Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms

Towards Enhanced Biofeedback Mechanisms for Upper Limb Rehabilitation in Stroke

This paper highlights a progressive rehabilitation strategy which details the development of a suite of biomedical feedback sensors to promote enhanced rehabilitation after stroke. The strategy involves promoting total upper limb recovery by focusing on aspects of each stage of post-stroke rehabilitation. For a patient with a complete absence of movement in the affected upper limb, brain signals will be acquired using ear-Infrared Spectroscopy (IRS) combined with motor imagery to move a robotic splint. Once residual movement has returned, EMG signals from the muscles will be detected and used to power a robotic splint. For later stages and continuous enhanced rehabilitation of the upper limb, a Sensor Glove will be used for intense rehabilitation exercises of the hand. These combined techniques cover all levels of ability for total upper limb rehabilitation and will be used to provide positive feedback and motivation for patients

On Consistency and Network Latency in Distributed Interactive Applications: A Survey—Part I

This paper is the first part of a two-part paper that documents a detailed survey of the research carried out on consistency and latency in distributed interactive applications (DIAs) in recent decades. Part I reviews the terminology associated with DIAs and offers definitions for consistency and latency. Related issues such as jitter and fidelity are also discussed. Furthermore, the various consistency maintenance mechanisms that researchers have used to improve consistency and reduce latency effects are considered. These mechanisms are grouped into one of three categories, namely time management, Information management and system architectural management. This paper presents the techniques associated with the time management category. Examples of such mechanisms include time warp, lock step synchronisation and predictive time management. The remaining two categories are presented in part two of the survey

An Information-Based Dynamic Extrapolation Model for Networked Virtual Environments

Various Information Management techniques have been developed to help maintain a consistent shared virtual world in a Networked Virtual Environment. However, such techniques have to be carefully adapted to the application state dynamics and the underlying network. This work presents a novel framework that minimizes inconsistency by optimizing bandwidth usage to deliver useful information. This framework measures the state evolution using an information model and dynamically switches extrapolation models and the packet rate to make the most information-efficient usage of the available bandwidth. The results shown demonstrate that this approach can help optimize consistency under constrained and time-varying network conditions

A Novel Convergence Algorithm for the Hybrid Strategy Model Packet Reduction Technique

Several approaches exist for maintaining consistency in Distributed Interactive Applications. Among these are techniques such as dead reckoning which use prediction algorithms to approximate actual user behaviour and thus reduce the number of update packets required to maintain spatial consistency. The Hybrid Strategy Model operates in a similar way, exploiting long-term patterns in user behaviour whenever possible. Otherwise it simply adopts a short-term model. A major problem with these techniques is the reconstruction of the local behaviour at a remote node. Using the modelled dynamics directly can result in unnatural and sudden jumps in position where updates occur. Convergence algorithms are thus required to smoothly reconstruct remote behaviour from discontinuous samples of the actual local behaviour. This paper makes two important contributions. Primarily, it proposes a novel convergence approach for the Hybrid Strategy Model. Secondly, and more fundamentally, it exposes a lack of suitable and quantifiable measures of different convergence techniques. In this paper the standard smoothing algorithm employed by DIS is used as a benchmark for comparison purposes

Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms

On network latency in distributed interactive applications

This paper has three objectives. Firstly it describes the historical development of Distributed Interactive Applications. It then defines network latency. Finally it describes a new approach to masking network latency in Distributed Interactive Applications called the strategy model approach. This approach derives from the on-going PhD studies of one of the authors. A software application to gather strategy data from users is described in detail and an example of deriving a user strategy is given

A Novel Convergence Algorithm for the Hybrid Strategy Model Packet Reduction Technique

Several approaches exist for maintaining consistency in Distributed Interactive Applications. Among these are techniques such as dead reckoning which use prediction algorithms to approximate actual user behaviour and thus reduce the number of update packets required to maintain spatial consistency. The Hybrid Strategy Model operates in a similar way, exploiting long-term patterns in user behaviour whenever possible. Otherwise it simply adopts a short-term model. A major problem with these techniques is the reconstruction of the local behaviour at a remote node. Using the modelled dynamics directly can result in unnatural and sudden jumps in position where updates occur. Convergence algorithms are thus required to smoothly reconstruct remote behaviour from discontinuous samples of the actual local behaviour. This paper makes two important contributions. Primarily, it proposes a novel convergence approach for the Hybrid Strategy Model. Secondly, and more fundamentally, it exposes a lack of suitable and quantifiable measures of different convergence techniques. In this paper the standard smoothing algorithm employed by DIS is used as a benchmark for comparison purposes

Harnessing brain power at NUI Maynooth

The Department of Electronic Engineering at NUI Maynooth is involved in exciting interdisciplinary work in the biomedical, digital signal processing, control and electronic systems areas. Here Tomas Ward, Seán McLoone and Shirley Coyle highlight three specific projects

A Physics-Aware Dead Reckoning Technique for Entity State Updates in Distributed Interactive Applications

This paper proposes a novel entity state update technique for physics-rich environments in peer-to-peer Distributed Interactive Applications. The proposed technique consists of a dynamic authority scheme for shared objects and a physics-aware dead reckoning model with an adaptive error threshold. The former is employed to place a bound on the overall inconsistency present in shared objects, while the latter is implemented to minimise the instantaneous inconsistency during users’ interactions with shared objects. The performance of the proposed entity state update mechanism is validated using a simulated application