Measuring Generalization of Visuomotor Perturbations in Wrist Movements Using Mobile Phones

Recent studies in motor control have shown that visuomotor rotations for reaching have narrow generalization functions: what we learn during movements in one direction only affects subsequent movements into close directions. Here we wanted to measure the generalization functions for wrist movement. To do so we had 7 subjects performing an experiment holding a mobile phone in their dominant hand. The mobile phone's built in acceleration sensor provided a convenient way to measure wrist movements and to run the behavioral protocol. Subjects moved a cursor on the screen by tilting the phone. Movements on the screen toward the training target were rotated and we then measured how learning of the rotation in the training direction affected subsequent movements in other directions. We find that generalization is local and similar to generalization patterns of visuomotor rotation for reaching

Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls

Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring

Metal oxide semiconductor gas sensors are utilised in a variety of different roles and industries. They are relatively inexpensive compared to other sensing technologies, robust, lightweight, long lasting and benefit from high material sensitivity and quick response times. They have been used extensively to measure and monitor trace amounts of environmentally important gases such as carbon monoxide and nitrogen dioxide. In this review the nature of the gas response and how it is fundamentally linked to surface structure is explored. Synthetic routes to metal oxide semiconductor gas sensors are also discussed and related to their affect on surface structure. An overview of important contributions and recent advances are discussed for the use of metal oxide semiconductor sensors for the detection of a variety of gases—CO, NOx, NH3 and the particularly challenging case of CO2. Finally a description of recent advances in work completed at University College London is presented including the use of selective zeolites layers, new perovskite type materials and an innovative chemical vapour deposition approach to film deposition

Developmental roadmap for antimicrobial susceptibility testing systems

Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability, optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions

Comparative Evaluation of Feature Extraction Methods for Human Motion Detection

Part 2: MHDW WorkshopInternational audienceIn this article we conduct an evaluation of feature extraction methods for the problem of human motion detection based on 3-dimensional inertial sensor data. For the purpose of this study, different preprocessing methods are used, and statistical as well as physical features are extracted from the motion signals. At each step, state-of-the-art methods are applied, and the produced results are finally compared in order to evaluate the importance of the applied feature extraction and preprocessing combinations, for the human activity recognition task

Baking Optimization as a Strategy to Extend Shelf-Life through the Enhanced Quality and Bioactive Properties of Pulse-Based Snacks

Food processing optimization can enhance the nutrient bioavailability, storage time, and stability of convenience foods. Baking is a heat and mass transfer process with a high impact on the shelf-life of the obtained product; a small variation in the parameters during baking can lead to significant changes in the end baked product, as it significantly affects the food nutrient profile and bioactive compounds. Response surface methodology (RSM) was used for mapping a response surface over a particular region of interest of baking conditions. The combined effect of the two factors (baking temperature and time) on the selected quality and bioactive parameters as dependent factors was evaluated in order to predict the optimal baking conditions which can facilitate the extended shelf-life of the product through maximizing the antioxidant bioactive properties. This design was used to develop models to predict the effect of the temperature and time baking profile and select those conditions where the quality and bioactive parameters reached a balance to obtain pulse snacks with a high quality, enhanced bioactive properties, and thus a longer shelf-life. Simultaneous optimization by the desirability function showed that a maximum temperature of 210 °C and a time of 14 min were the optimum conditions to produce a pulse-based snack with high antioxidant-antihypertensive activity and nutritional quality

MSF: An Efficient Mobile Phone Sensing Framework

Recent evolutions in smartphones, today provided with several sensors, have the strong processing capabilities needed to extract from raw sensed data sensor meaningful high-level views of the physical context around the user. A new promising research area called mobile sensing promotes completely decentralized sensing based on smartphone capabilities only. However, current mobile sensing solutions are not very mature; yet, because they are based on ad hoc software solutions tailored to one specific technical problem (e.g., power management, resource locking, etc.), they are difficult to reuse and integrate in different projects, and they do not focus on the performance efficiency of the monitoring support. To overcome those limitations, this paper proposes Mobile Sensing Framework (MSF), a flexible platform to ease the development of mobile sensing applications through the definition of a common set of facilities that mask all low-level technical details in reading and processing raw sensor data. MSF has been optimized also to enhance performances for Android-based systems, and we report an extensive set of experimental results that assess our architecture and quantitatively compare it with a selection of other mobile sensing systems by showing that MSF outperforms them by presenting lower CPU usage and memory footprints