Towards human-level performance on automatic pose estimation of infant spontaneous movements

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1 424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance.Comment: Published in Computerized Medical Imaging and Graphics (CMIG

Mid-range outcomes in 64 consecutive cases of multilevel fusion for degenerative diseases of the lumbar spine

In the treatment of multilevel degenerative disorders of the lumbar spine, spondylodesis plays a controversial role. Most patients can be treated conservatively with success. Multilevel lumbar fusion with instrumentation is associated with severe complications like failed back surgery syndrome, implant failure, and adjacent segment disease (ASD). This retrospective study examines the records of 70 elderly patients with degenerative changes or instability of the lumbar spine treated between 2002 and 2007 with spondylodesis of more than two segments. Sixty-four patients were included; 5 patients had died and one patient was lost to follow-up. We evaluated complications, clinical/radiological outcomes, and success of fusion. Flexion-extension and standing X-rays in two planes, MRI, and/or CT scans were obtained pre-operatively. Patients were assessed clinically using the Oswestry disability index (ODI) and a Visual Analogue Scale (VAS). Surgery performed was dorsolateral fusion (46.9%) or dorsal fusion with anterior lumbar interbody fusion (ALIF; 53.1%). Additional decompression was carried out in 37.5% of patients. Mean follow-up was 29.4±5.4 months. Average patient age was 64.7±4.3 years. Clinical outcomes were not satisfactory for all patients. VAS scores improved from 8.6±1.3 to 5.6±3.0 pre- to post-operatively, without statistical significance. ODI was also not significantly improved (56.1±22.3 pre- and 45.1±26.4 post-operatively). Successful fusion, defined as adequate bone mass with trabeculation at the facets and transverse processes or in the intervertebral segments, did not correlate with good clinical outcomes. Thirty-five of 64 patients (54%) showed signs of pedicle screw loosening, especially of the screws at S1. However, only 7 of these 35 (20%) complained of corresponding back pain. Revision surgery was required in 24 of 64 patients (38%). Of these, indications were adjacent segment disease (16 cases), pedicle screw loosening (7 cases), and infection (one case). At follow-up of 29.4 months, patients with radiographic ASD had worse ODI scores than patients without (54.7 vs. 36.6; P<0.001). Multilevel fusion for degenerative disease still has a high rate of complications, up to 50%. The problem of adjacent segment disease after fusion surgery has not yet been solved. This study underscores the need for strict indication guidelines to perform lumbar spine fusion of more than two levels

The in-motion-app for remote general movement assessment : a multi-site observational study

Objectives To determine whether videos taken by parents of their infants' spontaneous movements were in accordance with required standards in the In-Motion-App, and whether the videos could be remotely scored by a trained General Movement Assessment (GMA) observer. Additionally, to assess the feasibility of using home-based video recordings for automated tracking of spontaneous movements, and to examine parents' perceptions and experiences of taking videos in their homes. Design The study was a multi-centre prospective observational study. Setting Parents/families of high-risk infants in tertiary care follow-up programmes in Norway, Denmark and Belgium. Methods Parents/families were asked to video record their baby in accordance with the In-Motion standards which were based on published GMA criteria and criteria covering lighting and stability of smartphone. Videos were evaluated as GMA 'scorable' or 'non-scorable' based on predefined criteria. The accuracy of a 7-point body tracker software was compared with manually annotated body key points. Parents were surveyed about the In-Motion-App information and clarity. Participants The sample comprised 86 parents/families of high-risk infants. Results The 86 parent/families returned 130 videos, and 121 (96%) of them were in accordance with the requirements for GMA assessment. The 7-point body tracker software detected more than 80% of body key point positions correctly. Most families found the instructions for filming their baby easy to follow, and more than 90% reported that they did not become more worried about their child's development through using the instructions. Conclusions This study reveals that a short instructional video enabled parents to video record their infant's spontaneous movements in compliance with the standards required for remote GMA. Further, an accurate automated body point software detecting infant body landmarks in smartphone videos will facilitate clinical and research use soon. Home-based video recordings could be performed without worrying parents about their child's development

Convolutional networks for video-based infant movement analysis. Towards objective prognosis of cerebral palsy from infant spontaneous movements

