Development of a foot interface to control supernumerary robotics limbs

Supernumerary robotic limbs (SRLs) can be used to provide a person with extra arms to help with difficult tasks. For example, a task that normally requires three hands to complete could be accomplished by just one person with an SRL. One way to control an SRL and still leave both hands available is to use the foot. This paper describes two parts of developing this foot interface: characterizing the range of forces that the foot can apply, and prototyping systems for different control methods. First, a small sample of data was collected to learn how much force the foot can apply in different degrees of freedom including rotations about the ankle and translations of the foot. Typically, the most force could be exerted in plantarflexion. These forces were then used with a kinematic model of the leg to calculate the torques applied by each joint. It showed that, in most cases, the joints at the hip and ankle see the greatest torque. Two foot devices are currently being developed: a Foot Pedal and a Foot Plate. The former uses rotations of the foot in different directions to control the robotic arm while the latter uses force inputs from a stationary foot. For the Foot Pedal, each degree of freedom was outfitted with a spring return mechanism so that it could be used as a rate control device. Now, when the user relaxes their foot the pedal will snap back to its resting position. For the Foot Plate, software was developed to read and scale the inputs using a six-axis force-torque sensor. Additionally, adjustable scaling was implemented to accommodate users of different abilities. In the future, an experiment will compare the effectiveness of these two devices for both rate and position control methods

The Emerging Role of Women in the New Executive Workforce

No abstract is currently available

Care staff intentions to support adults with an intellectual disability to engage in physical activity: An application of the Theory of Planned Behaviour

Researchers suggest that people with an intellectual disability (ID) undertake less physical activity than the general population and many rely, to some extent, on others to help them to access activities. The Theory of Planned Behaviour (TPB) model was previously found to significantly predict the intention of care staff to facilitate a healthy diet in those they supported. The present study examined whether the TPB was useful in predicting the intentions of 78 Scottish care staff to support people with ID to engage in physical activity. Regression analyses indicated that perceived behavioural control was the most significant predictor of both care staff intention to facilitate physical activity and reported physical activity levels of the people they supported. Attitudes significantly predicted care staff intention to support physical activity, but this intention was not itself significantly predictive of reported activity levels. Increasing carers' sense of control over their ability to support clients' physical activity may be more effective in increasing physical activity than changing their attitudes towards promoting activit

Hosting the Third Annual Young Women in Agriculture Career and Leadership Seminar

Agriculture has traditionally been a male-dominated industry; however, recent years have shown a significant growth in female presence. Women are beginning to take leadership roles both on the farm and within advocacy, lobbying, and business entities in the industry. According to the latest agricultural census data from the United States Department of Agriculture (USDA), in 2012, women farmers controlled seven percent of the nation’s farmland (Census, 2014). Additionally, women held leadership roles in 14 percent of the nation’s two million farms (Census, 2014). This data is several years old, and likely the numbers have grown since the last census was taken. The National Agricultural Library’s website articulates the variety of roles that are available to women in agriculture today: “Women fill many roles in agriculture. They are farmers and farm workers, ranchers, agricultural researchers, educators and agri-business people” (Agricultural Library, 2014). This seminar is directed primarily at women in California, though ideally it could be replicated anywhere. California leads the nation in agricultural output by a large margin, and as such is a role model for the rest of the nation. There are more and more jobs each year available to women in agriculture; it is an exciting field for women to be in today. It is also important to note that careers in agriculture does not necessarily mean “on the farm.” There are opportunities for everything from educators to lawyers to accountants and credit managers. Ideally this seminar will host a speaker who can address the range of available careers

Dose–response effect of a whey protein preload on within-day energy intake in lean subjects

The effect of consuming different amounts of whey protein on appetite and energy intake was investigated in two separate studies using randomised, crossover designs. Healthy-weight men and women (range: BMI 19·0–25·0 kg/m2, age 19·4–40·4 years) consumed one of four 400 ml liquid preloads, followed by an ad libitum test meal 90 min later. In study 1, preloads were 1675 kJ with 12·5, 25 or 50 % of energy from protein, and in study 2, preloads were 1047 kJ with 10, 20 or 40 % energy from protein. Flavoured water was used as the control in both the studies. Appetite ratings were collected immediately before 30, 60 and 90 min after consuming the preloads; and immediately, 30 and 60 min after consuming the test meal. In study 1, energy intake following the control preload (4136 (sem 337) kJ) was significantly higher than each of the 12·5 % (3520 (sem 296) kJ), 25 % (3384 (sem 265) kJ) and 50 % (2853 (sem 244) kJ) protein preloads (P < 0·05). Intake after the 12·5 % preload was significantly higher than following 25 and 50 % preloads (P < 0·05). In study 2, energy intake following the control preload (4801 (sem 325) kJ) was higher than following the 10 % (4205 (sem 310) kJ), 20 % (3988 (sem 250) kJ) and 40 % (3801 (sem 245) kJ) protein preloads (P < 0·05). There were no differences in subjective appetite ratings between preloads in either study. These findings indicate a dose–response effect of protein content of the preload on energy intake at a subsequent meal

Automated Bayesian model development for frequency detection in biological time series

<p>Abstract</p> <p>Background</p> <p>A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies.</p> <p>Results</p> <p>In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing.</p> <p>Conclusions</p> <p>Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.</p