands of AFL football using GPS tracking","volume":"13","author":"Wisbey","year":"2010","journal-title":"J. Sci. Med. Sport"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Neville, J., Wixted, A., Rowlands, D., and James, D. (2010, January 7\u201310). Accelerometers: An underutilized resource in sports monitoring. Proceedings of the 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Brisbane, Australia.","DOI":"10.1109\/ISSNIP.2010.5706766"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1027\/0269-8803.21.1.51","article-title":"Sensors and sensor systems for psychophysiological monitoring: A review of current trends","volume":"21","author":"Cutmore","year":"2007","journal-title":"J. Psychophysiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.proeng.2011.05.122","article-title":"iPhone sensor platforms: Applications to sports monitoring","volume":"13","author":"McNab","year":"2011","journal-title":"Proc. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s12283-010-0043-2","article-title":"Validation of trunk mounted inertial sensors for analysing running biomechanics under field conditions, using synchronously collected foot contact data","volume":"12","author":"Wixted","year":"2010","journal-title":"Sports Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12283-012-0107-6","article-title":"Velocity profiling using inertial sensors for freestyle swimming","volume":"16","author":"Stamm","year":"2013","journal-title":"Sports Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1080\/02640414.2014.962577","article-title":"Peak outward acceleration and ball release in cricket","volume":"33","author":"Spratford","year":"2015","journal-title":"J. Sport Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.proeng.2012.04.136","article-title":"Sensor fusion: Let\u2019s enhance the performance of performance enhancement","volume":"34","author":"Lee","year":"2012","journal-title":"Proc. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.proeng.2013.07.072","article-title":"A sports technology needs assessment for performance monitoring in swimming","volume":"60","author":"Ride","year":"2013","journal-title":"Proc. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deng, Z., Yang, P., Zhao, Y., Zhao, X., and Dong, F. (2015, January 26\u201328). Life-Logging Data Aggregation Solution for Interdisciplinary Healthcare Research and Collaboration. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT\/IUCC\/DASC\/PICOM), Liverpool, UK.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.342"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"McGregor, A., Bennett, D., Majumdar, S., Nandy, B., Melendez, J.O., St-Hilaire, M., Lau, D., and Liu, J. (July, January 27). A Cloud-Based Platform for Supporting Research Collaboration. Proceedings of the 2015 IEEE 8th International Conference on Cloud Computing, New York, NY, USA.","DOI":"10.1109\/CLOUD.2015.162"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kambona, K., Boix, E.G., and De Meuter, W. (2013, January 1). An Evaluation of Reactive Programming and Promises for Structuring Collaborative Web Applications. Proceedings of the 7th Workshop on Dynamic Languages and Applications, New York, NY, USA.","DOI":"10.1145\/2489798.2489802"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/19346182.2013.868468","article-title":"A structured approach for technology innovation in sport","volume":"6","author":"Hahn","year":"2013","journal-title":"Sports Technol."},{"key":"ref_15","first-page":"14","article-title":"Validation of a single inertial sensor for measuring running kinematics overground during a prolonged run","volume":"5","author":"Winter","year":"2016","journal-title":"J. Fit. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/j.jbiomech.2016.03.012","article-title":"The development and validation of using inertial sensors to monitor postural change in resistance exercise","volume":"49","author":"Gleadhill","year":"2016","journal-title":"J. Biomech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.proeng.2015.07.242","article-title":"On the use of inertial sensors in educational engagement activities","volume":"1","author":"Espinosa","year":"2015","journal-title":"Proc. Eng."},{"key":"ref_18","unstructured":"The Sports Performance Laboratory, National Institute of Fitness and Sports, Kanoya, Japan. Available online: http:\/\/splab.nifs-k.ac.jp\/function\/ or http:\/\/splab.nifs-k.ac.jp\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.proeng.2011.05.113","article-title":"ADAT: A Matlab toolbox for handling time series athlete performance data","volume":"13","author":"James","year":"2011","journal-title":"Proc. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.proeng.2012.04.069","article-title":"A distributed architecture for storing and processing multi channel multi-sensor athlete performance data","volume":"34","author":"Ride","year":"2012","journal-title":"Proc. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MIC.2010.145","article-title":"Node. js: Using JavaScript to build high-performance network programs","volume":"14","author":"Tilkov","year":"2010","journal-title":"IEEE Internet Comp."},{"key":"ref_22","first-page":"2349","article-title":"Orange: Data mining toolbox in Python","volume":"14","author":"Curk","year":"2013","journal-title":"J. Mach Lng. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/19346182.2012.725410","article-title":"An integrated swimming monitoring system for the biomechanical analysis of swimming strokes","volume":"4","author":"James","year":"2011","journal-title":"Sports Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1080\/19346182.2013.867965","article-title":"Visualization of wearable sensor data during swimming for performance analysis","volume":"6","author":"Rowlands","year":"2013","journal-title":"Sports Technol."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/1\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:27:19Z","timestamp":1760207239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/1\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,1]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["a10010023"],"URL":"https:\/\/doi.org\/10.3390\/a10010023","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2017,2,1]]}}}