Remote sensing technologies for physiotherapy assessment

The paper presents a set of remote, unobtrusive sensing technologies that can be used in upper and lower limbs rehabilitation monitoring. The advantages of using sensors based on microwave Doppler radar or infrared technologies for physiotherapy assessment are discussed. These technologies allow motion sensing at distance from monitored subject, reducing thus the discomfort produced by some wearable technologies for limbs movement assessment. The microwave radar that may be easily hidden into environment by nonmetallic parts allows remote sensing of human motion, providing information on user movements characteristics and patterns. The infrared technologies - infrared LEDs from Leap-Motion, infrared laser from Kinect depth sensor, and infrared thermography can be used for different movements' parameters evaluation. Visible for users, Leap-motion and Kinect sensors assure higher accuracy on body parts movements' detection at low computation load. These technologies are commonly used for virtual reality (VR) and augmented reality (AR) scenarios, in which the user motion patterns and the muscular activity might be analyzed. Thermography can be employed to evaluate the muscular loading. Muscular activity during movements training in physiotherapy can be estimated through skin temperature measurement before and after physical training. Issues related to the considered remote sensing technologies such as VR serious game for motor rehabilitation, signal processing and experimental results associated with microwave radar, infrared sensors and thermography for physiotherapy sensing are included in the paper.info:eu-repo/semantics/acceptedVersio

A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Motivation: Epistasis, the presence of gene–gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes, but computational costs have made its application to genome-wide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis

The State of the Art in Hydrodynamic Turbulence: Past Successes and Future Challenges

We present a personal view of the state of the art in turbulence research. We summarize first the main achievements in the recent past, and then point ahead to the main challenges that remain for experimental and theoretical efforts.Comment: 19 pages, 6 figures, submitted to Physica

Reputation and credit based incentive mechanism for data-centric message delivery in delay tolerant networks

In a Data-centric Delay Tolerant Networks (DTNs), it is essential for nodes to cooperate in message forwarding in order to enable successful delivery of a message in an opportunistic fashion with nodes having their social interests defined. In the data-centric dissemination protocol proposed here, a source annotates messages (images) with keywords, and then intermediate nodes are presented with an option of adding keyword-based annotations in order to create higher content strength messages on path toward the destination. Hence, contents like images get enriched as there is situation evolution or learned by these intermediate nodes, such as in a battlefield, or in a disaster situation. Nodes might turn selfish and not participate in relaying messages due to relative scarcity of battery and storage capacity in mobile devices. Therefore, in addition to content enrichment, an incentive mechanism is proposed in this thesis which considers factors like message quality, battery usage, level of interests, etc. for the calculation of incentives. Moreover, with the goal of preventing the nodes from turning malicious by adding inappropriate message tags in the quest of acquiring more incentive, a distributed reputation model (DRM) is developed and consolidated with the proposed incentive scheme. DRM takes into account inputs from multiple users like ratings for the relevance of annotations in the message, message quality, etc. The proposed scheme safeguards the network from congestion due to uncooperative or selfish nodes in the system. The performance evaluation shows that our approach delivers more high priority and high quality messages while reducing traffic at a slightly lower message delivery ratio compared to ChitChat --Abstract, page iv