Norsk sammendrag Cerebral parese (CP) er en samlebetegnelse på motoriske funksjonsforstyrrelser grunnet skade på hjernen tidlig i barnets utvikling. Det er særlig spedbarn med medisinske risikofaktorer, som for eksempel for tidlig fødsel, pustebesvær og infeksjoner, som står i fare for å utvikle CP. CP har innvirkning på barnets holdning og motorikk, men gir også andre utfordringer og komplikasjoner. Som følge av manglende tidlige symptomer blir ofte ikke diagnosen satt før 1-2 års alder. Tidlig gjenkjenning av CP hos spedbarn er viktig for å kunne starte målrettet behandling, forebygge komplikasjoner og redusere bekymring hos foreldre. Undersøkelse av spedbarnets spontane bevegelser med metoden General Movement Assessment (GMA) kan indikere om et barn har CP allerede før 5 måneders alder. GMA utføres ved observasjon av et spedbarns spontane bevegelser i en video. Ettersom dette avhenger av tilgang til erfarne og trenede observatører er denne undersøkelsen ikke tilgjengelig for alle. Maskinlæringsbasert CP-prediksjon har blitt utforsket som et alternativ til GMA, men foreløpig har man ikke lyktes med å lokalisere de spontane bevegelsene til et spedbarn i en video på en presis måte. Samtidig er man avhengig av menneskelige eksperter for å kunne velge ut relevante egenskaper i spedbarnsbevegelsene og for å utvikle prediksjonsmodeller. Konvolusjonelle nettverk kan tilpasse seg komplekse oppgaver gjennom automatisk utvelgelse av relevante egenskaper ved bruk av tilpassede nettverksarkitekturer. Formålet med denne avhandlingen var å undersøke presisjonen og beregningseffektiviteten til bildebaserte konvolusjonelle nettverk (ConvNets) for lokalisering av spedbarns spontane bevegelser i videoopptak, og å evaluere nøyaktigheten til grafbaserte konvolusjonelle nettverk (GCNs) for prediksjon av CP. Resultatene fra dette doktorgradsarbeidet viser at ConvNets er i stand til å lokalisere spedbarnsbevegelser i video like godt som det et menneske gjør samtidig som videoen prosesseres i sanntid. En GCN-basert prediksjonsmodell for CP kan videre oppnå like god nøyaktighet som det kliniske eksperter gjør ved bruk av GMA ved 3 måneders alder. Prediksjonsmodellen har også svært god evne til å forutsi gående eller ikke-gående funksjon hos barn med CP og å skille mellom spedbarn som utvikler ensidig og tosidig lammelse. Denne avhandlingen viser at konvolusjonelle nettverk kan brukes til videobasert bevegelsesanalyse av spedbarn for nøyaktig automatisk prediksjon av CP. Tidlig og objektiv gjenkjenning av CP hos spedbarn med medisinske risikofaktorer kan inspirere til utvikling av maskinlæringsbasert klinisk beslutningsstøtte og oppmuntre til videre forskning i grenseflaten mellom moderne medisinsk teknologi og klinisk ekspertkunnskap

Infant Body Part Tracking in Videos Using Deep Learning - Facilitating Early Detection of Cerebral Palsy

The breakthrough of Artificial Intelligence with the advent of Deep Learning has opened paths beyond what have earlier been explored. Within the medical domain, there are potentials to improve how problems are addressed and in the quality of the solutions. Computer-based methods have proven to facilitate early detection of cerebral palsy, which can make a difference by enabling treatment that can reduce the extent of disabilities in affected children. As these systems depend on motion patterns, information about the movement of infants must be collected. In this thesis, we propose a method for tracking body parts of infants in video recordings. The developed framework addresses the task by identifying body parts frame by frame. In this way, the approach can be related to existing methods within the domain of Computer Vision, and more specifically the task of Human Pose Estimation. Taking advantage of recent progress in Deep Learning, the proposed Convolutional Neural Network outperforms existing techniques within the field, by localizing body parts of infants more precisely and at the same time operating at a speed of 120 frames per second. A large dataset of 140 700 annotated keypoints is constructed to facilitate this development. The proposed method has the potential of constituting an essential part of a system able to detect cerebral palsy at an age when the brain still has the ability to adapt

Report on consumer receipts data and sample-derived data in exposome research. Public deliverable 2.2

The scope of this paper is on exposome research in the context of the right to health and how the results of exposome research can contribute to a just society while, at the same time, the methods by which exposome research is performed adhere to applicable legal and ethical standards. Section 2 outlines exposome research as an approach to health research that aims to move away from approaches which focus on isolating and assessing singular exposures or risk factors, instead taking an integrated approach to the assessment and impact of exposures in their totality and full complexity. Section 3 defines health and dissects this definition in the right to health care and the right to health protection, the latter being public health. Section 3 further goes on to distinguish public health from the more individually focused 'medicine'. Exposome research is ultimately focused on public health. Section 4 outlines the context in which exposome research is placed within the confines of this paper, namely how the objectives of exposome research can contribute to a just society. Section 4.3 gives an overview of the themes and issues that play a role in the ethically and legally compliant methods of exposome research. Section 5 then gives concrete recommendations on the methods of exposome research and how these should foster trust and enhance participant engagement. Section 6 focuses on a consumer cohort as a concrete example of exposome research and looks at how the ethical and legal challenges were dealt with in this particular use case. The authors also give recommendations on how participant engagement as it is described in the paper could in the future be enhanced in this consumer cohort

Towards human-level performance on automatic pose estimation of infant spontaneous movements

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance

Next Generation Railway System III (NGRS III) - Zusammenfassungen der Projektergebnisse

Das Projekt Next Generation Railway System (NGRS) III wurde in den Jahren 2016 bis einschließlich 2018 am Deutschen Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Verkehrssystemtechnik bearbeitet. Es stellte in diesem Zeitraum das institutionell geförderte Projekt für Forschung zur Eisenbahninfrastruktur, -betrieb und des Gesamtsystems Eisenbahn am DLR dar. Dadurch konnten zahlreiche Themen grundlegend erforscht und vertieft sowie eine Fortführung und Anwendung in Drittmittelprojekten erreicht werden. Der vorliegende Bericht dient der Zusammenstellung der wichtigsten Meilensteinergebnisse: • Weiterentwicklung des DLR-Tools zur integrierten Bewertung (Railonomics®) • Identifikation und Definition von Kennzahlen zur Messung und Bewertung der Betriebsqualität • Beschreibung der Einflussfaktoren auf die Resilienz des Verkehrssystems Eisenbahn • Entwicklung technologischer Zukunftsszenarien für die Bahn von Morgen • Definition eines Technologiebaukastens ETCS 4.0 • Erhöhung des Situationsbewusstseins am Fahrdienstleiter- und Triebfahrzeugführerarbeitsplatz • Systemtests und Testfallableitung aus geeigneten entwickelten Modellen der Leit- und Sicherungstechnik (LST) • Zustandsdiagnose und –prognose zur optimierten Instandhaltung entscheidender Infrastrukturelement

Performance analysis in ski jumping with a differential global navigation satellite system and video-based pose estimation

This study investigated the explanatory power of a sensor fusion of two complementary methods to explain performance and its underlying mechanisms in ski jumping. A differential Global Navigation Satellite System (dGNSS) and a markerless video-based pose estimation system (PosEst) were used to measure the kinematics and kinetics from the start of the in-run to the landing. The study had two aims; firstly, the agreement between the two methods was assessed using 16 jumps by athletes of national level from 5 m before the take-off to 20 m after, where the methods had spatial overlap. The comparison revealed a good agreement from 5 m after the take-off, within the uncertainty of the dGNSS (±0.05m). The second part of the study served as a proof of concept of the sensor fusion application, by showcasing the type of performance analysis the systems allows. Two ski jumps by the same ski jumper, with comparable external conditions, were chosen for the case study. The dGNSS was used to analyse the in-run and flight phase, while the PosEst system was used to analyse the take-off and the early flight phase. The proof-of-concept study showed that the methods are suitable to track the kinematic and kinetic characteristics that determine performance in ski jumping and their usability in both research and practice