Appropriation in the development of information systems for voluntary organisations

This paper describes two action research projects in co-located voluntary organizations, where both projects could be characterized as process failures. Our main focus is on the mechanisms of appropriation. The analytic framework as well as the basis for the action research projects, have been informed by activity theory. In particular we describe how ICT-based systems enforce structural discipline, how designers may misconceive the design task when designing for voluntary organisations, how the tension between use value and exchange value influences the use and thus appropriation of ICT in voluntary organisations, and finally what the possible impact of a lacking common conceptual basis may be. It is concluded that voluntary organisations exhibit unique features that should be taken into account in the design of ICT based support

Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction

Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user that not only bypasses, but also goes beyond what can be communicated using traditional means. In this form, implicit input can potentially provide significant improvements to human-computer interaction. This paper describes a selection of work done by Team PhyPA (Physiological Parameters for Adaptation) at the Technische Universität Berlin to use brain-computer interfacing to enrich human-computer interaction

Editorial: Using neurophysiological signals that reflect cognitive or affective state

The central question of this Frontiers Research Topic is: What can we learn from brain and other physiological signals about an individual's cognitive and affective state and how can we use this information? This question reflects three important issues which are addressed by the 22 articles in this volume: (1) the combination of central and peripheral neurophysiological measures; (2) the diversity of cognitive and affective processes reflected by these measures; and (3) how to apply these measures in real world applications

Consequences of critical interchain couplings and anisotropy on a Haldane chain

Effects of interchain couplings and anisotropy on a Haldane chain have been investigated by single crystal inelastic neutron scattering and density functional theory (DFT) calculations on the model compound SrNi$_2$V$_2$O$_8$. Significant effects on low energy excitation spectra are found where the Haldane gap ($\Delta_0 \approx 0.41J$; where $J$ is the intrachain exchange interaction) is replaced by three energy minima at different antiferromagnetic zone centers due to the complex interchain couplings. Further, the triplet states are split into two branches by single-ion anisotropy. Quantitative information on the intrachain and interchain interactions as well as on the single-ion anisotropy are obtained from the analyses of the neutron scattering spectra by the random phase approximation (RPA) method. The presence of multiple competing interchain interactions is found from the analysis of the experimental spectra and is also confirmed by the DFT calculations. The interchain interactions are two orders of magnitude weaker than the nearest-neighbour intrachain interaction $J$ = 8.7~meV. The DFT calculations reveal that the dominant intrachain nearest-neighbor interaction occurs via nontrivial extended superexchange pathways Ni--O--V--O--Ni involving the empty $d$ orbital of V ions. The present single crystal study also allows us to correctly position SrNi$_2$V$_2$O$_8$ in the theoretical $D$-$J_{\perp}$ phase diagram [T. Sakai and M. Takahashi, Phys. Rev. B 42, 4537 (1990)] showing where it lies within the spin-liquid phase.Comment: 12 pages, 12 figures, 3 tables PRB (accepted). in Phys. Rev. B (2015

Effects of High Heeled Gait on Knee Joint Mechanics

Numerous women wear high heeled shoes, whether it be a professional attire, part of an outfit for a ballroom gala, or just casual day to day wear. Often, the high heel of choice in these situations is the stiletto. These shoes adversely affect natural gait and have the potential to alter joint mechanics in the knee during gait. PURPOSE: This study is designed to analyze the impacts of wearing high heels, and if it puts the user at a higher risk of a degenerative condition with repeated use. We hypothesized that all of our dependent variables would see a significant increase when wearing high heels. METHODS: For the scope of this project, we narrowed our analysis to the knee joint and ground reaction force loading rate. We designed this study using a Cortex motion capture system along with force plates to conduct a series of experiments. Six college aged women with experience walking in high heels and no injury or condition that would adversely affect normal gait were selected to participate in motion analysis experiments. There are 4 trials conducted in total, which include walking, and performing a lateral stepping motion to simulate dancing, each under barefoot and high heeled conditions. The variables we set out to analyze include knee compressive force, flexion moment, varus and valgus moments, ground loading rate, and EMG peak activity for muscles including medial and lateral gastrocnemius, vastus lateralis, and biceps femoris. All force data was normalized by body weight to compare across participants. RESULTS: After processing the data and performing a statistical analysis using a paired T-test with significance of α \u3c 0.05, we found the variables with a significant difference between barefoot and high heels is the knee compressive force during gait (P = 0.001) and loading rate from the ground reaction force (P = 0.009). CONCLUSION: This indicates that wearing high heels can significantly increase knee joint loading

Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach

According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners' working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners' WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing electroencephalography (EEG) data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work

Using neurophysiological signals that reflect cognitive or affective state: Six recommendations to avoid common pitfalls

Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic “Using neurophysiological signals that reflect cognitive or affective state” we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently “cheating” with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications

Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes

Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons