Modeling Walking with an Inverted Pendulum Not Constrained to the Sagittal Plane. Numerical Simulations and Asymptotic Expansions

Goldsztein, Guillermo H.

doi:10.4236/am.2017.81006

Cited by 3 publications

(1 citation statement)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All of the datasets were acquired during 26 cycling experiments in a hilly area on a route that is 12 km long, with an altitude difference of 300 m. Records were subsequently stored to the Garmin Connect website, exported as TCX files, converted to CSV files, and then imported to the MATLAB software for further processing. Accelerometric data were recorded by the mobile phone in the spine position, which was selected following previous studies [25], [41], which described the higher discriminative abilities of sensors located in the upper half of the body [42], [43] in comparison to other positions. The sampling frequency of the Android mobile phone sensor was 100 Hz during all cycling routes.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

et al. 2021

View full text Add to dashboard Cite

Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well.INDEX TERMS Multimodal signal analysis, computational intelligence, machine learning, motion monitoring, accelerometer-derived cycling data, classification.

show abstract

Section: A Data Acquisitionmentioning

confidence: 99%

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

et al. 2021

View full text Add to dashboard Cite

show abstract

Recognition of motion patterns using accelerometers for ataxic gait assessment

Dostál

Procházka

Vyšata

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

Controlling Two-Legged Mobile Robot

Hong

2021

IJSRST

View full text Add to dashboard Cite

Since the appearance of robots, they have brought many benefits, for example: they can work continuously; they can work in harsh and dangerous environments that cannot be accessed by humans. Thanks to their mobility, mobile robots have a wide and flexible working area, especially two-legged mobile robots that can move in bumpy terrains, go up and down stairs or step over obstacles easily. Nowadays, with the increasing development of science, more and more mobile robots are applied and participated in human activities not only in service activities but also in direct coordination with humans. Robot control methods usually come from robot dynamic model and robot motion differential equation, thereby, calculating driving forces based on the deviation of input and output signals to drive motors on joints in order to ensure that robots moves in the desired trajectory. Two-legged mobile robots have a structure of many phases and joints connected together, besides, due to a large number of degrees of freedom, this type of robot is able to operate flexibly and move easily, however, it has a difficulty in dynamic and kinematic modeling, and robot control. Normally, the differential equation of robot motion will have complex quantities and massive formulas. In order to improve the walk of this robot, this study focuses on researching and surveying the problem of kinetics and dynamics and using a control method to control a specific two-legged mobile robot that moves in a cycle of walking.

show abstract

Modeling Walking with an Inverted Pendulum Not Constrained to the Sagittal Plane. Numerical Simulations and Asymptotic Expansions

Cited by 3 publications

References 49 publications

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Recognition of motion patterns using accelerometers for ataxic gait assessment

Controlling Two-Legged Mobile Robot

Contact Info

Product

Resources

About