scholarly journals Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

2007 ◽  
Vol 44 (4) ◽  
pp. 599 ◽  
Author(s):  
Paul S. Sung ◽  
Ulrich Zurcher ◽  
Miron Kaufman
2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
John M. Vasudevan ◽  
Andrew Logan ◽  
Rebecca Shultz ◽  
Jeffrey J. Koval ◽  
Eugene Y. Roh ◽  
...  

Aim. The purpose of this pilot study is to use surface electromyography to determine an individual athlete’s typical muscle onset activation sequence when performing a golf or tennis forward swing and to use the method to assess to what degree the sequence is reproduced with common conditioning exercises and a machine designed for this purpose.Methods. Data for 18 healthy male subjects were collected for 15 muscles of the trunk and lower extremities. Data were filtered and processed to determine the average onset of muscle activation for each motion. A Spearman correlation estimated congruence of activation order between the swing and each exercise. Correlations of each group were pooled with 95% confidence intervals using a random effects meta-analytic strategy.Results. The averaged sequences differed among each athlete tested, but pooled correlations demonstrated a positive association between each exercise and the participants’ natural muscle onset activation sequence.Conclusion. The selected training exercises and Turning Point™device all partially reproduced our athletes’ averaged muscle onset activation sequences for both sports. The results support consideration of a larger, adequately powered study using this method to quantify to what degree each of the selected exercises is appropriate for use in both golf and tennis.


2010 ◽  
Vol 26 (5) ◽  
pp. 353-356 ◽  
Author(s):  
Kristina M. Price ◽  
Nicholas A. Ramey ◽  
Michael J. Richard ◽  
Donald J. Woodward ◽  
Julie A. Woodward

Author(s):  
Vishnu Unnikrishnan ◽  
Yash Shah ◽  
Miro Schleicher ◽  
Mirela Strandzheva ◽  
Plamen Dimitrov ◽  
...  

Abstract Some mHealth apps record user activity continuously and unobtrusively, while other apps rely by nature on user engagement and self-discipline: users are asked to enter data that cannot be assessed otherwise, e.g., on how they feel and what non-measurable symptoms they have. Over time, this leads to substantial differences in the length of the time series of recordings for the different users. In this study, we propose two algorithms for wellbeing-prediction from such time series, and we compare their performance on the users of a pilot study on diabetic patients - with time series length varying between 8 and 87 recordings. Our first approach learns a model from the few users, on which many recordings are available, and applies this model to predict the 2nd, 3rd, and so forth recording of users newly joining the mHealth platform. Our second approach rather exploits the similarity among the first few recordings of newly arriving users. Our results for the first approach indicate that the target variable for users who use the app for long are not predictive for users who use the app only for a short time. Our results for the second approach indicate that few initial recordings suffice to inform the predictive model and improve performance considerably.


2020 ◽  
Vol 82 ◽  
pp. 129-137
Author(s):  
John J. Drewry ◽  
Heather North ◽  
Stella E. Belliss ◽  
Alexander Amies

Winter grazing of forage crops is a key land-use in southern New Zealand, providing important feed for livestock but has been identified as risky if not managed well, potentially resulting in soil degradation and nutrient losses. We hypothesised that analysing an existing time series of winter-forage maps, derived from satellite imagery could be used to identify how often paddocks are re-used for winter forage. A pilot study was undertaken to explore the practicality and utility of this new method by examining maps derived from satellite images of the Gore-Mataura area, Southland taken in 2013, 2014, 2017, and 2018. Within the study site (67,618 ha), 8925 ha was classed as winter forage in one or more of the source maps. Eighty-five percent of this area was used in only one of the four years, and just 1% in three or four years. High-certainty class pairs for 2013/14 and 2017/18 show two consecutive years of winter forage in the same paddock, 31% or 21% of the time, respectively. These winter-forage crops were generally grown on Brown soils (63%), followed by Pallic and Gley soils. Although, this study was limited by differences in the mapping methodologies of the source maps, it nonetheless  demonstrated that potentially valuable data can be derived. It showed a low level of repeat use of paddocks for winter forage grazing over all the years studied, and that Brown soils are more commonly used for winter forage than previous studies suggested.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Andrea Chiavenna ◽  
Alessandro Scano ◽  
Matteo Malosio ◽  
Lorenzo Molinari Tosatti ◽  
Franco Molteni

Exoskeleton devices for upper limb neurorehabilitation are one of the most exploited solutions for the recovery of lost motor functions. By providing weight support, passively compensated exoskeletons allow patients to experience upper limb training. Transparency is a desirable feature of exoskeletons that describes how the device alters free movements or interferes with spontaneous muscle patterns. A pilot study on healthy subjects was conducted to evaluate the feasibility of assessing transparency in the framework of muscle synergies. For such purpose, the LIGHTarm exoskeleton prototype was used. LIGHTarm provides gravity support to the upper limb during the execution of movements in the tridimensional workspace. Surface electromyography was acquired during the execution of three daily life movements (reaching, hand-to-mouth, and hand-to-nape) in three different conditions: free movement, exoskeleton-assisted (without gravity compensation), and exoskeleton-assisted (with gravity compensation) on healthy people. Preliminary results suggest that the muscle synergy framework may provide valuable assessment of user transparency and weight support features of devices aimed at rehabilitation.


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