Quantitative analysis of human motion plays an important role for diagnosis, treatment effects and monitoring of neurological disorders, such as epilepsy, multiple sclerosis or Parkinson's disease. Stationary motion analysis systems in clinical environments allow acquisition of various human motion parameters based on inertial sensors or cameras. However, such systems do not permit to analyze patient movements in everyday-life situations over extended periods of time. Existing systems using portable inertial sensors typically extract coarse-grained movement information, e.g. overall activity indices. In this paper, we describe a machine learning-based method that is able to recognize multiple activities and to track the human full-body pose from wearable inertial sensor data.     «

Quantitative analysis of human motion plays an important role for diagnosis, treatment effects and monitoring of neurological disorders, such as epilepsy, multiple sclerosis or Parkinson's disease. Stationary motion analysis systems in clinical environments allow acquisition of various human motion parameters based on inertial sensors or cameras. However, such systems do not permit to analyze patient movements in everyday-life situations over extended periods of time. Existing systems using port...     »