Transformation Assay:Article Title: An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals
Article Snippet: .. Fifty-one predictor features were extracted (see Table and Fig. for full list). (1) The HR and Acc magnitude. (2) Time–frequency decomposition using the wavelet packet transform was used to analyse HR and Acc over different time scales (MATLAB 2022a routine ‘ modwpt’ , Daubechies wavelet with two vanishing moments), yielding sixteen features (frequency bands) for each measure (WPT-HR and WPT-Acc). (3) The time-evolving spectral cross-dependence between HR and Acc was estimated using multivariate locally stationary wavelet processes, with the localised coherence Fisher’s-z transformed (R package ‘ mvLSW ’ version 1.2.5) , yielding 12 features (LSW-HR-Acc). (4) HRV measures from the recording session were included, specifically the standard deviation of inter-beat intervals (SDRR) – the time deviation between consecutive R-peaks. (5) A binary time series derived from the CPD step was included, indicating potential attention and inattention states (Change point binary). (6) Segment duration (Duration) and time to the previous segment (Latency) yielded two more features. (7) Lastly, age in months was included. ..
Article Title: Energy features fusion based hydraulic cylinder seal wear and internal leakage fault diagnosis method
Article Snippet: Internal leakage is one of the most common faults in hydraulic cylinders, and seal wear is the main factor in internal leakage.. However, it is difficult to detect seal wear and internal leakage in hydraulic cylinder using present approaches due to the complex hydraulic system.. Therefore, an intelligent fault diagnosis method based energy features fusion is proposed to detect seal wear and internal leakage.
Article Title: An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals.
Article Snippet: .. Fifty-one predictor features were extracted (see Table S3 and Fig. 6 for full list). (1) The HR and Acc magnitude. (2) Time–frequency decomposition using the wavelet packet transform was used to analyse HR and Acc over different time scales (MATLAB 2022a routine ‘modwpt’, Daubechies wavelet with two vanishing moments), yielding sixteen features (frequency bands) for each measure (WPT-HR and WPT-Acc). (3) The time-evolving spectral cross-dependence between HR and Acc was estimated using multivariate locally stationary wavelet processes, with the localised coherence Fisher’s-z transformed (R package ‘mvLSW’ version 1.2.5)91, yielding 12 features (LSW-HR-Acc). (4) HRV measures from the recording session were included, specifically the standard deviation of inter-beat intervals (SDRR) – the time deviation between consecutive R-peaks. (5) A binary time series derived from the CPD step was included, indicating potential attention and inattention states (Change point binary). (6) Segment duration (Duration) and time to the previous segment (Latency) yielded two more features. (7) Lastly, age in months was included. ..
Standard Deviation:Article Title: An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals
Article Snippet: .. Fifty-one predictor features were extracted (see Table and Fig. for full list). (1) The HR and Acc magnitude. (2) Time–frequency decomposition using the wavelet packet transform was used to analyse HR and Acc over different time scales (MATLAB 2022a routine ‘ modwpt’ , Daubechies wavelet with two vanishing moments), yielding sixteen features (frequency bands) for each measure (WPT-HR and WPT-Acc). (3) The time-evolving spectral cross-dependence between HR and Acc was estimated using multivariate locally stationary wavelet processes, with the localised coherence Fisher’s-z transformed (R package ‘ mvLSW ’ version 1.2.5) , yielding 12 features (LSW-HR-Acc). (4) HRV measures from the recording session were included, specifically the standard deviation of inter-beat intervals (SDRR) – the time deviation between consecutive R-peaks. (5) A binary time series derived from the CPD step was included, indicating potential attention and inattention states (Change point binary). (6) Segment duration (Duration) and time to the previous segment (Latency) yielded two more features. (7) Lastly, age in months was included. ..
Article Title: An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals.
Article Snippet: .. Fifty-one predictor features were extracted (see Table S3 and Fig. 6 for full list). (1) The HR and Acc magnitude. (2) Time–frequency decomposition using the wavelet packet transform was used to analyse HR and Acc over different time scales (MATLAB 2022a routine ‘modwpt’, Daubechies wavelet with two vanishing moments), yielding sixteen features (frequency bands) for each measure (WPT-HR and WPT-Acc). (3) The time-evolving spectral cross-dependence between HR and Acc was estimated using multivariate locally stationary wavelet processes, with the localised coherence Fisher’s-z transformed (R package ‘mvLSW’ version 1.2.5)91, yielding 12 features (LSW-HR-Acc). (4) HRV measures from the recording session were included, specifically the standard deviation of inter-beat intervals (SDRR) – the time deviation between consecutive R-peaks. (5) A binary time series derived from the CPD step was included, indicating potential attention and inattention states (Change point binary). (6) Segment duration (Duration) and time to the previous segment (Latency) yielded two more features. (7) Lastly, age in months was included. ..
Derivative Assay:Article Title: An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals
Article Snippet: .. Fifty-one predictor features were extracted (see Table and Fig. for full list). (1) The HR and Acc magnitude. (2) Time–frequency decomposition using the wavelet packet transform was used to analyse HR and Acc over different time scales (MATLAB 2022a routine ‘ modwpt’ , Daubechies wavelet with two vanishing moments), yielding sixteen features (frequency bands) for each measure (WPT-HR and WPT-Acc). (3) The time-evolving spectral cross-dependence between HR and Acc was estimated using multivariate locally stationary wavelet processes, with the localised coherence Fisher’s-z transformed (R package ‘ mvLSW ’ version 1.2.5) , yielding 12 features (LSW-HR-Acc). (4) HRV measures from the recording session were included, specifically the standard deviation of inter-beat intervals (SDRR) – the time deviation between consecutive R-peaks. (5) A binary time series derived from the CPD step was included, indicating potential attention and inattention states (Change point binary). (6) Segment duration (Duration) and time to the previous segment (Latency) yielded two more features. (7) Lastly, age in months was included. ..
Article Title: An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals.
Article Snippet: .. Fifty-one predictor features were extracted (see Table S3 and Fig. 6 for full list). (1) The HR and Acc magnitude. (2) Time–frequency decomposition using the wavelet packet transform was used to analyse HR and Acc over different time scales (MATLAB 2022a routine ‘modwpt’, Daubechies wavelet with two vanishing moments), yielding sixteen features (frequency bands) for each measure (WPT-HR and WPT-Acc). (3) The time-evolving spectral cross-dependence between HR and Acc was estimated using multivariate locally stationary wavelet processes, with the localised coherence Fisher’s-z transformed (R package ‘mvLSW’ version 1.2.5)91, yielding 12 features (LSW-HR-Acc). (4) HRV measures from the recording session were included, specifically the standard deviation of inter-beat intervals (SDRR) – the time deviation between consecutive R-peaks. (5) A binary time series derived from the CPD step was included, indicating potential attention and inattention states (Change point binary). (6) Segment duration (Duration) and time to the previous segment (Latency) yielded two more features. (7) Lastly, age in months was included. ..
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