scholarly journals Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1720
Author(s):  
Pieter Van den Berghe ◽  
Maxim Gosseries ◽  
Joeri Gerlo ◽  
Matthieu Lenoir ◽  
Marc Leman ◽  
...  
Keyword(s):  
Real Time ◽  
Change Point ◽  
Major Change ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Biofeedback System ◽  
Individualized Approach ◽  
Tibial Acceleration ◽  
Point Detection

A method is presented for detecting changes in the axial peak tibial acceleration while adapting to self-discovered lower-impact running. Ten runners with high peak tibial acceleration were equipped with a wearable auditory biofeedback system. They ran on an athletic track without and with real-time auditory biofeedback at the instructed speed of 3.2 m·s−1. Because inter-subject variation may underline the importance of individualized retraining, a change-point analysis was used for each subject. The tuned change-point application detected major and subtle changes in the time series. No changes were found in the no-biofeedback condition. In the biofeedback condition, a first change in the axial peak tibial acceleration occurred on average after 309 running gait cycles (3′40″). The major change was a mean reduction of 2.45 g which occurred after 699 running gait cycles (8′04″) in this group. The time needed to achieve the major reduction varied considerably between subjects. Because of the individualized approach to gait retraining and its relatively quick response due to a strong sensorimotor coupling, we want to highlight the potential of a stand-alone biofeedback system that provides real-time, continuous, and auditory feedback in response to the axial peak tibial acceleration for lower-impact running.

scholarly journals Examination of Changes in Flood Data in Australia

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1734 ◽  
Author(s):  
Elias Ishak ◽  
Ataur Rahman
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Significance Level ◽  
Monotonic Trend ◽  
Point Analysis ◽  
Eastern Australia ◽  
Change In The Mean ◽  
The Mean ◽  
Point Detection

This study performs a simultaneous evaluation of gradual and abrupt changes in Australian annual maximum (AM) flood data using a modified Mann–Kendall and Pettitt change-point detection test. The results show that AM flood data in eastern Australia is dominated by downward trends. Depending on the significance level and study period under consideration, about 8% to 33% of stations are characterised by significant trends, where over 85% of detected significant trends are downward. Furthermore, the change-point analysis shows that the percentages of stations experiencing one abrupt change in the mean or in the direction of the trend are in the range of 8% to 33%, of which over 50% occurred in 1991, with a mode in 1995. Prominent resemblance between the monotonic trend and change-point analysis results is also noticed, in which a negative shift in the mean is observed at catchments that exhibited downward trends, and a positive shift in the mean is observed in the case of upward trends. Trend analysis of the segmented AM flood series based on their corresponding date indicates an absence of a significant trend, which may be attributed to the false detection of trends when the AM flood data are characterised by a shift in its mean.

scholarly journals Examining nonstationarity in the recruitment dynamics of fishes using Bayesian change point analysis

2017 ◽  
Vol 74 (5) ◽  
pp. 751-765 ◽  
Author(s):  
Tommi A. Perälä ◽  
Douglas P. Swain ◽  
Anna Kuparinen
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Model Parameters ◽  
Point Analysis ◽  
Stock Recruitment ◽  
Novel Method ◽  
Point Detection ◽  
Time Invariant

Marine ecosystems can undergo regime shifts, which result in nonstationarity in the dynamics of the fish populations inhabiting them. The assumption of time-invariant parameters in stock–recruitment models can lead to severe errors when forecasting renewal ability of stocks that experience shifts in their recruitment dynamics. We present a novel method for fitting stock–recruitment models using the Bayesian online change point detection algorithm, which is able to cope with sudden changes in the model parameters. We validate our method using simulations and apply it to empirical data of four demersal fishes in the southern Gulf of St. Lawrence. We show that all of the stocks have experienced shifts in their recruitment dynamics that cannot be captured by a model that assumes time-invariant parameters. The detected shifts in the recruitment dynamics result in clearly different parameter distributions and recruitment predictions between the regimes. This study illustrates how stock–recruitment relationships can experience shifts, which, if not accounted for, can lead to false predictions about a stock’s recovery ability and resilience to fishing.

scholarly journals Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 274
Author(s):  
Ourania Theodosiadou ◽  
Kyriaki Pantelidou ◽  
Nikolaos Bastas ◽  
Despoina Chatzakou ◽  
Theodora Tsikrika ◽  
...  
Keyword(s):  
Time Series ◽  
Change Point ◽  
Hate Speech ◽  
Change Point Detection ◽  
Change Points ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Depth Analysis ◽  
Textual Data ◽  
Point Detection

Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.

scholarly journals Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 142246-142260 ◽  
Author(s):  
Sotiris Skaperas ◽  
Lefteris Mamatas ◽  
Arsenia Chorti
Keyword(s):  
Real Time ◽  
Change Point ◽  
Change Point Analysis ◽  
Video Content ◽  
Point Analysis ◽  
Content Popularity

scholarly journals Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

2018 ◽  
Vol 119 (4) ◽  
pp. 1394-1410 ◽  
Author(s):  
Sile Hu ◽  
Qiaosheng Zhang ◽  
Jing Wang ◽  
Zhe Chen
Keyword(s):  
Real Time ◽  
Spike Activity ◽  
Change Point ◽  
Particle Filtering ◽  
Jump Process ◽  
Change Point Detection ◽  
Stochastic Jump ◽  
Abrupt Changes ◽  
Non Gaussian ◽  
Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

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