Espen Alexander F. Ihlen
Background and activities
Bakgrunn og aktiviteter
Espen A. F. Ihlen is associate Professor at Department of Neuromedicine and Human Movement Science (INB). He has background in Human Movement Science, Informatics, Mathematics, and Physics. His main interest is application of machine learning and artificial intelligence (AI) in clinical movement analysis.
- Bachelor in Human Movement Science (1999-2003)
- Master in Human Movement Science (2003-2007)
- PhD in clinical medicine (2009-2014)
- Signalprocessing of human movement and activity data
- Machine learning used in prediction models and risk assessments of movement related problems
- Non-linear time series analyses focusing on:
- Hilbert spectral analyses
- Wavelet analyses and decompositions
- Multifractal analyses
- Multiplicative cascading processes
- Lyapunov stability
- Fractional stability
- Application of analyses and models to movements of older adults and infants at risk of motor disorders
- DeepInMotion: Explainable artificial intelligent system to discover new infant movement biomarkers for early detection of disease (project period 2021-2025)
- PROMISE: Early diagnosis and intervention in infants at risk of motor disability
- Introduction to signal processing in Matlab (BEV3201)
- Biomechanics (BEV2005)
- Measurement of physical activity (BEV2101)
- BEV2005 - Biomechanics
- BEV2102 - Biomechanics and human movement analysis
- BEV3201 - Introduction to Signal Processing in Matlab
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2022) Towards human-level performance on automatic pose estimation of infant spontaneous movements. Computerized Medical Imaging and Graphics. vol. 95.
- (2021) In-Motion-App for remote General Movement Assessment: a multi-site observational study. BMJ Open. vol. 11 (3).
- (2021) Classical machine learning versus deep learning for the older adults free-living activity classification. Sensors. vol. 21 (14).
- (2021) Performance analysis in ski jumping with a differential global navigation satellite system and video-based pose estimation. Sensors. vol. 21 (16).
- (2021) Effects of Ankle-Foot Orthoses on acceleration and energy cost of walking in children and adolescents with cerebral palsy. Prosthetics and orthotics international. vol. 45 (6).
- (2021) Fully automated clinical movement analysis from videos using skeleton-based deep learning. Gait & Posture. vol. 90.
- (2021) New automatic, efﬁcient, and highly precise tracking of infant spontaneous movements. Developmental Medicine & Child Neurology. vol. 63.
- (2020) Predicting Advanced Balance Ability and Mobility with an Instrumented Timed Up and Go Test. Sensors. vol. 20 (17).
- (2020) Approaching human precision on automatic markerless tracking of human movements. Gait & Posture. vol. 81.
- (2020) EfficientPose: Scalable single-person pose estimation. Applied intelligence (Boston).
- (2019) Physical Activity Classification for Elderly People in Free-Living Conditions. IEEE journal of biomedical and health informatics. vol. 23 (1).
- (2019) Deep Learning‐based infant motion tracking facilitating early detection of cerebral palsy. Developmental Medicine & Child Neurology. vol. 61.
- (2019) Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study. Journal of Clinical Medicine. vol. 9 (1).
- (2019) The predictive accuracy of the general movement assessment for cerebral palsy: A prospective, observational study of high-risk infants in a clinical follow-up setting. Journal of Clinical Medicine. vol. 8 (11).
- (2018) Characteristics of general movements in preterm infants assessed by computer-based video analysis. Physiotherapy Theory and Practice. vol. 34 (4).
- (2018) Development of a gold-standard method for the identification of sedentary, light and moderate physical activities in older adults: Definitions for video annotation. Journal of Science and Medicine in Sport.
- (2018) Improved prediction of falls in community-dwelling older adults through phase-dependent entropy of daily-life walking. Frontiers in Aging Neuroscience. vol. 10.
- (2017) A physical activity reference data-set recorded from older adults using body-worn inertial sensors and video Technology - The ADAPT study data-set. Sensors. vol. 17 (3).
- (2017) Fractional stability of trunk acceleration dynamics of daily-life walking: toward a unified concept of gait stability. Frontiers in Physiology. vol. 8.
- (2016) Video analysis validation of a real-time physical activity detection algorithm based on a single waist mounted tri-axial accelerometer sensor. IEEE Engineering in Medicine and Biology Society. Conference Proceedings. vol. 2016-October.