Dr. Jan-Matthias Braun

Group(s): Neural Control and Robotics
Email:
jan-matthias.braun@phys.uni-goettingen.de
Phone: +49 551/ 39 10765
Room: E.01.102
Office hour: Mittwochs, 15:00 - 15:45 Uhr

Notice:On Wednesday, 3rd of September I won't be in my office.


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    Author / Editor / Organization
    Year
    Title
    Journal / Proceedings / Book
    Kuhlemann, I. and Braun, J -M. and Wörgötter, F. and Manoonpong, P. (2014).
    Comparing Arc-shaped Feet and Rigid Ankles with Flat Feet and Compliant Ankles for a Dynamic Walker. Mobile Service RoboticsWSPC Proceedings, 353-360, 17. DOI: 10.1142/9789814623353_0041.
    BibTeX:
    @inproceedings{kuhlemannbraunwoergoetter2014,
      author = {Kuhlemann, I. and Braun, J -M. and Wörgötter, F. and Manoonpong, P.},
      title = {Comparing Arc-shaped Feet and Rigid Ankles with Flat Feet and Compliant Ankles for a Dynamic Walker},
      pages = {353-360},
      booktitle = {Mobile Service Robotics},
      journal = {WSPC Proceedings},
      year = {2014},
      number = {17},
      language = {English},
      location = {Poznan, Poland},
      series = {Proceedings of the International Conference on Climbing and Walking Robots},
      url = {http://www.bfnt-goettingen.de/Publications/articlereference.2014-10-23.5545515863},
      doi = {10.1142/9789814623353_0041},
      abstract = {In this paper we show that exchanging curved feet and rigid ankles by at feet and compliant ankles improves the range of gait parameters for a bipedal dynamic walker. The new lower legs were designed such that they t to the old set-up, allowing for a direct and quantitative comparison. The dynamic walking robot RunBot, controlled by an re exive neural network, uses only few sensors for generating its stable gait. The results show that at feet and compliant ankles extend RunBots parameter range especially to more leaning back postures. They also allow the robot to stably walk over obstacles with low height.}}
    Abstract: In this paper we show that exchanging curved feet and rigid ankles by at feet and compliant ankles improves the range of gait parameters for a bipedal dynamic walker. The new lower legs were designed such that they t to the old set-up, allowing for a direct and quantitative comparison. The dynamic walking robot RunBot, controlled by an re exive neural network, uses only few sensors for generating its stable gait. The results show that at feet and compliant ankles extend RunBots parameter range especially to more leaning back postures. They also allow the robot to stably walk over obstacles with low height.
    Review:
    Braun, J -M. and Wörgötter, F. and Manoonpong, P. (2014).
    Internal Models Support Specific Gaits in Orthotic Devices. Mobile Service Robotics, 539-546, 17. DOI: 10.1142/9789814623353_0063.
    BibTeX:
    @inproceedings{braunwoergoettermanoonpong2014a,
      author = {Braun, J -M. and Wörgötter, F. and Manoonpong, P.},
      title = {Internal Models Support Specific Gaits in Orthotic Devices},
      pages = {539-546},
      booktitle = {Mobile Service Robotics},
      year = {2014},
      number = {17},
      language = {English},
      location = {Poznan, Poland},
      series = {Proceedings of the International Conference on Climbing and Walking Robots},
      url = {http://www.worldscientific.com/doi/abs/10.1142/9789814623353_0063},
      doi = {10.1142/9789814623353_0063},
      abstract = {Patients use orthoses and prosthesis for the lower limbs to support and enable movements, they can not or only with difficulties perform themselves. Because traditional devices support only a limited set of movements, patients are restricted in their mobility. A possible approach to overcome such limitations is to supply the patient via the orthosis with situation-dependent gait models. To achieve this, we present a method for gait recognition using model invalidation. We show that these models are capable to predict the individual patients movements and supply the correct gait. We investigate the systems accuracy and robustness on a Knee-Ankle-Foot-Orthosis, introducing behaviour changes depending on the patients current walking situation. We conclude that the here presented model-based support of different gaits has the power to enhance the patients mobility.}}
    Abstract: Patients use orthoses and prosthesis for the lower limbs to support and enable movements, they can not or only with difficulties perform themselves. Because traditional devices support only a limited set of movements, patients are restricted in their mobility. A possible approach to overcome such limitations is to supply the patient via the orthosis with situation-dependent gait models. To achieve this, we present a method for gait recognition using model invalidation. We show that these models are capable to predict the individual patients movements and supply the correct gait. We investigate the systems accuracy and robustness on a Knee-Ankle-Foot-Orthosis, introducing behaviour changes depending on the patients current walking situation. We conclude that the here presented model-based support of different gaits has the power to enhance the patients mobility.
    Review:
    Braun, J. and Wörgötter, F. and Manoonpong, P. (2014).
    Orthosis Controller with Internal Models Supports Individual Gaits. Proceedings of the 9th Annual Dynamic Walking Conference, 1 --2, 9.
    BibTeX:
    @inproceedings{braunwoergoettermanoonpong2014,
      author = {Braun, J. and Wörgötter, F. and Manoonpong, P.},
      title = {Orthosis Controller with Internal Models Supports Individual Gaits},
      pages = {1 --2},
      booktitle = {Proceedings of the 9th Annual Dynamic Walking Conference},
