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Scheidig, Andrea; Schütz, Benjamin; Trinh, Thanh Quang; Vorndran, Alexander; Mayfarth, Anke; Sternitzke, Christian; Röhner, Eric; Groß, Horst-Michael;
Robot-assisted gait self-training: assessing the level achieved. - In: Sensors. - Basel : MDPI, ISSN 1424-8220, Bd. 21 (2021), 18, S. 1-15

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists and patients point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients?
Zhang, Yan; Müller, Steffen; Stephan, Benedict; Groß, Horst-Michael; Notni, Gunther;
Point cloud hand-object segmentation using multimodal imaging with thermal and color data for safe robotic object handover. - In: Sensors. - Basel : MDPI, ISSN 1424-8220, Bd. 21 (2021), 16, S. 1-16
Katzmann, Alexander; Taubmann, Oliver; Ahmad, Stephen; Mühlberg, Alexander; Sühling, Michael; Groß, Horst-Michael;
Explaining clinical decision support systems in medical imaging using cycle-consistent activation maximization. - In: Neurocomputing : an international journal.. - Amsterdam : Elsevier, ISSN 1872-8286, Bd. 458 (2021), S. 141-156

Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. In recent years, this has been addressed by a variety of approaches that have successfully contributed to providing deeper insight. Most notably, additive feature attribution methods are able to propagate decisions back into the input space by creating a saliency map which allows the practitioner to "see what the network sees." However, the quality of the generated maps can become poor and the images noisy if only limited data is available - a typical scenario in clinical contexts. We propose a novel decision explanation scheme based on CycleGAN activation maximization which generates high-quality visualizations of classifier decisions even in smaller data sets. We conducted a user study in which we evaluated our method on the LIDC dataset for lung lesion malignancy classification, the BreastMNIST dataset for ultrasound image breast cancer detection, as well as two subsets of the CIFAR-10 dataset for RBG image object recognition. Within this user study, our method clearly outperformed existing approaches on the medical imaging datasets and ranked second in the natural image setting. With our approach we make a significant contribution towards a better understanding of clinical decision support systems based on deep neural networks and thus aim to foster overall clinical acceptance.
Röhner, Eric; Mayfarth, Anke; Sternitzke, Christian; Layher, Frank; Scheidig, Andrea; Groß, Horst-Michael; Matziolis, Georgios; Böhle, Sabrina; Sander, Klaus;
Mobile robot-based gait training after total hip arthroplasty (THA) improves walking in biomechanical gait analysis. - In: Journal of Clinical Medicine : open access journal.. - Basel : MDPI, ISSN 2077-0383, Bd. 10 (2021), 11, S. 1-11

There are multiple attempts to decrease costs in the healthcare system while maintaining a high treatment quality. Digital therapies receive increasing attention in clinical practice, mainly relating to home-based exercises supported by mobile devices, eventually in combination with wearable sensors. The aim of this study was to determine if patients following total hip arthroplasty (THA) could benefit from gait training on crutches conducted by a mobile robot in a clinical setting. Method: This clinical trial was conducted with 30 patients following total hip arthroplasty. Fifteen patients received the conventional physiotherapy program in the clinic (including 5 min of gait training supported by a physiotherapist). The intervention group of 15 patients passed the same standard physiotherapy program, but the 5-min gait training supported by a physiotherapist was replaced by 2 × 5 min of gait training conducted by the robot. Length of stay of the patients was set to five days. Biomechanical gait parameters of the patients were assessed pre-surgery and upon patient discharge. Results: While before surgery no significant difference in gait parameters was existent, patients from the intervention group showed a significant higher absolute walking speed (0.83 vs. 0.65 m/s, p = 0.029), higher relative walking speed (0.2 vs. 0.16 m/s, p = 0.043) or shorter relative cycle time (3.35 vs. 3.68 s, p = 0.041) than the patients from the control group. Conclusion: The significant higher walking speed of patients indicates that such robot-based gait training on crutches may shorten length of stay (LOS) in acute clinics. However, the number of patients involved was rather small, thus calling for further studies.
Eisenbach, Markus;
Personenwiedererkennung mittels maschineller Lernverfahren. - In: Ausgezeichnete Informatikdissertationen. - Bonn : Ges. für Informatik, Bd. 2019 (2021), S. 59-68

Wengefeld, Tim; Lewandowski, Benjamin; Seichter, Daniel; Pfennig, Lennard; Müller, Steffen; Groß, Horst-Michael;
Real-time person orientation estimation and tracking using colored point clouds. - In: Robotics and autonomous systems : international journal.. - Amsterdam [u.a.] : Elsevier, ISSN 1872-793X, Bd. 135 (2021), 103665, S. 1-13

Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given peoples orientations, mobile systems can apply navigation strategies which take peoples proxemics into account or approach them in a human like manner to perform human robot interaction (HRI) tasks. In this paper, we present an approach for person orientation estimation based on computationally efficient features extracted from colored point clouds, formerly used for a two-class person attribute classification. The classification approach has been extended to the continuous domain while treating the problem of orientation estimation in real time. Furthermore, we present an approach for tracking estimated orientations over time using a Bayesian filter. We will show that tracking can increase the accuracy of orientations by up to 3.69˚ on a dataset recorded with a mobile robot. Best results on this highly challenging dataset are achieved with a regression approach for orientation estimation in combination with tracking. The mean angular error of just 16.49˚ proofs the applicability in real-world scenarios.
Scheidig, Andrea; Mayfarth, Anke; Sternitzke, Christian; Vorndran, Alexander; Trinh, Thanh Q.; Schütz, Benjamin; Groß, Horst-Michael;
Roboterassistiertes Gangtraining: Wertung des erreichten Stands. - In: AAL Kongress 2020. - Berlin : VDE VERLAG GMBH, (2020), S. 7-12

Seichter, Daniel; Lewandowski, Benjamin; Höchemer, Dominik; Wengefeld, Tim; Groß, Horst-Michael;
Multi-task deep learning for depth-based person perception in mobile robotics. - In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - [Piscataway, NJ] : IEEE, (2020), S. 10497-10504
Westhoven, Martin; Grinten, Tim van der;
Communicating robotic help requests : effects of eye-expressions, LED-lights and polite language. - In: i-com : journal of interactive media.. - Berlin : De Gruyter, ISSN 2196-6826, Bd. 19 (2020), 2, S. 153-167
Sesselmann, Maximilian; Stricker, Ronny; Naber, Thorsten; Schelle, Steffen;
Automatic detection and georeferencing of road damage from a mobile mapping systems imagery with the help of deep learning :
Automatische Detektion und objektscharfe Georeferenzierung von Fahrbahnschäden aus Bilddaten eines Mobile-Mapping-Systems mithilfe von Deep Learning. - In: - Berlin : Wichmann, ISSN 1869-9391, Bd. 15 (2020), 1, S. 18-30