No abstract

This paper is focused on the multimodal analysis of patient performance, carried out by means of robotic technology and wearable sensors, and aims at providing quantitative measure of biomechanical and motion planning features of arm motor control following rehabilitation. Upper-limb robotic therapy was administered to 24 community-dwelling persons with chronic stroke. Performance indices on patient motor performance were computed from data recorded with the InMotion2 robotic machine and a magneto-inertial sensor. Motor planning issues were investigated by means of techniques of motion decomposition into submovements. A linear regression analysis was carried out to study correlation with clinical scales. Robotic outcome measures showed a significant improvement of kinematic motor performance; improvement of dynamic components was more significant in resistive motion and highly correlated with MP. The analysis of motion decomposition into submovements showed an important change with recovery of submovement number, amplitude and order, tending to patterns measured in healthy subjects. Preliminary results showed that arm biomechanical functions can be objectively measured by means of the proposed set of performance indices. Correlation with MP is high, while correlation with FM is moderate. Features related to motion planning strategies can be extracted from submovement analysis.

Abstract-The possibility of controlling dexterous hand prostheses by using a direct connection with the nervous system is particularly interesting for the significant improvement of the quality of life of patients, which can derive from this achievement. Among the various approaches, peripheral nerve based intra fascicular electrodes are excellent neural interface candidates, representing an excellent compromise between high selectivity and relatively low invasiveness. Moreover, this approach has undergone preliminary testing in human volunteers and has shown promises.In this manuscript, we investigated whether the use of intrafascicular electrodes can be used to decode multiple sensory and motor information channels with the aim to develop a finite state algorithm that may be employed to control neuroprostheses and neuro-controlled hand prostheses. The results achieved both in animal and human experiments show that the combination of multiple sites recordings and advanced signal processing techniques (such as wavelet denoising and spike sorting algorithms) can be used to identify both sensory stimuli (in animal models) and motor commands (in a human volunteer). These findings have interesting implications, which should be investigated in future experiments.

Interaction with machines is mediated by Human-Machine Interfaces (HMIs). Brainmachine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.

High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each group toward a common location. Clustering and shrinking effects induced by the BNP-Lasso prior are well suited for the extraction of causal networks from time series, since they account for some stylized facts in realworld networks, which are sparsity, communities structures and heterogeneity in the edges intensity. In order to fully capture the richness of the data and to achieve a better understanding of financial and macroeconomic risk, it is therefore crucial that the model used to extract network accounts for these stylized facts.