Sami Äyrämö


Docent, PhD (mathematical information technology), MSc (sport sciences)

Head of Digital Health Intelligence Laboratory at Faculty of Information Technology, University of Jyväskylä, Finland

Research Coordinator at Faculty of Information Technology, University of Jyväskylä, Finland

email: sami.ayramo at jyu.fi

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List of publications


Tools, links etc.


Publications and reports

 

           

·       Jauhiainen, S., Kauppi, JP., Krosshaug, T., Bahr, R., Bartsch, J., Äyrämö, S., (2022) Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes. Am J Sports Med. doi: 10.1177/03635465221112095. Epub ahead of print.

·       Prezja, F., Pölönen, I., Äyrämö, S., Ruusuvuori, P. & Kuopio, T. (2022). H&E Multi-Laboratory Staining Variance Exploration with Machine Learning. Applied Sciences, 12, 7511.

·       Niinimäki, E., Paloneva, J., Pölönen, I., Heinonen, A. & Äyrämö, S. (2022). Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data. In: Tuovinen T., Periaux J., Neittaanmäki P. (eds) Computational Sciences and Artificial Intelligence in Industry. Intelligent Systems, Control and Automation: Science and Engineering, vol 76. Springer, Cham.

·       Rautiainen, I., Kauppi, J-P., Ruohonen, T., Karhu, E., Lukkarinen, K. & Äyrämö, S. (2022) Predicting Future Overweight and Obesity from Childhood Growth Data: A Case Study. In: Tuovinen T., Periaux J., Neittaanmäki P. (eds) Computational Sciences and Artificial Intelligence in Industry. Intelligent Systems, Control and Automation: Science and Engineering, vol 76. Springer, Cham.

·       Rautiainen I. & Äyrämö S. (2022) Predicting Overweight and Obesity in Later Life from Childhood Data: A Review of Predictive Modeling Approaches. In: Tuovinen T., Periaux J., Neittaanmäki P. (eds) Computational Sciences and Artificial Intelligence in Industry. Intelligent Systems, Control and Automation: Science and Engineering, vol 76. Springer, Cham.

·       Niemelä, M., Äyrämö, S. & Kärkkäinen, T. (2021). Toolbox for Distance Estimation and Cluster Validation on Data with Missing Values, IEEE Access, doi: 10.1109/ACCESS.2021.3136435.

·       Niinimäki, E., Paloneva, J., Pölönen, I, Lainiala, O., & Äyrämö, S. (2021) New edge detection method for automated feature extraction from knee x-ray images. ECCOMAS Thematic Conference on Computational Science and AI in Industry (CSAI 2021). Abstract.

·       Prezja, F., Kuopio, T., & Äyrämö, S. (2021). Unsupervised analysis of staining quality in histopathological images, ECCOMAS Thematic Conference on Computational Science and AI in Industry (CSAI 2021). Abstract.

·       Nykänen, V., Vasankari, N., Kokko, S., Paloniemi. R., Tapio., P., Tuominen, A., Järvinen, B., & Äyrämö, S. (2021). Clustering association rules with an application to physical activity. ECCOMAS Thematic Conference on Computational Science and AI in Industry (CSAI 2021). Abstract.

·       Jauhiainen, S., Krosshaug, T., Petushek, E., Kauppi, J-P. & Äyrämö, S. (2021) Information Extraction from Binary Skill Assessment Data with Machine Learning, International Journal of Learning Analytics and Artificial Intelligence for Education, 3(1).

·       Joensuu, L., Rautiainen, I., Äyrämö, S., Syväoja, H.J., Kauppi, J-P, Kujala, U., & Tammelin, T.H. (2021). Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning, BMJ Open Sport & Exercise Medicine 2021;7:e001053.

·       Jauhiainen, S., Kauppi, J-P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., Kannus, P., Äyrämö, S. (2021). New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes. International Journal of Sports Medicine, 42(02): 175-182.

