Control interfaces for active trunk support

People with Duchenne muscular dystrophy (DMD) lose the ability to move due to severe muscular weakness hindering their activities of daily living (ADL). As a consequence, they have difficulties with remaining independent and have to depend on caregivers. Medication cannot prevent or cure DMD but it can increase the life expectancy of patients. Notwithstanding the increase in life expectancy, people with DMD have a lower Health Related Quality of Life (HRQoL) compared to people without DMD. A possible improvement could be achieved with assistive devices to perform ADL and, as a result, to depend less on caregivers.Symbionics (2.1) has been focusing on developing dynamic trunk and head supportive devices for people with neuromuscular disorders to assist them when performing daily activities. Three sub-projects were defined; they investigated user involvement, passive trunk support and active trunk support. User involvement entailed the interaction between the trunk and arm when accomplishing daily tasks. A passive trunk support was designed and tested in an experimental environment by people without and with an early stage of DMD. As the DMD progresses, more assistance is needed which could possibly be provided by an active trunk support. Thus, an active trunk support (which is the focus of this thesis) concentrates on the actuation and control of a passive trunk support.Operating and controlling an active assistive device requires a control interface. The control interface is responsible for converting the intended movement of the user into a device movement. Several control interfaces have been proposed for the control of assistive devices, the most common ones being a joystick, force sensors and sEMG (surface electromyography). We evaluated their performance by building an experimental user-controllable trunk support.The goal of this dissertation, therefore, is to evaluate control interfaces for active trunk support.To this end, several research questions were formulated and investigated:I. Is there an alternative to the intuitive trunk control interface to steer trunk muscles?Current research on the control of orthotic devices that use sEMG signals as control inputs, focuses mainly on muscles that are directly linked to the movement being performed (intuitive control). However, in some cases, it is hard to detect a proper sEMG signal (e.g., when there is a significant amount of fat) or specifically for EMG from trunk muscles, respiratory muscles are located in the trunk as well and can easily disturb the control signal, which can result in poor control performance. A way to overcome this problem might be the introduction of other, non-intuitive forms of control. We performed an explorative, comparative study on the learning behaviour of two different control interfaces, one with trunk muscle sEMGs (intuitive) and one with leg muscle sEMGs (non-intuitive) that can be potentially used for an active trunk support. Six healthy subjects undertook a 2-D Fitts’ law style task. They were asked to steer a cursor towards targets that were radially distributed symmetrically in five directions. II. Which control interface aids an active trunk support better?A feasibility study evaluated control interface performance with a novel trunk support assistive device (Trunk Drive) for adult men with Duchenne Muscular Dystrophy (DMD) namely, joystick, force on sternum, force on feet and sEMG (electromyography). This was done as a discrete position tracking task. We built a one degree of freedom active trunk support device that was tested on 10 healthy men. An experiment, based on Fitts’ law, was conducted for the evaluation. III. Which assistive admittance controller performs best in a 1-D Fitts’ law task?This study was dedicated to the development and assessment of three different admittance control algorithms for a trunk supporting robot; a law with constant parameters, a law with added feedforward force, and a law with variable parameters. A Fitts’-like experiment with 12 healthy subjects was performed to compare the control laws. IV. Do people with DMD generate satisfactory signals which can potentially drive an active trunk support?In a previous study, we showed that healthy people were able to control an active trunk support using four different control interfaces (based on joystick, force on feet, force on sternum and sEMG). All four control interfaces had different advantages and disadvantages. The aim of this study was to explore which of the four inputs could be detectably used by people with DMD to control an active trunk support. Three subjects with DMD participated in two experiments: an active experiment with an active trunk support assistive device and a static experiment without the active trunk support. The challenge in both experiments was to steer the cursor into a target of a graphical user interface using the signals from the different control interfaces. We concluded that, although the non-intuitive force on feet control is one of the best interfaces for people with DMD to control an active trunk support some DMD patients find it easier to use the EMG from their leg muscles. The joystick is the only usable intuitive control interface but, the function of one hand has to be sacrificed. The decision, as to which control interface works best, must be made per individual.<br/

Optimization of Automotive Light Distributions for Different Real Life Traffic Situations

The major goal of this thesis is to find a way to optimize current automotive headlamps in order to provide safer nighttime driving. While this has already been done in the past with the works by Damasky and Huhn, the current approach combines methods previously not used in one single study. In the first steps, the influence of different headlamp parameters on viewing distance of the driver is evaluated in field tests. In the second step, the current German traffic space is analysed before in the third step, the gaze behaviour of drivers is recorded and investigated for different situations. The combination of these studies is then used to propose new light distributions. In the first part, field tests are conducted in order to investigate detection distances with different lighting conditions. The gained data is used to provide recommended luminous intensity values for certain detection distances. Furthermore, the data is used to extract luminous intensity recommendations for different angular positions relative to the hot spot. These investigations show, that the current limits set by the ECE for high beam headlamps are sufficient to provide safe detection distances for nearly all situations. However, the data also shows, that low beam should be disregarded for any situation and only be used if high beam cannot be used at all. The traffic space analysis in the second part of this thesis shows, that there are significant differences between different road categories in terms of object location and frequency. For these situations, optimized segment distributions are proposed, leading to significant benefits over the conventional high beam setup. The difference between the proposed segment partitioning and the standard setup is, that the segments are not set equal in size. The segments at the centre of the distribution are set to be smaller in order to better mask out traffic that is further away. Furthermore, it is shown, that the benefit of additional segments is limited at around 280 segments, where a performance identical to a 10000 pixel headlamp is achieved. In the last section, regarding the gaze analysis a large driving test, including 54 test subjects is performed. Here the findings by Diem, Damasky, Brückmann and Weber are confirmed. New approaches regarding the correlation between the driver’s gaze and objects in the traffic space are tested. On a general level, no correlation between the object distribution and the gaze is found. However, a large databank containing object positions as well as driver’s gaze, speed, lighting condition and position in the world is set up for further, more detailed information. The data from all presented studies is then used to propose new, optimized light distributions

aTrunk—An ALS-Based Trunk Detection Algorithm

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide &amp; Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m2, while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59m average difference and 0.78m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks

aTrunk—An ALS-Based Trunk Detection Algorithm

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide &amp; Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m2, while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59m average difference and 0.78m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks