Braun, Jan-Matthias

English

Walking is so fundamental in everyday life that it is, for most people, an unconscious action.
Loss or limitations in the ability to walk or stand directly impair our mobility and independence.
Reasons of limitations can be stroke, paraplegia, or other damages to nerves, muscles, tendons, or limbs, encephalitis, brain abscesses, myopathies and further incidents and diseases affecting the motor control or the musculoskeletal system.
In many cases, patients can be helped by, e.g., the use of orthoses for the lower limbs which assist to support the body and enable the patients to regain their movement abilities.

Important factors and problems dominate the choice and usage of the suitable device: 
(i) Individualisation: The individual patients' neurological status and remaining motor function have to be compatible with the support provided by the device.
Particularly with regard to preserve---and not to interfere with---the remaining abilities, the device is selected to provide as little support as possible.
As the remaining abilities largely vary with the individual expression of medical indications, the matching process is personalised and patient centred.
(ii) Specialised Design: The movements a device supports are determined by its controller. Thereby, mobility is often limited to one or two basic movements, like walking and sitting. This specialisation imposes restrictions on the patient's mobility.
(iii) Target Group: The matching of the individual's need for assistance with the controller's abilities substantially restrict the target group of a device.
(iv) Asymmetric Use: Patients often favour their healthy limb, leading to asymmetric gait and other gait deviations, implying consequential damage.
(v) Device Acceptance and User Opinions: Device acceptance by its user is affected by many factors, as, for instance, comfort, the applicability in daily activities, cosmetic factors, and the patients' impression if their opinions were considered in the process of device selection.
Several studies indicate that, although a device might fit from an orthopaedic point of view, 60% up to nearly 100% of patients abandoned it for subjective reasons.

Here, we assume that all these five problems can be addressed by the device's controller.
So far, controllers are only used to tackle some of these problems isolated.
We propose a modular controller architecture, which is designed for flexible use, expandability, and adaptation, e.g., learning from individually observed gait samples and intent recognition, solving the set of problems.
The development was realised on a semi-active Knee-Ankle-Foot-Orthosis with hydraulic knee-damper and tested on a healthy walker.

To address specialised design, we develop a controller based on a gait-independent formalism: An artificial neural network abstracts gait progress by decoding the sensory input. On top of this gait progress representation a device-specific network provides hardware control. To facilitate individualisation, the gait progress representation is learned from the patient's gait samples, and a user interface allows direct user-interaction to define the control output, embedding the user's opinions directly in the process to provide support for the individual motions. The use of artificial neural networks provides adaptation algorithms.

The support of individual gaits leads itself to a specialisation of the controller. Here, we developed fast and reliable intent recognition with gait switching. The switching is done between per-gait modules, which consist of networks for gait progress abstraction, control output generation and internal models to predict gait dynamics. The prediction error identifies the optimal gait. This modular approach does not limit the number of movements, in contrast, it allows to extend the controller by further gaits in a formalised manner. It completes the solution to the problem of specialised design with a formalism which allows to extend the number of supported gaits with respect to the patient's requirements.

The proposed controller architecture focuses on the patient's gait dynamics. The used sensors describe the joint dynamics and are not bound to a specific hardware-design. Tests on two variations of the presented orthosis prototype support this hypotheses. This reduces the requirements on the patients' remaining abilities to the initiation of periodic motion with the support of the orthosis, expanding the target group. The support of individual gait allows the patients to develop their own gait, the patients do not have to force their gait into a pattern recognisable by the controller, providing a possibility for more symmetric gait.

In a gait laboratory study, combining motion capture and electromyography, we investigated the user-device-interaction and how it alters the subject's gait.
We found that 1. the deviations imposed by the hardware dominate those by the controller, 2. we located the upper body as the place with the largest deviations, and 3. we conclude that  controller optimisation can be driven by a careful analysis of additional muscular activity in 
 electromyographic recordings.
This study shows that the presented controller supports the healthy walker's gait, but shows the limitations of the controller's impact due to hardware and sensory restrictions.
The localisation of gait deviations identifies potential for manual and online controller-adaptation.

To summarise, in this thesis we developed a controller on an orthosis prototype with a healthy walker based on a modular architecture allowing individual patient support.
The system learns in a training process from observed gait samples and allows a simple and fast adaptation to gait changes and, in addition, enables easy extensions with further gaits.
The evaluation of the user-device-interaction indicates deviations in the upper body and muscle work against the orthosis.
This relation enables us in the next steps to infer how the devices’ support can be optimised and how an automatic adaptation mechanism can quantify its impact on the patient’s gait.
Based on the here presented groundwork of an adaptive controller architecture, now it is possible to develop an observing, adapting controller, which is capable of basic patient surveillance, complementing medical treatment and rehabilitation

Georg-August-University Göttingen

Modular Architecture for an Adaptive, Personalisable Knee-Ankle-Foot-Orthosis Controlled by Artificial Neural Networks

https://ediss.uni-goettingen.de/bitstream/11858/00-1735-0000-0028-866F-6/1/dissertation_jan-matthias_braun_eDiss.pdf

Modular Architecture for an Adaptive, Personalisable Knee-Ankle-Foot-Orthosis Controlled by Artificial Neural Networks

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Georg-August-University Göttingen