Balancing the playing field: collaborative gaming for physical training.

BACKGROUND: Multiplayer video games promoting exercise-based rehabilitation may facilitate motor learning, by increasing motivation through social interaction. However, a major design challenge is to enable meaningful inter-subject interaction, whilst allowing for significant skill differences between players. We present a novel motor-training paradigm that allows real-time collaboration and performance enhancement, across a wide range of inter-subject skill mismatches, including disabled vs. able-bodied partnerships. METHODS: A virtual task consisting of a dynamic ball on a beam, is controlled at each end using independent digital force-sensing handgrips. Interaction is mediated through simulated physical coupling and locally-redundant control. Game performance was measured in 16 healthy-healthy and 16 patient-expert dyads, where patients were hemiparetic stroke survivors using their impaired arm. Dual-player was compared to single-player performance, in terms of score, target tracking, stability, effort and smoothness; and questionnaires probing user-experience and engagement. RESULTS: Performance of less-able subjects (as ranked from single-player ability) was enhanced by dual-player mode, by an amount proportionate to the partnership's mismatch. The more abled partners' performances decreased by a similar amount. Such zero-sum interactions were observed for both healthy-healthy and patient-expert interactions. Dual-player was preferred by the majority of players independent of baseline ability and subject group; healthy subjects also felt more challenged, and patients more skilled. CONCLUSION: This is the first demonstration of implicit skill balancing in a truly collaborative virtual training task leading to heightened engagement, across both healthy subjects and stroke patients.

Use of mutual information arrays to predict coevolving sites in the full length HIV gp120 protein for subtypes B and C.

It is well established that different sites within a protein evolve at different rates according to their role within the protein; identification of these correlated mutations can aid in tasks such as ab initio protein structure, structure function analysis or sequence alignment. Mutual Information is a standard measure for coevolution between two sites but its application is limited by signal to noise ratio. In this work we report a preliminary study to investigate whether larger sequence sets could circumvent this problem by calculating mutual information arrays for two sets of drug naïve sequences from the HIV gp120 protein for the B and C subtypes. Our results suggest that while the larger sequences sets can improve the signal to noise ratio, the gain is offset by the high mutation rate of the HIV virus which makes it more difficult to achieve consistent alignments. Nevertheless, we were able to predict a number of coevolving sites that were supported by previous experimental studies as well as a region close to the C terminal of the protein that was highly variable in the C subtype but highly conserved in the B subtype.