This work presents a description of the EXOSMOOTH project, oriented to the benchmarking of lower limb exoskeletons performance. In the field of assisted walking by powered lower limb exoskeletons, the EXOSMOOTH project proposes an experiment that targets two scientific questions. The first question is related to the effectiveness of a novel control strategy for smooth assistance. Current assist strategies are based on controllers that switch the assistance level based on the gait segmentation provided by a finite state machine. The proposed strategy aims at managing phase transitions to provide a smoother assistance to the user, thus increasing the device transparency and comfort for the user. The second question is the role of the actuation at the ankle joint in assisted walking. Many novel exoskeletons devised for industrial applications do not feature an actuated ankle joint. In the EXOSMOOTH project, the ankle joint actuation will be one experimental factor to have a direct assessment of the role of an actuated joint in assisted walking. Preliminary results of 15 healthy subjects walking at different speeds while wearing a lower limb exoskeleton supported the rationale behind this question: having an actuated ankle joint could potentially reduce the torques applied by the user by a maximum value of 85 Nm. The two aforementioned questions will be investigated in a protocol that includes walking on a treadmill and on flat ground, with or without slope, and with a load applied on the back. In addition, the interaction forces measured at the exoskeleton harnesses will be used to assess the comfort of the user and the effectiveness of the control strategy to improve transparency.

This work presents a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control using the DEC (Disturbance Estimation and Compensation) parametric model. The modular structure of the proposed control model inspired the design of a modular identification procedure, in the sense that the same neural network is used to identify the parameters of the modules controlling different degrees of freedom. In this way the presented examples of body sway induced by external stimuli provide several training samples at once.

An objective performance measure for movement tasks is widely regarded as having utmost relevance for the therapy of movement disorders. Existing systems typically rely on human experts, which is known to produce substantial inter- and intra-rater variability. Present solutions are either based on simple features or invasive motion capture techniques. They typically work on a specific motion task only and fail to generalize to other tasks. In addition, they often require manual offline pre- and post-processing. In this paper we present a novel approach to compute a continuous and objective performance measure online during a patient session, without tedious and time-consuming pre- or post-processing steps. Our approach is able to generalize between different motion capture devices and different motion tasks. It runs on live motion data extracted with a non-invasive marker-less off-the-shelf vision-based tracking system as well as on data extracted from an inertial measurement unit suit. In the experiments we show that our approach is competitive with an offline approach as well as with the Unified Parkinson's Disease Rating Scale. Our approach is robust with respect to motion execution speed and it outperforms the offline approach regarding movement task generalization. We show promising results to track the current state of a Parkinson's subject online during a therapy session.

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.

Although the neurological impairments of Parkinson's disease (PD) patients are well known to go along with motor control deficits, e.g., tremor, rigidity, and reduced movement, not much is known about the motor control parameters affected by the disease. In this paper, we therefore present a novel approach to human motions analysis using motor control strategies with joint weight parameterization. We record the motions of healthy subjects and PD patients performing a hand coordination task with the whole-body XSens MVN motion capture system. For our motion strategy analysis we then follow a two step approach. First, we perform a complexity reduction by mapping the recorded human motions to a simplified kinematic model of the upper body. Second, we reproduce the recorded motions using a Jacobian weighted damped least squares controller with adaptive joint weights. We developed a method to iteratively learn the joint weights of the controller with the mapped human joint trajectories as reference input. Finally, we use the learned joint weights for a quantitative comparison between the motion control strategies of healthy subjects and PD patients. Other than expected from clinical experience, we found that the joint weights are almost evenly distributed along the arm in the PD group. In contrast to that, the proximal joint weights of the healthy subjects are notably larger than the distal ones.