A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Mediated Interactions and Musical Expression - A Survey

This chapter surveys the field of technologically mediated musical interaction and technologically enhanced musical expression. We look at several new technologies that enable new ways of musical expression and interaction, explore the micro-coordination that occurs in collaborative musical performance and look at the preconditions for human-agent interaction through co-creative agents. This survey collects a number of insights that will enable us to create better technological artifacts for musical expression and collaboration

Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature sets is compared using the kappa statistic which has very recently become popular in sleep staging research. A variable duration of the EEG segment (or epoch) to decide on the sleep stage is also analyzed. Spectral-domain, time-domain, linear, and nonlinear features are compared in terms of performance and two types of machine learning approaches (random forests and support vector machines) are assessed. We have determined that frontal EEG signals, with spectral linear features, epoch durations between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation

Deep learning approach for ECG-based automatic sleep state classification in preterm infants

Preterm infant neuronal development is related to the distribution of their sleep states. The distribution changes throughout development. Automated sleep state monitoring can become a powerful aid for development monitoring in preterm infants. Three datasets including 34 preterm infants and a total of 18,018 30 s manually annotated sleep intervals (sleep-epochs) were analyzed in this study. The annotation of sleep states includes active sleep, quiet sleep, intermediate sleep, wake, and caretaking. Four different recurrent neuronal network architectures were compared for two-state, three-state, and all-state analysis. A sequential network was used to compare long- and short-term memory and gated recurrent unit models. The other network architectures were based on the popular ResNet and ResNext architectures utilizing residual connection for more depth. The most essential sleep states, active and quiet sleep, could be separated with a kappa of 0.43 ± 0.08. Quiet versus caretaking and wake showed a kappa of 0.44 ± 0.01. The three state classifications of active versus quiet versus intermediate sleep resulted in a kappa of 0.35 ± 0.07 and active versus quiet versus wake and caretaking resulted in a kappa of 0.33 ± 0.04. The all-state classification was underperforming with probably due to difficulty in separating subtle differences between all states and a lack of sufficient training data for the minority classes