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2020


Learning to Predict Perceptual Distributions of Haptic Adjectives
Learning to Predict Perceptual Distributions of Haptic Adjectives

Richardson, B. A., Kuchenbecker, K. J.

Frontiers in Neurorobotics, 13(116):1-16, Febuary 2020 (article)

Abstract
When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels. In addition to analyzing the collected labels (10 basic haptic adjectives) and demonstrating the quality of our method's predictions, we hold out specific features to determine the influence of individual sensor modalities on the predictive performance for each adjective. Our results demonstrate the feasibility of modeling both the intensity and the variation of haptic perception, two crucial yet previously neglected components of human haptic perception.

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DOI [BibTex]

2020


DOI [BibTex]


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Exercising with Baxter: preliminary support for assistive social-physical human-robot interaction

Fitter, N. T., Mohan, M., Kuchenbecker, K. J., Johnson, M. J.

Journal of NeuroEngineering and Rehabilitation, 17(19), Febuary 2020 (article)

Abstract
Background: The worldwide population of older adults will soon exceed the capacity of assisted living facilities. Accordingly, we aim to understand whether appropriately designed robots could help older adults stay active at home. Methods: Building on related literature as well as guidance from experts in game design, rehabilitation, and physical and occupational therapy, we developed eight human-robot exercise games for the Baxter Research Robot, six of which involve physical human-robot contact. After extensive iteration, these games were tested in an exploratory user study including 20 younger adult and 20 older adult users. Results: Only socially and physically interactive games fell in the highest ranges for pleasantness, enjoyment, engagement, cognitive challenge, and energy level. Our games successfully spanned three different physical, cognitive, and temporal challenge levels. User trust and confidence in Baxter increased significantly between pre- and post-study assessments. Older adults experienced higher exercise, energy, and engagement levels than younger adults, and women rated the robot more highly than men on several survey questions. Conclusions: The results indicate that social-physical exercise with a robot is more pleasant, enjoyable, engaging, cognitively challenging, and energetic than similar interactions that lack physical touch. In addition to this main finding, researchers working in similar areas can build on our design practices, our open-source resources, and the age-group and gender differences that we found.

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DOI [BibTex]

DOI [BibTex]


Learning Multi-Human Optical Flow
Learning Multi-Human Optical Flow

Ranjan, A., Hoffmann, D. T., Tzionas, D., Tang, S., Romero, J., Black, M. J.

International Journal of Computer Vision (IJCV), January 2020 (article)

Abstract
The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the domain of human motion. Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset. We use a 3D model of the human body and motion capture data to synthesize realistic flow fields in both single-and multi-person images. We then train optical flow networks to estimate human flow fields from pairs of images. We demonstrate that our trained networks are more accurate than a wide range of top methods on held-out test data and that they can generalize well to real image sequences. The code, trained models and the dataset are available for research.

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Paper Publisher Version poster link (url) DOI [BibTex]


General Movement Assessment from videos of computed {3D} infant body models is equally effective compared to conventional {RGB} Video rating
General Movement Assessment from videos of computed 3D infant body models is equally effective compared to conventional RGB Video rating

Schroeder, S., Hesse, N., Weinberger, R., Tacke, U., Gerstl, L., Hilgendorff, A., Heinen, F., Arens, M., Bodensteiner, C., Dijkstra, L. J., Pujades, S., Black, M., Hadders-Algra, M.

Early Human Development, 2020 (article)

Abstract
Background: General Movement Assessment (GMA) is a powerful tool to predict Cerebral Palsy (CP). Yet, GMA requires substantial training hampering its implementation in clinical routine. This inspired a world-wide quest for automated GMA. Aim: To test whether a low-cost, marker-less system for three-dimensional motion capture from RGB depth sequences using a whole body infant model may serve as the basis for automated GMA. Study design: Clinical case study at an academic neurodevelopmental outpatient clinic. Subjects: Twenty-nine high-risk infants were recruited and assessed at their clinical follow-up at 2-4 month corrected age (CA). Their neurodevelopmental outcome was assessed regularly up to 12-31 months CA. Outcome measures: GMA according to Hadders-Algra by a masked GMA-expert of conventional and computed 3D body model (“SMIL motion”) videos of the same GMs. Agreement between both GMAs was assessed, and sensitivity and specificity of both methods to predict CP at ≥12 months CA. Results: The agreement of the two GMA ratings was substantial, with κ=0.66 for the classification of definitely abnormal (DA) GMs and an ICC of 0.887 (95% CI 0.762;0.947) for a more detailed GM-scoring. Five children were diagnosed with CP (four bilateral, one unilateral CP). The GMs of the child with unilateral CP were twice rated as mildly abnormal. DA-ratings of both videos predicted bilateral CP well: sensitivity 75% and 100%, specificity 88% and 92% for conventional and SMIL motion videos, respectively. Conclusions: Our computed infant 3D full body model is an attractive starting point for automated GMA in infants at risk of CP.

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[BibTex]

[BibTex]