Online data analysis for a Knee-Ankle-Foot-Orthosis with neuro-control
Georg-August-Universität Göttingen, 3. Physikalisches Institut, Germany
BFNT Göttingen, Germany
BCCN Göttingen, Germany
Otto Bock HealthCare GmbH, Germany
A Knee-Ankle-Foot-Orthosis (KAFO) is a modular lower-extremity orthosis prescribed to people with gait disability which might be, e.g., caused by diseases or injury to brain or spinal-cord. The KAFO should support, correct and assist the movement of the corresponding affected joints. Traditional KAFOs are restricted by a gait depending switch of the joints based on (electro-) mechanic non-adaptive switches. So common disturbances (floor unevenness, obstacles, ramps) cannot be mastered in a satisfactory way. Novel approaches include active elements into the orthosis, which do not directly act on the movement. Instead they adjust the compliance leading to new challenges for the controller of such actuators, which are difficult to handle with traditional approaches.
Thus new technologies have to be developed to improve control and to overcome the shortcomings of traditional non-adaptive approaches, thus solving the problem of efficient actuator control. Development of advanced orthotic devices is held back by the vast number of possible indications as well as by the wide range of neuromuscular variability within a specific patient group (Yakimovich et al., 2009). The development of advanced devices is therefore imposing the need for individual (online) adaptation of gait parameters to allow adaptation (1) to changing environments like slopes, stairs etc. as well as to gait parameters like stride length/frequency and (2) to the individual patients with respect to physiological conditions. To do so, we have employed a reflexive neuro-controller as inspired by RunBot (Manoonpong et al., 2007), embedded to a KAFO based on a controllable hydraulic damper, derived from OttoBock's C-Leg©.
In this study we extend the neural controller with additional neural modules for prediction of expected sensory inputs and observing typical gait parameters (like joint angles). This allows the complete neural controller to adapt gait parameters to master environmental changes like slopes of different steepness and to select different modes of locomotion to accomplish compliance for completely different environments, e.g., stair climbing.
Traditional control approaches are often using a set of optimized thresholds to account for all possible situations and patients at the same time, which proves to be very difficult, considering the variability in patients abilities. Therefore the use of these controllers are limited to only a subset of the possible patient groups.
To overcome the shortcomings of these one size fits all approaches, our neural controller needs to satisfy short reaction times, as adaptation should optimally happen within the first step after entering different environments. As a result the neural controller is gaining an ability to adapt the orthosis' compliance to deal with different situations, matched for individual patients.
This research was supported by the BMBF-funded BFNT Göttingen with grant number 01GQ0810 (project 3A) and BCCN Göttingen with grant number 01GQ1005A (project D1) and the Emmy Noether Program (DFG, MA4464/3-1).
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
data analysis and machine learning (please use "data analysis and machine learning" as keyword)
(2011). Online data analysis for a Knee-Ankle-Foot-Orthosis with neuro-control.
Front. Comput. Neurosci.
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
23 Aug 2011;
04 Oct 2011.
Mr. Jan-Matthias Braun, Georg-August-Universität Göttingen, 3. Physikalisches Institut, Göttingen, Niedersachsen, 37073, Germany, email@example.com