scholarly journals Effect of sampling frequency on fractal fluctuations during treadmill walking

2019 ◽  
Author(s):  
Vivien Marmelat ◽  
Austin Duncan ◽  
Shane Meltz
Keyword(s):  
Motion Capture ◽  
Event Detection ◽  
Temporal Dynamics ◽  
Intraclass Correlation ◽  
Sampling Frequency ◽  
Treadmill Walking ◽  
Fluctuation Analysis ◽  
Detection Accuracy ◽  
Gait Dynamics ◽  
Fractal Analyses

AbstractThe temporal dynamics of stride-to-stride fluctuations in steady-state walking reveal important information about locomotor control and can be quantified using so-called fractal analyses, notably the detrended fluctuation analysis (DFA). Gait dynamics are often collected during treadmill walking using 3-D motion capture to identify gait events from kinematic data. The sampling frequency of motion capture systems may impact the precision of event detection and consequently impact the quantification of stride-to-stride variability. This study aimed i) to determine if collecting multiple walking trials with different sampling frequency affects DFA values of spatiotemporal parameters during treadmill walking, and ii) to determine the reliability of DFA values across downsampled conditions. Seventeen healthy young adults walked on a treadmill while their gait dynamics was captured using different sampling frequency (60, 120 and 240 Hz) in each condition. We also compared data from the highest sampling frequency to downsampled versions of itself. We applied DFA to the following time series: step length, time and speed, and stride length, time and speed. Reliability between experimental conditions and between downsampled conditions were measured with 1) intraclass correlation estimates and their 95% confident intervals, calculated based on a single-measurement, absolute-agreement, two-way mixed-effects model (ICC 3,1), and 2) Bland-Altman bias and limits of agreement. Both analyses revealed a poor reliability of DFA results between conditions using different sampling frequencies, but a relatively good reliability between original and downsampled spatiotemporal variables. Collectively, our results suggest that using sampling frequencies of 120 Hz or 240 Hz provide similar results, but that using 60 Hz may alter DFA values. We recommend that gait kinematics should be collected at around 120 Hz, which provides a compromise between event detection accuracy and processing time.

scholarly journals Temporal patterns and ensemble learning for environmental sound recognition

2021 ◽  
Author(s):  
Wenjun Yang
Keyword(s):  
Event Detection ◽  
Temporal Dynamics ◽  
Total Error ◽  
Nonnegative Matrix ◽  
Training Data ◽  
Sound Recognition ◽  
Environmental Sound ◽  
Time Frequency ◽  
Ensemble Techniques ◽  
Environmental Sound Recognition

This thesis explores features characterizing the temporal dynamics and the use of ensemble techniques to improve the performances of environmental sound recognition (ESR) system. Firstly, for acoustic scene classification (ASC), local binary pattern (LBP) technique is applied to extract the temporal evolution of Mel-frequency cepstral coefficients (MFCC) features, and the D3C ensemble classifier is adopted to optimize the system performance. The results show that the proposed method achieved a classification improvement of 8% compared to the baseline system. Secondly, a new approach for sound event detection (SED) using Nonnegative Matrix Factor 2- D Deconvolution (NMF2D) and RUSBoost techniques is presented. The idea is to capture the two dimensional joint spectral and temporal information from the time-frequency representation (TFR) while possibly separating the sound mixture into several sources. Besides, the RUSBoost ensemble technique is utilized in the event detection process to alleviate class imbalance in the training data. This method reduced the total error rate by 5% compared to the baseline method.

scholarly journals A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Shukui Zhang ◽  
Hao Chen ◽  
Qiaoming Zhu ◽  
Juncheng Jia
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Event Detection ◽  
Dominating Set ◽  
Wireless Sensor ◽  
Rule Base ◽  
Detection Accuracy ◽  
Two Dimensional ◽  
Fuzzy Decision ◽  
Composite Event

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensionalτ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensionalτ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.

scholarly journals Effect of sampling frequency on fractal fluctuations during treadmill walking

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0218908 ◽  
Author(s):  
Vivien Marmelat ◽  
Austin Duncan ◽  
Shane Meltz
Keyword(s):  
Sampling Frequency ◽  
Treadmill Walking ◽  
Fractal Fluctuations

MITIGATION OF TIME SERIES APPROACH ON CLIMATE CHANGE ADAPTATION ON RAINFALL OF WADI AL-AQIQ, MADINAH, SAUDI ARABIA

2017 ◽  
Vol 79 (5) ◽  
Author(s):  
Norhan Abd Rahman ◽  
Zulkifli Yusop ◽  
Zekai Şen ◽  
Saud Taher ◽  
Ibrahim Lawal Kane
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Saudi Arabia ◽  
State Space ◽  
General Circulation ◽  
Temporal Dynamics ◽  
Resource Planning ◽  
General Circulation Models ◽  
Trend Test ◽  
Fluctuation Analysis

Rainfall record plays a significant role in assessment of climate change, water resource planning and management. In arid region, studies on rainfall are rather scarce due to intricacy and constraint of the available data. Most available studies use more advanced approaches such as A2 scenario, General Circulation Models (GCM) and the like, to study the temporal dynamics and make projection on future rainfall. However, those models take no account of the data patterns and its predictability. Therefore, this study uses time series analysis methodologies such as Mann- Kendall trend test, de-trended fluctuation analysis and state space time series approaches to study the dynamics of rainfall records of four stations in and around Wadi Al-Aqiq, Kingdom of Saudi Arabia (KSA). According to Mann-Kendall trend test there are decreasing trend in three out of the four stations. The de-trended fluctuation analysis revealed two distinct scaling properties that spells the predictability of the records and confirmed by state space methods. 

