Lower limb exoskeletons offer support for patients suffering from mobility disorders due to injury, stroke, etc. But these devices are not used in day-to-day life and environments due to their limited human-computer interface to perceive and handle different terrains and tasks. In this paper, we introduce a simple vision-based environment perception pipeline for lower- limb exoskeletons for obstacle crossing tasks. The proposed pipeline consists of three stages, namely, ground plane and obstacle detection, estimating obstacle location and dimensions, and obstacle tracking. To reduce noisy artifacts and reliably detect obstacles, we propose a similarity metric based on color, gradient orientation, and 2D surface normal. Depth map of the detected obstacle region is utilized for estimating the obstacle location and dimensions. Also, we consider two obstacle tracking modes for obstacle crossing, visual tracking using a RGB-D camera and positional tracking using a SLAM camera. The proposed vision-based perception pipeline is integrated with an exoskeleton, where we propose a control scheme that can vary step length adaptively to successfully cross detected obstacles. We conduct offline and online experiments to validate the proposed perception pipeline and provide insights on the same. Our experiments show that the proposed pipeline allows exoskeletons to understand their environment and successfully cross obstacles.

The uncanny valley hypothesis states that users might experience eerie when interacting with almost but not fully human-like artificial characters. The advancements in artificial intelligence, robotics, and computer graphics have led to life-like virtual humans and humanoid robots. It is necessary to revisit the hypothesis to check if they positively or negatively affect the current population, who are much more accustomed to the latest technologies. In this paper, we study and present a unique evaluation of the uncanny valley hypothesis by allowing participants to interact live with four different humanoid robots (of varying levels of humanlikeness). To evaluate the affinity of each robot, each participant needs to fill a survey questionnaire. Apart from this, we also use deep learning methods to quantify the participants’ emotional states using multi-modalcues, including visual, audio, and text, by recording the participant-robot interaction. The multi-modal analysis and surveys provide interesting results and insights into the uncanny valley hypothesis.

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers in a work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely: survey questionnaire and customer feedback. The survey questionnaires are analyzed based on the datapoints provided in the questionnaire. We propose a novel approach to analyze customer feedback data using sentic computing. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.

course Share on Real humans with virtual humans and social robots interactions (HCI) Authors: Daniel Thalmann EPFL, Switzerland EPFL, SwitzerlandView Profile , Nadia Magnenat Thalmann NTU, Singapore NTU, SingaporeView Profile , Manoj Ramanathan NTU, Singapore NTU, SingaporeView Profile Authors Info & Claims SA '17: SIGGRAPH Asia 2017 CoursesNovember 2017 Article No.: 15Pages 1–221https://doi.org/10.1145/3134472.3134513Published:27 November 2017Publication History 0citation237DownloadsMetricsTotal Citations0Total Downloads237Last 12 Months26Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

For reliable human-robot interaction, the robot must know the person's action in order to plan the appropriate way to interact or assist the person. As part of the pre-processing stage of action recognition, the robot also needs to recognize the various body parts and posture of the person. But estimation of posture and body parts is challenging due to the articulated nature of the human body and the huge intra-class variations. To address this challenge, we propose two schemes using Hierarchical-ELM (H-ELM) for posture detection into either upright or non-upright posture. In the first scheme, we follow a whole body detector approach, where a H-ELM classifier is trained on several whole body postures. In the second scheme, we follow a body part detection approach, where separate H-ELM classifiers are detected for each body part. Using the detected body parts a final decision is made on the posture of the person. We have conducted several experiments to compare the performance of both approaches under different scenarios like view angle changes, occlusion etc. Our experimental results show that body part H-ELM based posture detection works better than other proposed framework even in the presence of occlusion.

Body part segmentation and detection in videos is a useful analysis for many computer vision tasks such as action recognition and video search. Conventional methods mainly focus on body part detection assuming upright posture of the human body. Recently, a body part detection framework was proposed to include non-upright postures. This method consists of 2 parts, initial segmentation and computation of body part likelihood score for each segment. In this paper, we propose improvements to this approach. Firstly, we propose a novel motion based body part segmentation using kinematic features to identify segments which undergo similar motion in the video based on a consistency or error measure. Secondly, we replace the Extreme Learning Machine classifier in the original work with deep learning to investigate it's performance. For accurate detection, deep learning requires a lot of training data and it has so far been used only in high resolution images. Here we apply deep learning for body part detection in low resolution cases. We conduct experiments to study and analyse the effect of the improvements proposed.

Given a video sequence, the task of action recognition is to identify the most similar action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has wide application including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human action recognition is affected by several challenges due to view changes, occlusion, variation in execution rate, anthropometry, camera motion, and background clutter. In this survey, we provide an overview of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification will help researchers to identify the suitable methods available to address each of the challenges faced and their limitations. In addition, we also identify the publicly available datasets and the challenges posed by them. From this survey, we draw conclusions regarding how well a challenge has been solved, and we identify potential research areas that require further work.