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Register a new userAugLimb: Compact Robotic Limb for Human Augmentation
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Added by
Haoran Xie
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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This work proposes a compact robotic limb, AugLimb, that can augment our body functions and support the daily activities. AugLimb adopts the double-layer scissor unit for the extendable mechanism which can achieve 2.5 times longer than the forearm length. The proposed device can be mounted on the users upper arm, and transform into compact state without obstruction to wearers. The proposed device is lightweight with low burden exerted on the wearer. We developed the prototype of AugLimb to demonstrate the proposed mechanisms. We believe that the design methodology of AugLimb can facilitate human augmentation research for practical use. see http://www.jaist.ac.jp/~xie/auglimb.html
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Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps. This paper presents a data capture protocol that partially addresses these deficiencies by asking participants to specify ranges over which a grasp is valid. The protocol is verified both qualitatively through online survey questions (where 95.38% of within-range grasps are identified correctly with the nearest extreme grasp) and quantitatively by showing that there is small variation in grasps ranges from different participants as measured by joint angles, contact points, and position. We demonstrate that these grasp ranges are valid through testing on a physical robot (93.75% of grasps interpolated from grasp ranges are successful).
This work describes a new human-in-the-loop (HitL) assistive grasping system for individuals with varying levels of physical capabilities. We investigated the feasibility of using four potential input devices with our assistive grasping system interface, using able-bodied individuals to define a set of quantitative metrics that could be used to assess an assistive grasping system. We then took these measurements and created a generalized benchmark for evaluating the effectiveness of any arbitrary input device into a HitL grasping system. The four input devices were a mouse, a speech recognition device, an assistive switch, and a novel sEMG device developed by our group that was connected either to the forearm or behind the ear of the subject. These preliminary results provide insight into how different interface devices perform for generalized assistive grasping tasks and also highlight the potential of sEMG based control for severely disabled individuals.
Human activity recognition plays an increasingly important role not only in our daily lives, but also in the medical and rehabilitation fields. The development of deep learning has also contributed to the advancement of human activity recognition, but the large amount of data annotation work required to train deep learning models is a major obstacle to the development of human activity recognition. Contrastive learning has started to be used in the field of sensor-based human activity recognition due to its ability to avoid the cost of labeling large datasets and its ability to better distinguish between sample representations of different instances. Among them, data augmentation, an important part of contrast learning, has a significant impact on model effectiveness, but current data augmentation methods do not perform too successfully in contrast learning frameworks for wearable sensor-based activity recognition. To optimize the effect of contrast learning models, in this paper, we investigate the sampling frequency of sensors and propose a resampling data augmentation method. In addition, we also propose a contrast learning framework based on human activity recognition and apply the resampling augmentation method to the data augmentation phase of contrast learning. The experimental results show that the resampling augmentation method outperforms supervised learning by 9.88% on UCI HAR and 7.69% on Motion Sensor in the fine-tuning evaluation of contrast learning with a small amount of labeled data, and also reveal that not all data augmentation methods will have positive effects in the contrast learning framework. Finally, we explored the influence of the combination of different augmentation methods on contrastive learning, and the experimental results showed that the effect of most combination augmentation methods was better than that of single augmentation.
This paper aimed to explore whether human beings can understand gestures produced by telepresence robots. If it were the case, they can derive meaning conveyed in telerobotic gestures when processing spatial information. We conducted two experiments over Skype in the present study. Participants were presented with a robotic interface that had arms, which were teleoperated by an experimenter. The robot could point to virtual locations that represented certain entities. In Experiment 1, the experimenter described spatial locations of fictitious objects sequentially in two conditions: speech condition (SO, verbal descriptions clearly indicated the spatial layout) and speech and gesture condition (SR, verbal descriptions were ambiguous but accompanied by robotic pointing gestures). Participants were then asked to recall the objects spatial locations. We found that the number of spatial locations recalled in the SR condition was on par with that in the SO condition, suggesting that telerobotic pointing gestures compensated ambiguous speech during the process of spatial information. In Experiment 2, the experimenter described spatial locations non-sequentially in the SR and SO conditions. Surprisingly, the number of spatial locations recalled in the SR condition was even higher than that in the SO condition, suggesting that telerobotic pointing gestures were more powerful than speech in conveying spatial information when information was presented in an unpredictable order. The findings provide evidence that human beings are able to comprehend telerobotic gestures, and importantly, integrate these gestures with co-occurring speech. This work promotes engaging remote collaboration among humans through a robot intermediary.
User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal processing framework that can efficiently extract information from their deformation. Here we present OrbTouch, a device that uses statistical learning algorithms, based on convolutional neural networks, to map deformations from human touch to categorical labels (i.e., gestures) and touch location using stretchable capacitor signals as inputs. We demonstrate this approach by using the device to control the popular game Tetris. OrbTouch provides a modular, robust framework to interpret deformation in soft media, laying a foundation for new modes of human computer interaction through shape changing solids.
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