      year = {2014},
      number = {9},
      language = {English},
      location = {Zürich, Switzerland},
      month = {06},
      series = {Proceedings of the 9th Annual Dynamic Walking Conference}}
    Abstract:
    Review:
    Braun, J. (2015).
    Modular Architecture for an Adaptive, Personalisable Knee- Ankle-Foot-Orthosis Controlled by Artificial Neural Networks. .
    BibTeX:
    @phdthesis{braun2015,
      author = {Braun, J.},
      title = {Modular Architecture for an Adaptive, Personalisable Knee- Ankle-Foot-Orthosis Controlled by Artificial Neural Networks},
      year = {2015},
      url = {http://hdl.handle.net/11858/00-1735-0000-0028-866F-6},
      abstract = {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 patients mobility. (iii) Target Group: The matching of the individuals need for assistance with the controllers 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 devices 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 patients gait samples, and a user interface allows direct user-interaction to define the control output, embedding the users 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 patients requirements. The proposed controller architecture focuses on the patients 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 subjects 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 walkers gait, but shows the limitations of the controllers 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 patients 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.}}
    Abstract: 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 patients mobility. (iii) Target Group: The matching of the individuals need for assistance with the controllers 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 devices 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 patients gait samples, and a user interface allows direct user-interaction to define the control output, embedding the users 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 patients requirements. The proposed controller architecture focuses on the patients 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 subjects 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 walkers gait, but shows the limitations of the controllers 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 patients 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.
    Review:
    Goldbeck, C. and Kaul, L. and Vahrenkamp, N. and Wörgötter, F. and Asfour, T. and Braun, J. M. (2016).
    Two ways of walking: Contrasting a reflexive neuro-controller and a LIP-based ZMP-controller on the humanoid robot ARMAR-4. IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), 966--972. DOI: 10.1109/HUMANOIDS.2016.7803389.
    BibTeX:
    @inproceedings{goldbeckkaulvahrenkamp2016,
      author = {Goldbeck, C. and Kaul, L. and Vahrenkamp, N. and Wörgötter, F. and Asfour, T. and Braun, J. M.},
      title = {Two ways of walking: Contrasting a reflexive neuro-controller and a LIP-based ZMP-controller on the humanoid robot ARMAR-4},
      pages = {966--972},
      booktitle = {IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)},
      year = {2016},
      month = {Nov},
      url = {http://ieeexplore.ieee.org/abstract/document/7803389},
      doi = {10.1109/HUMANOIDS.2016.7803389},
      abstract = {Full-size humanoid robots are traditionally controlled with the Zero Moment Point (ZMP)-paradigm and simplified dynamics, a well established method which can be applied to balancing, walking, and whole-body manipulation tasks. For pure walking control, approaches like pattern generators and reflexes are employed, often on optimized hardware. Both controller groups are developed on different platforms and therefore can only be indirectly compared in terms of human likeness or energy efficiency. We present a reflex based neuro-controller with an underlying, simple hill-type muscle model on the extremely versatile humanoid robot ARMAR-4. We demonstrate the reflexive controllers flexible capabilities in terms of walking speed, step length, energy efficiency and inherent robustness against fall due to small slopes and pushes along the frontal axis. We contrast this controller with a Linearized Inverted Pendulum (LIP)-based ZMP-controller on the same platform. The promising results of this study show that even general humanoid robots can benefit from reflexive control schemes and encourage further investigation in this field.}}
    Abstract: Full-size humanoid robots are traditionally controlled with the Zero Moment Point (ZMP)-paradigm and simplified dynamics, a well established method which can be applied to balancing, walking, and whole-body manipulation tasks. For pure walking control, approaches like pattern generators and reflexes are employed, often on optimized hardware. Both controller groups are developed on different platforms and therefore can only be indirectly compared in terms of human likeness or energy efficiency. We present a reflex based neuro-controller with an underlying, simple hill-type muscle model on the extremely versatile humanoid robot ARMAR-4. We demonstrate the reflexive controllers flexible capabilities in terms of walking speed, step length, energy efficiency and inherent robustness against fall due to small slopes and pushes along the frontal axis. We contrast this controller with a Linearized Inverted Pendulum (LIP)-based ZMP-controller on the same platform. The promising results of this study show that even general humanoid robots can benefit from reflexive control schemes and encourage further investigation in this field.
    Review:

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