Providing QoS for networked peers in distributed haptic virtual environments

Haptic information originates from a different human sense (touch), therefore the quality of service (QoS) required to supporthaptic traffic is significantly different from that used to support conventional real-time traffic such as voice or video. Each type ofnetwork impairment has different (and severe) impacts on the user’s haptic experience. There has been no specific provision of QoSparameters for haptic interaction. Previous research into distributed haptic virtual environments (DHVEs) have concentrated onsynchronization of positions (haptic device or virtual objects), and are based on client-server architectures.We present a new peerto-peer DHVE architecture that further extends this to enable force interactions between two users whereby force data are sent tothe remote peer in addition to positional information. The work presented involves both simulation and practical experimentationwhere multimodal data is transmitted over a QoS-enabled IP network. Both forms of experiment produce consistent results whichshow that the use of specific QoS classes for haptic traffic will reduce network delay and jitter, leading to improvements in users’haptic experiences with these types of applications.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.