·       Leppänen, M., Parkkari, J., Vasankari, T., Äyrämö, S., Kulmala J-P, Krosshaug, T., Kannus, P., Pasanen, K. (2021). Change of Direction Biomechanics in a 180-Degree Pivot Turn and the Risk for Noncontact Knee Injuries in Youth Basketball and Floorball Players. Am J Sports Med. 2021 Aug; 49(10):2651-2658.

·       Rossi, M.K., Pasanen, K., Heinonen, A., Äyrämö, S., Leppänen, M., Myklebust, G., Vasankari, T., Kannus, P. & Parkkari, J., (2021) The standing knee lift test is not a useful screening tool for time loss from low back pain in youth basketball and floorball players, Physical Therapy in Sport, 49, 141-148.

·       Annala, L., Äyrämö, S. & Pölönen, I. (2020) Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion. Applied Sciences, 10, 7097.

·       Girka, A., Kulmala, J-P., & Äyrämö, S. (2020). Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity, Computer Methods in Biomechanics and Biomedical Engineering, 23:14, 1052-1059.

·       Rossi, M.K., Pasanen, K., Heinonen, A., Äyrämö, S., Räisänen, A. M., Leppänen, M., Myklebust, G, Vasankari, T., Kannus, P. & Parkkari, J. (2020), Performance in dynamic movement tasks and occurrence of low back pain in youth floorball and basketball players. BMC Musculoskelet Disord 21, 350.

·       Jauhiainen, S., Pohl, A.J., Äyrämö, S., Kauppi, J-P, & Ferber, R. (2020). A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns. Scand J Med Sci Sports. 2020; 30: 732740.

·       Rahkonen, S., Koskinen, E., Pölönen, I., Heinonen, T., Ylikomi, T., Äyrämö, S., Eskelinen, M.A. (2020). Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks. Journal of Medical Imaging, 7 (2), 024001.

·       Niemelä, M., Kärkkäinen, T., Äyrämö, S., Ronimus, M., Richardson, U., & Lyytinen, H. (2020). Game learning analytics for understanding reading skills in transparent writing system. British Journal of Educational Technology, 51(6), 2376-2390.

·       Leppänen, M., Rossi, M.T., Parkkari, J., Heinonen, A., Äyrämö, S., Krosshaug, T., Vasankari, T., Kannus, P. & Pasanen K. (2020), Altered hip control during a standing knee lift test is associated with increased risk of knee injuries. Scand J Med Sci Sports. 2020; 30: 922931.

·       Rossi., M, Pasanen, K., Heinonen, A, Äyrämö, S., Räisänen, A., Leppänen, M., Myklebust, G., Vasankari, T., Kannus, P., Parkkari, J. (2020). 007 Performance in dynamic movement tasks and occurrence of low back pain in youth floorball and basketball players. British Journal of Sports Medicine Mar 2020, 54 (Suppl 1) A4.

·       Leppänen, M., Rantala, A., Parkkari, J., Vasankari, T., Äyrämö, S., Krosshaug, T., Kannus, P., Heinonen, A., & Pasanen, K. (2020). 054 Cutting technique and risk for non-contact knee injuries in youth basketball and floorball players.

·       Jauhiainen, S., Äyrämö, S., Forsman, H., & Kauppi, J. P. (2019). Talent identification in soccer using a one-class support vector machine. International Journal of Computer Science in Sport, 18(3), 125-136.

·       Saarela, M., Ryynänen, O-P. & Äyrämö, S. (2019), Predicting Hospital Associated Disability from Imbalanced Data Using Supervised Learning, Artificial Intelligence in Medicine 95, 88-95.

·       Leppänen, M., Rossi, M.. Parkkari, J., Heinonen, A., Äyrämö, S., Vasankari, T., Kannus, P., Pasanen, K. (2019). Poor Pelvic Control During A Knee Lift Test Is Associated With Increased Risk Of Knee Injuries: 555 May 29 2:00 PM - 2:15 PM. Medicine & Science in Sports & Exercise. 51(6) Supplement:143, June 2019.

