No abstract

When a tool is tapped on or dragged over an object surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the surfaces. We present an approach for tool-mediated surface classification that combines these signals and demonstrate that the proposed method is robust against variable scan-time parameters. We examine freehand recordings of 69 textured surfaces recorded by different users and propose a classification system that uses perception-related features, such as hardness, roughness, and friction; selected features adapted from speech recognition, such as modified cepstral coefficients applied to our acceleration signals; and surface texture-related image features. We focus on mitigating the effect of variable contact force and exploration velocity conditions on these features as a prerequisite for a robust machine-learning-based approach for surface classification. The proposed system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of textured surfaces under variable freehand movement conditions, exerted by different human operators. The proposed subset of six features, selected from the described sound, image, friction force, and acceleration features, leads to a classification accuracy of 74 percent in our experiments when combined with a Naive Bayes classifier.

Abstract-While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. In this paper, we introduce a haptic texture database which allows for a systematic analysis of feature candidates. The publicly available database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures. As a preliminary feature analysis, we test and compare six wellestablished features from audio and speech recognition together with a Gaussian Mixture Model-based classifier on our recorded free hand signals. Among the tested features, best results are achieved using Mel-Frequency Cepstral Coefficients (MFCCs), leading to a texture recognition accuracy of 80.2%.

Abstract-Applications involving indirect interpersonal communication, such as collaborative design/assembly/exploration of physical objects, can benefit strongly from the transmission of contact-based haptic media, in addition to the more traditional audiovisual media. Inclusion of haptic media has been shown to improve immersiveness, task performance, and the overall experience of task execution. While several decades of research have been dedicated to the acquisition, processing, coding, and display of audio and video streams, similar aspects for haptic streams have been addressed only recently.Simultaneous masking is a perceptual phenomenon widely exploited in the compression of audio data. In the first part of this paper, to the best of our knowledge, we present first-time empirical evidence for masking in the perception of wideband vibrotactile signals. Our results show that this phenomenon for haptics is very similar to its auditory analog. Signals closer in frequency to a powerful masker (25 dB above detection threshold) are masked more strongly (peak threshold-shifts of up to 28 dB) than those away from the masker (threshold-shifts of 15-20 dB). The masking curves approximately follow the masker's spectral profile. In the second part of this paper, we present a bitrate scalable haptic texture codec, which incorporates the masking model and describe its subjective and objective performance evaluation. Experiments show that we can drive down the codec output bitrate to a very low value of 2.3 kbps, without the subjects being able to reliable discriminate between the codec input and distorted output texture signals.

No abstract

We study the combination of the perceptual deadband (PD)-based haptic packet rate reduction scheme with the time domain passivity approach (TDPA) for time-delayed teleoperation and propose a novel energy prediction (EP) scheme that deals with the conservative behavior of the resulting controller. The PD approach leads to irregular packet transmission, resulting in degraded system transparency and reduced teleoperation quality when the PD approach is combined with the TDPA. The proposed method (PD+TDPA+EP) adaptively predicts the system energy during communication interruptions and allows for larger energy output. This achieves less conservative control and improves the teleoperation quality. Evaluation of the displayed impedance shows that the PD+TDPA+EP method achieves improved system transparency, both objectively and subjectively, when compared with related approaches from literature. According to a subjective user study, the PD+TDPA+EP method allows for a high packet rate reduction (up to 80 percent) without noticeably distorting the perceived interaction quality. We also show that the PD+TDPA+EP method is preferred over related approaches from literature in a direct comparison test. Thus, with the proposed PD+TDPA+EP method, a high data reduction and a high teleoperation quality are simultaneously achieved for time-delayed teleoperation.

No abstract

Abstract. In this paper we present a traffic control scheme for server to client communication in distributed haptic virtual environments (VE). We adopt a client-server architecture where the server manages the state consistency of the distributed VE, while haptic feedback is computed locally at each client. The update rate of network traffic from the server to the client is dynamically adapted by exploiting characteristics and limitations of human haptic perception. With this, an excellent tradeoff between network communication efficiency and perceptually robust rendering of haptic feedback is achieved. Subjective tests with two users collaboratively manipulating a common object show a packet rate reduction of up to 99% from the server to the clients without deteriorating haptic feedback quality.