Detecting pattern transitions in psychological time series – A validation study on the Pattern Transition Detection Algorithm (PTDA)

Viol, Kathrin; Schöller, Helmut; Kaiser, Andreas; Fartacek, Clemens; Aichhorn, Wolfgang; Schiepek, Günter

doi:10.1371/journal.pone.0265335

Cited by 14 publications

(14 citation statements)

References 62 publications

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“…Here we used the algorithms of Dynamic Complexity, dynamic inter-item correlations, and Recurrence Plots. The PTDA analysis was realized in Matlab [25]. The clear-cut pattern transition and the precursors of the sports injury are promising for further studies.…”

Section: Discussionmentioning

confidence: 99%

“…In order to identify phase transitions (qualitative transitions of patterns), the Pattern Transition Detection Algorithm (PTDA) has been developed [22, 25]. The method combines Change Point Analysis (CPA, [34]) which is applied to the empirical time series and to “second level” time series, as the DC measure, RPs, and Time Frequency Distributions (TFDs).…”

Section: Methodsmentioning

confidence: 99%

“…Critical events like sport injuries may implicate qualitative transitions in the mental dynamics of an athlete (so called “phase transitions”). Phase transitions can be identified by applying a recently published Pattern Transition Detection Algorithm [22, 25]. Before and after transitions not only the features of the involved time series are expected to change, but also the network dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of sports injuries by psychological process monitoring

et al. 2023

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Abstract. Objectives: Sports injuries usually have severe consequences for the concerned athletes as well as for trainers and teams. The question is if accidents can be predicted in specific cases. Can early-warning signals be detected in psychological time series? Methods: An App-based method of process-monitoring was applied for data collection of psychological parameters. Daily self-assessments using a Sports Process Questionnaire were realized by a professional soccer player during the after-care period of a psychiatric treatment. Methods for the prediction of critical events were applied (Dynamic Complexity, Recurrence Plots, dynamic inter-item correlations). Injuries may demarcate pattern transitions in the mental functioning of athletes, which could be identified by the Pattern Transition Detection Algorithm (PTDA). Results: Early-warning signals of the accident could be identified in the time series. Dynamic Complexity revealed a critical instability, Recurrence Plots a transient period, and the dynamic inter-item correlations a period of increased system coherence just before the accident. The PTDA revealed a phase transition at the occurring injury. Conclusions: Even if the analysis is based on a single case, the results are promising. Psychological self-reports allow a short-term prediction of bio-mechanical injuries and by this, can help to prevent them. Nonlinear measures can be applied to time series data collected by digital process monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of sports injuries by psychological process monitoring

et al. 2023

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“…We used two distinct methods: a change point algorithm (CPA) based on the Pruned Exact Linear Time (PELT) method (Killick et al, 2012) and a windowed nonlinear prediction algorithm (NLPA) based on S-Map (Sugihara & May, 1990). These methods were chosen as both were previously successfully applied to time series reflecting dynamics to detect transitions (Gorman et al, 2020; Viol et al, 2022). Additionally, each method employs a distinct way of identifying transitions.…”

Section: Methodsmentioning

confidence: 99%

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

Eijndhoven

Wiltshire

Halgas

et al. 2023

Journal of Cognitive Engineering and Decision Making

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During crisis situations, teams are more prone to coordination breakdowns that are characterized by a temporary, diminished ability to function effectively as a team. However, team research currently lacks robust approaches for identifying transitions from effective team functioning to coordination breakdowns. With the current study, we aimed to develop such robust approaches, and to deepen our understanding of how team coordination dynamics across various physiological signals reflect coordination breakdowns. Consequently, we used audiovisual data from four-person teams involved in a stressful collaborative game task to manually identify coordination breakdowns. Next, we set out to computationally identify coordination breakdowns by applying continuous measures of team coordination (windowed synchronization coefficient and multidimensional recurrence quantification analysis) to photoplethysmogram and electrodermal activity data obtained during the task, and identifying transitions therein with change point and nonlinear prediction algorithms. We found that our computational coordination breakdown identification approaches can identify up to 96% of the manually identified coordination breakdowns although our results also show that the precision of our approaches falls far behind. Our findings contribute theoretically and methodologically to the systematic investigation of coordination breakdowns, which may ultimately facilitate the support of teams in responding to and mitigating negative consequences of crisis situations.

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“…The importance of dynamic behaviour-based churn prediction for telecoms was emphasised by [5] due to both the dynamic nature of the customer activities and their subsequent behaviours. [6] have recently highlighted the improving opportunities for real-time monitoring and resulting ability to discover changes in psychological patterns. They focus on Change Point Analysis (CPA) and describe a Pattern Transition Detection Algorithm for psychological time series data with pattern transitions.…”

Section: Previous Workmentioning

confidence: 99%

Profiling Television Watching Behavior Using Bayesian Hierarchical Joint Models for Time-to-Event and Count Data

et al. 2022

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Customer churn prediction is a valuable task in many industries. In telecommunications it presents great challenges, given the high dimensionality of the data, and how difficult it is to identify underlying frustration signatures, which may represent an important driver regarding future churn behaviour. Here, we propose a novel Bayesian hierarchical joint model that is able to characterise customer profiles based on how many events take place within different television watching journeys, and how long it takes between events. The model drastically reduces the dimensionality of the data from thousands of observations per customer to 11 customer-level parameter estimates and random effects. We test our methodology using data from 40 BT customers (20 active and 20 who eventually cancelled their subscription) whose TV watching behaviours were recorded from October to December 2019, totalling approximately half a million observations. Employing different machine learning techniques using the parameter estimates and random effects from the Bayesian hierarchical model as features yielded up to 92% accuracy predicting churn, associated with 100% true positive rates and false positive rates as low as 14% on a validation set. Our proposed methodology represents an efficient way of reducing the dimensionality of the data, while at the same time maintaining high descriptive and predictive capabilities. We provide code to implement the Bayesian model at https://github.com/rafamoral/profiling_tv_watching_behaviour.

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Detecting pattern transitions in psychological time series – A validation study on the Pattern Transition Detection Algorithm (PTDA)

Cited by 14 publications

References 62 publications

Prediction of sports injuries by psychological process monitoring

Prediction of sports injuries by psychological process monitoring

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

Profiling Television Watching Behavior Using Bayesian Hierarchical Joint Models for Time-to-Event and Count Data

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