The objective characterization of human motion is required in a variety of fields including competitive sports, rehabilitation and the detection of motor deficits. Nowadays, typically human experts evaluate the motor behavior. These evaluations are based on their individual experience which leads to a low inter- and intra-expert reliability. Standardized tests improve on the reliability but are still prone to subjective ratings and require human expert knowledge. This paper presents a novel method to characterize the motor state of Parkinson patients using full body motion capturing data based on a combination of multiple metrics. Our approach merges various metrics with a Random Forest and uses a probabilistic formulation to compute a one-dimensional measure for the performed motion. We present an application of our approach to the problem of relating subject motion to different classes like healthy subjects and Parkinson disease patients with deep brain stimulation switched on or off. In the experimental session we show that our measure leads to high classification rates and high entropy values for real-world data. Besides, we show that our method discriminates between Parkinson's subjects (with and without stimulation) and healthy persons as good as the Unified Parkinson's Disease Rating Scale (UPDRS).

In this work we present the decision support of the EMBalance platform. EMBalance is a platform for the management of balance disorders in terms of diagnosis, treatment and evolution. The EMBalance platform aims to extend existing but generic and currently uncoupled balance modelling activities leading to a multi-scale and patient-specific balance model, which will be incorporated in a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. The diagnosis part of the decision support system uses various data ranging from demographic characteristics to clinical examinations, auditory and vestibular tests. Currently we present some initial technical choices and indicative results of the decision support system for diagnosing balance disorders, based on data mining techniques and clinical guidelines.

In this paper we investigate effects of the mass, kinematic constraints imposed by the joint, and assistance provided by the spring of a pair of quasi-passive knee exoskeletons on the motion of the human body center of mass during normal walking. The exoskeletons implement a spring in parallel with the knee joint in the weight acceptance phase of gait, and allow free rotation during all other phases. We begin with a brief explanation of the exoskeleton design, which employs a friction-based latching mechanism to engage/disengage a spring in parallel with the knee. Additionally, a pair of joint-less mass replicas of the exoskeletons were used to separately investigate the effects of the exoskeleton added mass and articulation. It was found that the exoskeleton mass is the main contributor to the changes in the motion of the center of mass, with more pronounced fluctuations of the center of mass in the mediolateral direction, while the exoskeleton joint and spring had negligible effects over and above those of the mass. Additionally, the exoskeleton mass and assistance conditions respectively resulted in a non-significant increase and a non-significant decrease in the total mechanical work of the body.