Numerical Study on the Improvement of Detection Accuracy for a Wireless Motion Capture System

2009 ◽  
Vol 45 (6) ◽  
pp. 2736-2739 ◽  
Author(s):  
S. Hashi ◽  
S. Yabukami ◽  
H. Kanetaka ◽  
K. Ishiyama ◽  
K.I. Arai
Keyword(s):  
Motion Capture ◽  
Numerical Study ◽  
Detection Accuracy ◽  
Motion Capture System

scholarly journals Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

2020 ◽  
Author(s):  
Robert Kanko ◽  
Gerda Strutzenberger ◽  
Marcus Brown ◽  
Scott Selbie ◽  
Kevin Deluzio
Keyword(s):  
Motion Capture ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Healthy Adults ◽  
Video Data ◽  
Heel Strike ◽  
Markerless Motion Capture ◽  
Gait Parameters

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and pressure sensitive gait mats. For the first study, thirty healthy adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heel-strike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems. Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters during treadmill and overground gait.

scholarly journals Accuracy and Precision of Three Consumer-Grade Motion Sensors During Overground and Treadmill Walking in People With Parkinson Disease: Cross-Sectional Comparative Study (Preprint)

2019 ◽  
Author(s):  
Byron Lai ◽  
Jeffer E Sasaki ◽  
Brenda Jeng ◽  
Katie L Cederberg ◽  
Marcas M Bamman ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Treadmill Walking ◽  
Motion Sensors ◽  
Cross Sectional ◽  
Absolute Relative Error ◽  
Intraclass Correlation Coefficients ◽  
Accuracy And Precision ◽  
Manual Counting

BACKGROUND Wearable motion sensors are gaining popularity for monitoring free-living physical activity among people with Parkinson disease (PD), but more evidence supporting the accuracy and precision of motion sensors for capturing step counts is required in people with PD. OBJECTIVE This study aimed to examine the accuracy and precision of 3 common consumer-grade motion sensors for measuring actual steps taken during prolonged periods of overground and treadmill walking in people with PD. METHODS A total of 31 ambulatory participants with PD underwent 6-min bouts of overground and treadmill walking at a comfortable speed. Participants wore 3 devices (Garmin Vivosmart 3, Fitbit One, and Fitbit Charge 2 HR), and a single researcher manually counted the actual steps taken. Accuracy and precision were based on absolute and relative metrics, including intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS Participants walked 628 steps over ground based on manual counting, and Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR devices had absolute (relative) error values of 6 (6/628, 1.0%), 8 (8/628, 1.3%), and 30 (30/628, 4.8%) steps, respectively. ICC values demonstrated excellent agreement between manually counted steps and steps counted by both Garmin Vivosmart (0.97) and Fitbit One (0.98) but poor agreement for Fitbit Charge 2 HR (0.47). The absolute (relative) precision values for Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR were 11.1 (11.1/625, 1.8%), 14.7 (14.7/620, 2.4%), and 74.4 (74.4/598, 12.4%) steps, respectively. ICC confidence intervals demonstrated low variability for Garmin Vivosmart (0.96 to 0.99) and Fitbit One (0.93 to 0.99) but high variability for Fitbit Charge 2 HR (–0.57 to 0.74). The Fitbit One device maintained high accuracy and precision values for treadmill walking, but both Garmin Vivosmart and Fitbit Charge 2 HR (the wrist-worn devices) had worse accuracy and precision for treadmill walking. CONCLUSIONS The waist-worn sensor (Fitbit One) was accurate and precise in measuring steps with overground and treadmill walking. The wrist-worn sensors were accurate and precise only during overground walking. Similar research should inform the application of these devices in clinical research and practice involving patients with PD.

scholarly journals A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability

2021 ◽  
Vol 11 ◽  
Author(s):  
Bruce Rogers ◽  
David Giles ◽  
Nick Draper ◽  
Olaf Hoos ◽  
Thomas Gronwald
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Gas Exchange ◽  
Ventilatory Threshold ◽  
Intraclass Correlation ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Low Intensity ◽  
Low Intensity Exercise ◽  
Correlation Properties

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of 0.75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 39.8 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.1 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson’s r values of 0.99 (p < 0.001) and.97 (p < 0.001) for VO2 and HR comparisons, respectively. Intraclass correlation between VT1 GAS and HRVT was 0.99 for VO2 and 0.96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of 0.75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.

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