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[{"key": "dc.contributor.advisor", "value": "Cronin, Neil", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Avela, Janne", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Romppanen, Vesa", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2021-09-30T05:19:32Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2021-09-30T05:19:32Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2021", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/77969", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "The purpose of this study was to evaluate kinematic analysis repeatability by deep learning\napproach in countermovement jump. Seventy athletes (39 women, 31 men) performed two\nmaximal countermovement jumps in either one session or two separate sessions (jumps\nseparated by two-weeks). The jumps were filmed from lateral and frontal point of view. Video\ndata from 50 athletes were selected randomly to be used for training the deep learning model\nwith DeepLabCut. A total of 10 images were used from every athlete from this training set,\nmeaning that a total of 500 images were used to create the model for frontal view and side view\n(sagittal) videos. The performance of this model was then evaluated by applying it on 11 withinday measurements and 9 between-day measurements again for both frontal and sagittal videos.\nFor frontal view videos, the marker locations were labelled for both sides of the body to\nshoulder (acromion), hip joint (greater trochanter), knee joint (mid-point of patella) ankle joint\n(mid-point between malleoli) and toes (head of shoe). The marker locations of shoulder\n(acromion), hip joint (greater trochanter), knee joint (lateral femoral condyle), ankle joint\n(lateral malleolus) and toes (head of shoe) were manually labelled for sagittal test images. For\nthe sagittal videos, hip, knee and ankle joint angles were calculated by using atan2 function in\nMatlab, and for the frontal view videos, the same was done for the knee and ankle angles. To\ncompensate for misplaced or missing markers, raw data was filtered with a median filter and\nsubsequently with Butterworth 4th order low-pass filter. After filtering, data was further\nprocessed with Matlab by first aligning the curve data of consecutive (trial 1 and trial 2) jumps.\nThen data was cropped according to the movement of knee joint from sagittal plane: start of\ncropping was selected as the point where there was a 5-degree joint angle change from the\ninitial standing position, and the end point was selected as the same calculated value after\nlanding the countermovement jump. Test-retest values were calculated with intraclass\ncorrelation coefficients (ICC) for subjects in the evaluation set. The ICC model used for testretest was single measurement two-way mixed effects with absolute agreement. High mean ICC\nvalues were observed for sagittal within-day joint angles (0.95 \u00b1 0.04 for hip joint, 0.96 \u00b1 0.03\nfor knee joint and 0.95 \u00b1 0.05 for ankle joint). Similar values were found for mean betweenday measurements (0.95 \u00b1 0.03 for hip joint, 0.95 \u00b1 0.07 for knee joint and 0.89 \u00b1 0.08 for ankle\njoint). On the contrary, correlations of joint angle values for frontal plane varied substantially\nmore: For within-day measurements, mean ICC values revealed poor test-retest reliability for\nright knee angle (ICC = 0.43 \u00b1 0.31), and moderate test-retest reliability for left knee (ICC =\n0.68 \u00b1 0.23), right ankle (ICC = 0.62 \u00b1 0.22) and left ankle (ICC = 0.53 \u00b1 0.29) angles. Mean\nbetween-day ICC values demonstrated good (ICC = 0.75 \u00b1 0.10) test-retest reliability for right\nknee angle, moderate test-retest reliability for left ankle angle (0.53 \u00b1 0.17), and poor test-retest\nreliability for left knee (ICC = 0.49 \u00b1 0.27) and right ankle (ICC = 0.34 \u00b1 0.26) angles. These\nresults imply that deep learning approach provides very repeatable measurements for sagittal\njoint angles in countermovement jump, but not as such for frontal plane kinematics. Hence deep\nlearning approach provides an affordable and easy-to-access method to perform repeated\nmeasurements for 2-D motion analysis of countermovement jump and possibly other sports\nmovements filmed from sagittal plane. Further studies on repeatability and the validation of\ndeep learning-based systems are required to prove their accuracy and to provide reliable data\nfor practitioners.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2021-09-30T05:19:32Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2021-09-30T05:19:32Z (GMT). No. of bitstreams: 0\n Previous issue date: 2021", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "45", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "markerless", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "countermovement jump", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "deeplabcut", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Between- and within-day repeatability of markerless 2D motion analysis using deep neural networks", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202109305033", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Liikuntatieteellinen tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Sport and Health Sciences", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Liikunta- ja terveystieteet", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Sport and Health Sciences", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Biomekaniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Biomechanics", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "5012", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "toistettavuus", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "syv\u00e4oppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "biomekaniikka", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "nivelet", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "liikeanalyysi", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "hypp\u00e4\u00e4minen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "liikeoppi", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "algoritmit", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "repeatability", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "deep learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "biomechanics", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "joints (musculoskeletal system)", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "motion analysis", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "jumping", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "kinematics", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": 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