scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

scholarly journals Detecting Controversial Articles on Citizen Journalism

2018 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Alfan Farizki Wicaksono ◽  
Sharon Raissa Herdiyana ◽  
Mirna Adriani
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Structural Features ◽  
The Body ◽  
Supervised Machine Learning ◽  
Citizen Journalism ◽  
Learning Approach ◽  
Daily News ◽  
Machine Learning Approach ◽  
Controversial Topic

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.

scholarly journals A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0178366 ◽  
Author(s):  
Ryan S. McGinnis ◽  
Nikhil Mahadevan ◽  
Yaejin Moon ◽  
Kirsten Seagers ◽  
Nirav Sheth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Gait Speed ◽  
Wearable Sensors ◽  
Learning Approach ◽  
Speed Estimation ◽  
Healthy Controls ◽  
Machine Learning Approach

scholarly journals Development of an Algorithm for Automatically Assessing Lifting Risk Factors Using Inertial Measurement Units

2019 ◽  
Vol 63 (1) ◽  
pp. 1334-1338 ◽  
Author(s):  
Ming-Lun Lu ◽  
Shuo Feng ◽  
Grant Hughes ◽  
Menekse S. Barim ◽  
Marie Hayden ◽  
...  
Keyword(s):  
Risk Factors ◽  
The Body ◽  
Measurement Unit ◽  
Trunk Flexion ◽  
Inertial Measurement ◽  
Motion Data ◽  
Low Back Disorders ◽  
Machine Learning Approach ◽  
Measurement Units ◽  
Forearm Length

The objective of this study was to develop an algorithm for automatically processing data collected with inertial measurement unit (IMU) wearable devices to measure lifting risk factors for low back disorders. Five IMU sensors attached to five body segments were used for developing the algorithm. The algorithm consists of two modules running in parallel for detecting the beginning and ending of a lifting event as well as the vertical height (V) of the object lifted by two hands and the horizontal (H) distance between the object and the body during the lift. The motion synchronization feature of wrists’ motion data were used to train the lifting detection module using a machine learning approach. This module achieved a training accuracy of 85%. In the second module, the forearm length and gyroscope data of four sensors are proposed for calculating trunk flexion angle, V and H during a lift.

A machine learning approach to estimate Minimum Toe Clearance using Inertial Measurement Units

2015 ◽  
Vol 48 (16) ◽  
pp. 4309-4316 ◽  
Author(s):  
Braveena K. Santhiranayagam ◽  
Daniel T.H. Lai ◽  
W.A. Sparrow ◽  
Rezaul K. Begg
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Machine Learning Approach ◽  
Measurement Units

scholarly journals A neural surveyor to map touch on the body

2022 ◽  
Vol 119 (1) ◽  
pp. e2102233118
Author(s):  
Luke E. Miller ◽  
Cécile Fabio ◽  
Malika Azaroual ◽  
Dollyane Muret ◽  
Robert J. van Beers ◽  
...  
Keyword(s):  
Somatosensory System ◽  
Body Part ◽  
Afferent Input ◽  
The Body ◽  
Positioning Systems ◽  
Tactile Localization ◽  
Global Positioning ◽  
Somatosensory Neurons ◽  
Somatotopic Map ◽  
Sensory Map

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

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