In real settings, natural body movements can be erroneously recognized by whole-body input systems as explicit input actions. We call body activity not intended as input actions "background activity." We argue that understanding background activity is crucial to the success of always-available whole-body input in the real world. To operationalize this argument, we contribute a reusable study methodology and software tools to generate standardized background activity datasets composed of data from multiple Kinect cameras, a Vicon tracker, and two high-definition video cameras. Using our methodology, we create an example background activity dataset for a television-oriented living room setting. We use this dataset to demonstrate how it can be used to redesign a gestural interaction vocabulary to minimize conflicts with the real world. The software tools and initial living room dataset are publicly available (this http URL).
Using body gestures for explicit input has been extensively studied @cite_33 @cite_23 @cite_20 @cite_7 @cite_21 @cite_37 @cite_2 @cite_26 . With always-available body input, the difference between gesture and non-gesture can be subtle, introducing false positives @cite_11 @cite_9 . Baudel and Beaudouin-Lafon @cite_32 call systems that interpret every gesture of the user as possible meaning as having immersion syndrome, ignoring that interacting with the system is not the users only ongoing activity.
@cite_37 @cite_26 @cite_33 @cite_7 @cite_9 @cite_21 @cite_32 @cite_23 @cite_2 @cite_20 @cite_11
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This paper explores how interaction with systems using touchless gestures can be made intuitive and natural. Analysis of 912 video clips of gesture production from a user study of 16 subjects communicating transitive actions (manipulation of objects with or without external tools) indicated that 1) dynamic pantomimic gestures where imagined tool object is explicitly held are performed more intuitively and easily than gestures where a body part is used to represent the tool object or compared to static hand poses and 2) gesturing while communicating the transitive action as how the user habitually performs the action (pantomimic action) is perceived to be easier and more natural than gesturing while communicating it as an instruction. These findings provide guidelines for the characteristics of gestures and user mental models one must consciously be concerned with when designing and implementing gesture vocabularies of touchless interaction. Psychological research findings suggest that humans rely on the combined visual channels of face and body more than any other channel when they make judgments about human communicative behavior. However, most of the existing systems attempting to analyze the human nonverbal behavior are mono-modal and focus only on the face. Research that aims to integrate gestures as an expression mean has only recently emerged. Accordingly, this paper presents an approach to automatic visual recognition of expressive face and upper-body gestures from video sequences suitable for use in a vision-based affective multi-modal framework. Face and body movements are captured simultaneously using two separate cameras. For each video sequence single expressive frames both from face and body are selected manually for analysis and recognition of emotions. Firstly, individual classifiers are trained from individual modalities. Secondly, we fuse facial expression and affective body gesture information at the feature and at the decision level. In the experiments performed, the emotion classification using the two modalities achieved a better recognition accuracy outperforming classification using the individual facial or bodily modality alone. In most applications of touch based humancomputer interaction, multi-touch gestures are used for directlymanipulating the interface such as scaling, panning, etc. In thispaper, we propose using multi-touch gesture as indirectcommand, such as redo, undo, erase, etc., for the operatingsystem. The proposed recognition system is guided by temporal,spatial and shape information. This is achieved using a graphembedding approach where all previous information are used.We evaluated our multi-touch recognition system on a set of 18different multi-touch gestures. With this graph embeddingmethod and a SVM classifier, we achieve 94.50 recognition rate.We believe that our research points out a possibility ofintegrating together raw ink, direct manipulation and indirectcommand in many gesture-based complex application such as asketch drawing application. Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human–computer robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02 on the validation set of IsoGD and 98.89 on SKIG).