Discrete vs. functional based data to analyze countermovement jump performance

While discrete point analysis (DPA) (e.g. peak power) is by far the most common method of analyzing movement data, it may have significant limitations because it ignores the vast majority of a signal’s data. In response, there has been a small but growing use of methods, such as functional data analysis (FDA), which allow an investigation of the underlying structure of the continuous signal and may therefore provide a more powerful analysis. However, a direct comparison between DPA and FDA has not been previously reported

Mining whole sample mass spectrometry proteomics data for biomarkers: an overview

In this paper we aim to provide a concise overview of designing and conducting an MS proteomics experiment in such a way as to allow statistical analysis that may lead to the discovery of novel biomarkers. We provide a summary of the various stages that make up such an experiment, highlighting the need for experimental goals to be decided upon in advance. We discuss issues in experimental design at the sample collection stage, and good practise for standardising protocols within the proteomics laboratory. We then describe approaches to the data mining stage of the experiment, including the processing steps that transform a raw mass spectrum into a useable form. We propose a permutation-based procedure for determining the significance of reported error rates. Finally, because of its general advantages in speed and cost, we suggest that MS proteomics may be a good candidate for an early primary screening approach to disease diagnosis, identifying areas of risk and making referrals for more specific tests without necessarily making a diagnosis in its own right. Our discussion is illustrated with examples drawn from experiments on bovine blood serum conducted in the Centre for Proteomic Research (CPR) at Southampton University

Investigating the role of assessment method on reports of déjà vu and tip-of-the-tongue states during standard recognition tests

Déjà vu and tip-of-the-tongue (TOT) are retrieval-related subjective experiences whose study relies on participant self-report. In four experiments (ns = 224, 273, 123 and 154), we explored the effect of questioning method on reported occurrence of déjà vu and TOT in experimental settings. All participants carried out a continuous recognition task, which was not expected to induce déjà vu or TOT, but were asked about their experiences of these subjective states. When presented with contemporary definitions, between 32% and 58% of participants nonetheless reported experiencing déjà vu or TOT. Changing the definition of déjà vu or asking participants to bring to mind a real-life instance of déjà vu or TOT before completing the recognition task had no impact on reporting rates. However, there was an indication that changing the method of requesting subjective reports impacted reporting of both experiences. More specifically, moving from the commonly used retrospective questioning (e.g. “Have you experienced déjà vu?”) to free report instructions (e.g. “Indicate whenever you experience déjà vu.”) reduced the total number of reported déjà vu and TOT occurrences. We suggest that research on subjective experiences should move toward free report assessments. Such a shift would potentially reduce the presence of false alarms in experimental work, thereby reducing the overestimation of subjective experiences prevalent in this area of research.Publisher PDFPeer reviewe

Evidence for the contribution of a threshold retrieval process to semantic memory

Maria Kempnich was supported by the University of St Andrews University Research Internship Placement Scheme and a Walker Trust Travel Scholarship. Josephine Urquhart was supported by the Economic and Social Research Council 1+3 Scheme.It is widely held that episodic retrieval can recruit two processes, a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely, and in spite of its importance for everyday memory, the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver operating characteristics (ROCs) for a marker of a threshold retrieval process. We then fit observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual process signal detection models) and a novel dual process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions used here, we anticipated the DP-RR model would best fit the data obtained from our semantic task. In Experiment 1 (506 participants), we found evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. In Experiment 2 (316 participants), we found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated, suggesting the relationship between the analogous memory processes is not straightforward. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.PostprintPeer reviewe

Automatic detection, extraction and analysis of unrestrained gait using a wearable sensor system

Within this paper we demonstrate thee ffectiveness of a novel body-worn gait monitoring and analysis framework to both accurately and automatically assess gait during ’freeliving’ conditions. Key features of the system include the ability to automatically identify individual steps within specific gait conditions, and the implementation of continuous waveform analysis within an automated system for the generation of temporally normalized data and their statistical comparison across subjects

Classification of continuous vertical ground reaction forces

The aim of this study is to assess and compare the performance of com- monly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the perfor- mance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred-and twenty two verti- cal jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82 %) and hierarchical clustering (normalized scores; 85 %) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78 %). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to iden- tify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis

Performance related factors in countermovement jumps: identified using a continuous subgroup analysis approach

The aim of this study was to examine the benefit of utilizing a subgroup analysis design over a single group analysis design, and determine if performance related factors differ across individuals in countermovement jumping. Joint kinematics and kinetics were used to cluster 122 individuals into four groups, based on their movement strategy. The ability to describe jump height across a single group and subgroup analysis design was assessed to measure the performance of both analysis designs, and performance related factors were identified across the generated clusters. Findings highlight a greater ability of the subgroup analysis design to describe jump height, indicating a benefit of utilizing a subgroup analysis. This is supported by the performance related factors identified, which differed across individuals

Cross-comparison of the performance of discrete, phase and functional data analysis to describe a dependent variable

The aim of this study was to assess and contrast the ability of discrete point, functional principal component analysis (fPCA) and analysis of characterizing phases (ACP) to describe a dependent variable (jump height) from vertical ground reaction force curves captured during the propulsion phase of a countermovement jump. A stepwise multiple regression analysis was used to assess the ability of each data analysis technique. The order of effectiveness (high to low) was ACP, fPCA and discrete point analysis. Discrete point analysis was not able to generate strong predictors and detected also erroneous variables. FPCA and ACP detected similar factors to describe jump height. However, ACP performed better than fPCA because it considers the time and magnitude domain separately and in combination and it examines key-phases, without the influence of non-key-phases

Analysis of characterizing phases on waveforms – an application to vertical jumps

The aim of this study is to propose a novel data analysis approach, ‘Analysis of Characterizing Phases’ (ACP), that detects and examines phases of variance within a sample of curves utilizing the time, magnitude and magnitude-time domain; and to compare the findings of ACP to discrete point analysis in identifying performance related factors in vertical jumps. Twenty five vertical jumps were analyzed. Discrete point analysis identified the initial-to-maximum rate of force development (p = .006) and the time from initial-to-maximum force (p = .047) as performance related factors. However, due to inter-subject variability in the shape of the force curves (i.e non-, uni- and bi-modal nature), these variables were judged to be functionally erroneous. In contrast, ACP identified the ability to: apply forces for longer (p < .038), generate higher forces (p < .027) and produce a greater rate of force development (p < .003) as performance related factors. Analysis of Characterizing Phases showed advantages over discrete point analysis in identifying performance related factors because it: (i) analyses only related phases, (ii) analyses the whole data set, (iii) can identify performance related factors that occur solely as a phase, (iv) identifies the specific phase over which differences occur, and (v) analyses the time, magnitude and combined magnitude-time domains