·       Rossi., M, Pasanen, K., Heinonen, A, Äyrämö, S., Räisänen, A., Leppänen, M., Myklebust, G., Vasankari, T., Kannus, P., Parkkari, J. (2019) 5 Frontal plane femoral adduction during single-leg landing and low back pain in young athletes: a prospective profits cohort study, British Journal of Sports Medicine 2019;53:A2.

·       Rautiainen, I. & Äyrämö, S., Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches (2019). arXiv:1911.08361.

·       Moilanen, H., Äyrämö, S., & Kankaanranta, M., Fysiikkaa liikkuen – 7-luokkalaisten oppilaiden ja opettajien kokemuksia kehollisesta opetuksesta fysiikassa, in Tutkimuksesta luokkahuoneisiin: Ainedidaktisia tutkimuksia 15, Suomen ainedidaktisen tutkimusseuran julkaisuja, 2019.

·       Moilanen, H., Äyrämö, S., Jauhiainen, S., & Kankaanranta, M., Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching, Education Research International, vol. 2018, Article ID 3012079, 11 pages, 2018.

·       Moilanen, H., Äyrämö, S., & Kankaanranta, M., Detecting pupils’ opinions on learning physics bodily by unsupervised machine learning, Proceedings of E-Learn 2018 - World Conference on E-Learning, Association for the Advancement of Computing in Education, 2018.

·       Moilanen, H., Äyrämö, S., & Kankaanranta, M., Learning physics outside the classroom by combinating use of tablets and bodily activity, Proceedings of EdMedia + Innovate Learning Conference, Association for the Advancement of Computing in Education, 2018.

·       Rosso, V., Gastaldi, L., Rapp, W., Lindinger, S., Vanlandewijck, Y., Äyrämö, S., & Linnamo, V., Balance perturbations as a measurement tool for trunk impairment in cross country sit skiing, Adapted physical activity quarterly, 2018.

·       Niemelä, M., Äyrämö, S., & Kärkkäinen, T., Comparison of cluster validation indices with missing data, in Proceedings of 26th European Symposium on Artificial Neural Networks (ESANN2018), Bruges (Belgium), 25-27 April 2018.

·       Äyrämö, S. & Neittaanmäki, P. (toim.), Koneoppimispohjaiset tekoälyratkaisut hyvinvointi- ja terveyssovelluksissa, Informaatioteknologian tiedekunnan julkaisuja, No. 42/2017.

·       Äyrämö, S., Pölönen, I., and Eskelinen, M. A.: Clustering incomplete spectral data with robust methods, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 13-17, 2017.

·       Niinimäki, E., Pöyhönen, J., Äyrämö, S., & Neittaanmäki, P., Omadata terveydenhuollon tietointensiivisessä rakenteessa, Informaatioteknologian tiedekunnan julkaisuja, No. 40/2017.

·       Äyrämö, S., Vilmi, N., Mero, A., Piirainen, J., Nummela, A., Pullinen, T., Avela, J., & Linnamo, V. (2017). Maturation-related differences in neuromuscular fatigue after a short-term maximal run., Human Movement, 18(3), 17-25.

·       Niemelä, M., Kulmala, JP., Kauppi, JP., Kosonen, J. & Äyrämö, S., Prediction of active peak force using a multilayer perceptron. Sports Engineering (2017) 20: 213.

·       Leppänen M, Pasanen K, Kujala UM, Vasankari T, Kannus P, Äyrämö S, Krosshaug T, Bahr R, Avela J, Perttunen J, Parkkari J. Stiff Landings Are Associated With Increased ACL Injury Risk in Young Female Basketball and Floorball Players. Am J Sports Med. 2017. 45(2):386-393.

·       Vilmi, N., Äyrämö, S., Nummela, A., Pullinen, T., Linnamo, V.,  Häkkinen, K., & Mero, A., Oxygen Uptake, Acid-Base Balance and Anaerobic Energy System Contribution in Maximal 300 – 400 m Running in Child, Adolescent and Adult Athletes. Journal of Athletic Enhancement. 2016 5(3).

·       Vesterinen, V., Nummela, A., Äyrämö, S., Laine, T., Hynynen, E., Mikkola, J., & Häkkinen, K., Monitoring Training Adaptation With a Submaximal Running Test in Field Conditions, Int J Sports Physiol Perform, 2016,11(3):393-9.

·       Kulmala, J-P., Palosaari, K., & Äyrämö, S., 3D-liikeanalyysi määrittää nivelen kuormituksen. Niveltieto, 3/2014.

·       Kulmala J-P, Äyrämö S, Pasanen K, Avela J, & Parkkari J., Biomechanical factors underlying different knee loading profiles during rearfoot running. Extended abstract. Proceedings of 13th International symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14 – 17 July, 2014, Lausanne, Switzerland. (PDF)

·       Kulmala, J-P, Äyrämö, S., and Avela, J., Knee extensor and flexor dominant gait patterns increase the knee frontal plane moment during walking. J Orthop Res. 2013 Jul;31(7):1013-9.

·       Wartiainen, P., Kärkkäinen, T., Heimbürger, A., and Äyrämö, S., Context-sensitive approach to dynamic visual analytics of energy production processes. Information Modelling and Knowledge Bases XXIV: Frontiers in Artificial Intelligence and Applications, v 251. Peter Vojtas, Yasushi Kiyoki, Takehiro Tokuda, Hannu Jaakkola, and Naofumi Yoshida eds., IOS Press 2013.

·       Äyrämö, S., Vilmi, N., Mero, A., Häkkinen, K, Piirainen, J., Pullinen, T., Nummela, A. and Linnamo, V. Neuromuscular fatigue after short-term maximal run in child, youth, and adult athletes. XIX Congress of the International Society of Electromyography and Kinesiology. Brisbane, Australia, July 19-21, 2012. Abstract.

·       Kulmala J-P, Äyrämö S, Avela J. Associations between different gait strategies and knee adduction moment. 17th annual Congress of the VII European College of Sport Science, 2012:43. Abstract.

·       Wartiainen, P., Kärkkäinen, T., Heimbürger, A., and Äyrämö, S., Position paper: Methods of visual analytics in knowledge mining. In Jaak Henno, Yasushi Kiyoki, Takehiro Tokuda, and Naofumi Yoshida, eds., 21th European-Japanese Conference on Information Modelling and Knowledge Bases, vol. 1. Tallinn University of Technology, 6 2011.

·       Tirronen, V., Äyrämö, S. and Weber, M., Study on the Effects of Pseudorandom Generation Quality on the Performance of Differential Evolution, Proceedings of 10th International Conference on Adaptive and Natural Computing Algorithms, Part I, ICANNGA 2011, LNCS 6593, Springer 2011.

·       Balahur, D., Hiltunen, L. and Äyrämö, S., Gender and ICT – The Comparative Analysis of Finland and Romania, chapter in Women and technological education: A European comparative perspective. The 10 Commends to the policy makers. D. Balahur, and P. Fadjukoff (Eds.), Alexandru Ioan Cuza University Press, 2010.

·       Nieminen, P., Rabin, N., Kärkkäinen, T., Averbuch, A., and Äyrämö, S., Robust Clustering and Neural Network Training with Dimension Reduction for Industrial Use, Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), 3/2010, University of Jyväskylä.

·       Pechenizkiy, M., Ivannikov, A., Äyrämö, S. and Kärkkäinen, T., Towards Better Understanding and Control of CFB-Boilers: Review of Recent Research in Mining Time Series Data, Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), 2/2010, University of Jyväskylä.

·       Mininno, E., Kärkkäinen, T., and Äyrämö, S., Multi-objective Online Optimization using Evolutionary Algorithms, Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), 1/2010, University of Jyväskylä.

