User recognition for guiding and following people with a mobile robot in a clinical environment

Eisenbach, Markus; Vorndran, Alexander; Sorge, Sven; Groß, Horst–Michael

doi:10.1109/iros.2015.7353880

Cited by 25 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tracking the position and velocity of the detected persons is performed by utilizing a multivariate Kalman filter. The re-identification was based on a face-based approach [ 34 ] and the patient’s overall appearance by using a metric-learning approach with color and texture features [ 35 ].…”

Section: Mobile Gait Self-training Under Real Clinical Environment Conditionsmentioning

confidence: 99%

Robot-Assisted Gait Self-Training: Assessing the Level Achieved

Scheidig

Schütz

Trinh

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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?

show abstract

Section: Mobile Gait Self-training Under Real Clinical Environment Conditionsmentioning

confidence: 99%

Robot-Assisted Gait Self-Training: Assessing the Level Achieved

Scheidig

Schütz

Trinh

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Person re-identification and recovery: In addition to robust person detection and tracking, person-following robots need to be able to plan to re-identify when necessary (Koide and Miura, 2016). Moreover, these techniques are essential for accompanying a specific person (Eisenbach et al, 2015;Ilias et al, 2014). Predictive and probabilistic models such as Kalman filters, particle filters, etc., are typically used to estimate the person's location in future, which can be used as prior knowledge in case of a missing target situation.…”

Section: Other Considerations For Planning and Controlmentioning

confidence: 99%

Person-following by autonomous robots: A categorical overview

Islam

Hong

Sattar

2019

The International Journal of Robotics Research

111

View full text Add to dashboard Cite

A wide range of human-robot collaborative applications in diverse domains such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to follow its human companion. Different working environments and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of a person-following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed and their applicability in varied operational scenarios are presented. Then, some of the prominent methods are qualitatively compared, corresponding practicalities are illustrated, and their feasibility is analyzed for various usecases. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.

show abstract

“…The first set of features describes the appearance of each person. Following our proposed approach in [28], we extract color histograms in various color spaces for the full body enclosed by the bounding box in the RGB image. Then, we apply a learned metric to transform the extracted features to a 40 dimensional subspace to allow for fast matching.…”

Section: Re-identificationmentioning

confidence: 99%

“…Then, we apply a learned metric to transform the extracted features to a 40 dimensional subspace to allow for fast matching. To compute a suitable feature transformation for distinguishing persons by their appearance under varying environmental conditions, we applied a metric learning approach, namely, local Fisher discriminant analysis (LFDA), on feature vectors transferred to a kernel space using a χ 2 -RBF kernel [28]. This results in a 40 dimensional feature vector for appearance-based re-identification.…”

Section: Re-identificationmentioning

confidence: 99%

“…For the 40 dimensional color feature vectors, the applied metric learning method (see Section 3.2.3) optimizes for a euclidean distances measure, in case the distance is d(v a , v b ) = |v a − v b |. In order to transform the distances into probabilities of representing the same individual (similarity), we used data of a test dataset in order to tune the parameters m and c of a Fermi-function, which pretty much fit the resulting distribution [28,34]. The similarity, therefore, is:…”

Section: Face and Color Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots

Müller

Wengefeld

Trinh

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.

show abstract

User recognition for guiding and following people with a mobile robot in a clinical environment

Cited by 25 publications

References 15 publications

Robot-Assisted Gait Self-Training: Assessing the Level Achieved

Robot-Assisted Gait Self-Training: Assessing the Level Achieved

Person-following by autonomous robots: A categorical overview

A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots

Contact Info

Product

Resources

About