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My Lab: LMAM (lmam.epfl.ch)
The multidisciplinary research of the Laboratory of Movement Analysis and Measurement aims to transfer bioengineering findings into clinical applications. We are particularly interested to characterize sport performances and pathologies affecting motor function such as osteoarthritis, frailty, pain or movement disorder by studying the movement ability.
Our research involves biomechanical instrumentation for measuring and modelling human biodynamics in daily conditions, during spontaneous activity or physical exercises.Based on body worn sensors, we design wearable systems and algorithms for long-term monitoring of physical activity and gait analysis, for the estimation of the 3D joint kinematics and kinetics, and for the sport performance evaluation. This involves advanced signal processing, multi-parametric approach, sensorsfusion and functional calibration methods to devise new methods for activity recognition and to extract relevant disease/health related features hidden in human biomechanical signals.
Based on these features and instruments new metrics are defined and validated to provide early diagnosis and objective clinimetry for outcome evaluation in orthopaedics and aging, to assess the change of motor function with disease and rehabilitation, to characterise improved performances in sport, and to classify movement disorders.
My Lab: LMAM (lmam.epfl.ch)
The multidisciplinary research of the Laboratory of Movement Analysis and Measurement aims to transfer bioengineering findings into clinical applications. We are particularly interested to characterize sport performances and pathologies affecting motor function such as osteoarthritis, frailty, pain or movement disorder by studying the movement ability.
Our research involves biomechanical instrumentation for measuring and modelling human biodynamics in daily conditions, during spontaneous activity or physical exercises.Based on body worn sensors, we design wearable systems and algorithms for long-term monitoring of physical activity and gait analysis, for the estimation of the 3D joint kinematics and kinetics, and for the sport performance evaluation. This involves advanced signal processing, multi-parametric approach, sensorsfusion and functional calibration methods to devise new methods for activity recognition and to extract relevant disease/health related features hidden in human biomechanical signals.
Based on these features and instruments new metrics are defined and validated to provide early diagnosis and objective clinimetry for outcome evaluation in orthopaedics and aging, to assess the change of motor function with disease and rehabilitation, to characterise improved performances in sport, and to classify movement disorders.
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