Speed Estimation of Walking and Running Using a Wearable Accelerometer Device

2015 ◽  
Vol 3 (0) ◽  
pp. 14
Author(s):  
Jiateng Hou ◽  
Yingfei Sun ◽  
Shengyun Yao ◽  
Zhipei Huang ◽  
Jiankang Wu
Keyword(s):  
Speed Estimation

Position and Speed Estimation Methods of SPMSMs using a Hall Effect Sensor

2008 ◽  
Vol 128 (2) ◽  
pp. 125-130
Author(s):  
Kan Akatsu ◽  
Nobuhiro Mitomo ◽  
Shinji Wakui
Keyword(s):  
Hall Effect ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Hall Effect Sensor

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.

Toward the development of a hybrid approach to speed estimation in urban and rural areas

2021 ◽  
pp. 1-6
Author(s):  
Mauro D’Apuzzo ◽  
Daniela Santilli ◽  
Azzurra Evangelisti ◽  
Gaetano Fusco ◽  
Vittorio Nicolosi ◽  
...  
Keyword(s):  
Rural Areas ◽  
Hybrid Approach ◽  
Speed Estimation ◽  
Urban And Rural Areas
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