Teager-Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio

A temporal analysis of electromyographic (EMG) activity has widely been used for non-invasive study of muscle activation patterns. Such an analysis requires robust methods to accurately detect EMG onset. We examined whether data conditioning, supplemented with Teager–Kaiser Energy Operator (TKEO), would improve accuracy of the EMG burst onset detection. EMG signals from vastus lateralis, collected during maximal voluntary contractions, performed by seventeen subjects (8 males, 9 females, mean age of 46 yrs), were analyzed. The error of onset detection using enhanced signal conditioning was significantly lower than that of onset detection performed on signals conditioned without the TKEO (40 ±99 ms vs. 229 ±356 ms, t-test, p = 0.023). The Pearson correlations revealed that neither accuracy after enhanced conditioning nor accuracy after standard conditioning was significantly related to signal-to-noise ratio (SNR) (r = −0.05, p = 0.8 and r = −0.19, p = 0.46, respectively). It is concluded that conditioning of the EMG signals with TKEO significantly improved the accuracy of the threshold-based onset detection methods, regardless of SNR magnitude. Originally published Acta Bioeng Biomech, Vol. 10, No. 2, 200

BusTr: Predicting Bus Travel Times from Real-Time Traffic

We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. We demonstrate that our neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. We also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.Comment: 14 pages, 2 figures, 5 tables. Citation: "Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu (2020). BusTr: Predicting Bus Travel Times from Real-Time Traffic. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. doi: 10.1145/3394486.3403376

Teager-Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio

A temporal analysis of electromyographic (EMG) activity has widely been used for non-invasive study of muscle activation patterns. Such an analysis requires robust methods to accurately detect EMG onset. We examined whether data conditioning supplemented with Teager–Kaiser Energy Operator (TKEO) would improve accuracy of the EMG burst onset detection. EMG signals from vastus lateralis collected during maximal voluntary contractions performed by seventeen subjects (8 males 9 females mean age of 46 yrs) were analyzed. The error of onset detection using enhanced signal conditioning was significantly lower than that of onset detection performed on signals onditioned without the TKEO (40 ±99 ms vs. 229 ±356 ms t-test p = 0.023). The Pearson correlations revealed that neither accuracy after enhanced conditioning nor accuracy after standard conditioning was significantly related to signal-to-noise ratio (SNR) (r = −0.05 p = 0.8 and r = −0.19 p = 0.46 respectively). It is concluded that conditioning of the EMG signals with TKEO significantly improved the accuracy of the threshold-based onset detection methods regardless of SNR magnitude. Originally published Acta Bioeng Biomech Vol. 10 No. 2 200