·       Äyrämö, S., Pirtala, P., Kauttonen, J, Naveed, K., and Kärkkäinen, T., Mining Road Traffic Accidents, Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), 2/2009, University of Jyväskylä. (PDF)

·       Äyrämö, S. and Kärkkäinen, T., Tiedonlouhinnalla uutta ja yllättävää tietämystä tieliikenneonnettomuuksista (Finnish), Tie & Liikenne, 11/2009.

·       Ivannikov, A., Pechenizkiy, M., Bakker, J., Leino, T., Jegoroff, M., Kärkkäinen, T., and Äyrämö, S.: Online Mass Flow Prediction in CFB Boiler, in Advances in Data Mining. Applications and Theoretical Aspects, 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20-22, 2009. Proceedings, Petra Perner (ed.), LNCS, Vol. 5633, Springer 2009, pp. 206-219.

·       Pylvänen, M., Äyrämö, S., and Kärkkäinen, T., Visualizing Time Series State Changes with Prototype based Clustering, in Proceedings of International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, LNCS, Vol. 5495, Springer 2009.

·       Nurminen, M., Suominen, P., Äyrämö, S., and Kärkkäinen, T.. Applying Semiautomatic Generation of Conceptual Models to Decision Support Systems Domain, in Proceedings of IASTED International Conference on Software Engineering (SE2009), Innsbruck, Austria, February 17-18 2009.

·       Aittokoski, T., Äyrämö, S., and Miettinen, K., Clustering aided approach for expensive multiobjective design optimization, Optimization Methods and Software, 24(2), 2009.

·       Leiviskä, K., Jämsä-Jounela, S-L, Olli, J., and Äyrämö, S., Computational methods and techniques, chapter in Operational decision making in the process industry: Multidisciplinary approach, Mätäsniemi, Teemu (ed.), VTT Tiedotteita - Research Notes: 2442, VTT, Espoo, 2008. (PDF)

·       Nurminen, M., Suominen, P., Äyrämö, S., and Kärkkäinen, T., Use cases for operational decision support system, chapter in Operational decision making in the process industry: Multidisciplinary approach, Mätäsniemi, Teemu (ed.), VTT Tiedotteita - Research Notes: 2442, VTT, Espoo, 2008 (PDF)

·       Äyrämö, S., Kärkkäinen, T., and Majava, K., Robust refinement of initial prototypes for partitioning-based clustering algorithms, in Recent Advances in Stochastic Modeling and Data Analysis, C. H. Skiadas, (eds.), World Scientific, Singapore, 2007, pp. 473-482.__

·       Äyrämö, S., Kärkkäinen, T., and Majava, K., Robust refinement of initial prototypes for partitioning-based clustering algorithms, in Proceedings of XIIth International Conference on Applied Stochastic Models and Data Analysis (ASMDA), C. H. Skiadas, (eds.), Chania, Crete, Greece, May 29 - June 1, 2007.

·       Äyrämö, S., Knowledge Mining using Robust Clustering, PhD thesis (monograph), University of Jyväskylä, 2006.

·       Äyrämö, S. and Kärkkäinen, T. Introduction to partitioning-based clustering methods with a robust example, Reports of the Dept. of Math. Inf. Tech. (Series C. Software and Computational Engineering), 1/2006, University of Jyväskylä, 2006. (PDF)

·       Kärkkäinen, T. and Äyrämö, S., On Computation of Spatial Median for Robust Data Mining , In Proceedings of Sixth Conference on Evolutionary and Deterministic Methods for Design, Optimisation and Control with Applications to Industrial and Societal Problems (EUROGEN 2005), Munich, Germany, September 12 - 14, 2005. (PDF)

·       Kärkkäinen, T. and Äyrämö, S., Robust Clustering Methods for Incomplete and Erroneous Data, in Data Mining V: Data Mining, Text Mining and their Business Applications, 2004.

·       Äyrämö, S., Reaaliaikajärjestelmän mallintaminen UML–Kuvausmenetelmän avulla (Finnish), Pro Gradu -tutkielma (MSc thesis), Jyväskylän yliopisto, 2002. (PDF)