Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: reach

Sortuj według:

Ogranicz wyniki do:

Reach of an inclined cantilever with a tip load

Singh P., Goss V. G. A.

Archives of Mechanics

2019

Vol. 71, nr 6

595--614

We investigate the problem of determining the reach of an inclined cantilever for a given point load suspended from its tip. Two situations are considered. Firstly, we find the maximum reach of the cantilever by varying its angle of inclination. Secondly, we find the reach of the cantilever subject to the condition that its tip is at some specified height, above or below, the level of the clamped end. In the second case, the reach of the cantilever is maximised by shortening its physical length whilst keeping the physical load and physical height of load deployment constant. All of our solutions representing various reaches of an inclined cantilever for a given point load suspended from its tip are shown to be stable to the snap-back instability.

Application of the Teager Kaiser Energy Operator in an autonomous burst detector to create onset and offset profiles of forearm muscles during reach-to-grasp movements

Krabben T., Prange G. B., Kobus H. J., Rietman J. S., Buurke J. H.

Acta of Bioengineering and Biomechanics

2016

Vol. 18, no. 4

135--144

Purpose: The primary aim of this study is to investigate the potential benefit of the Teager–Kaiser Energy Operator (TKEO) as data pre-processor, in an autonomous burst detection method to classify electromyographic signals of the (fore)arm and hand. For this purpose, optimal settings of the burst detector, leading to minimal detection errors, need to be known. Additionally, the burst detector is applied to real muscle activity recorded in healthy adults performing reach-to-grasp movements. Methods: The burst detector was based on the Approximated Generalized Likelihood Ratio (AGLR). Simulations with synthesized electromyographic (EMG) traces with known onset and offset times, yielded optimal settings for AGLR parameters “window width” and “threshold value” that minimized detection errors. Next, comparative simulations were done with and without TKEO data pre-processing. Correct working of the burst detector was verified by applying it to real surface EMG signals obtained from arm and hand muscles involved in a submaximal reach-to-grasp task, performed by healthy adults. Results: Minimal detection errors were found with a window width of 100 ms and a detection threshold of 15. Inclusion of the TKEO contributed significantly to a reduction of detection errors. Application of the autonomous burst detector to real data was feasible. Conclusions: The burst detector was able to classify muscle activation and create Muscle Onset Offset Profiles (MOOPs) autonomously from real EMG data, which allows objective comparison of MOOPs obtained from movement tasks performed in different conditions or from different populations. The TKEO contributed to improved performance and robustness of the burst detector.