scholarly journals An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2432 ◽  
Author(s):  
Zhen Gang Xiao ◽  
Carlo Menon
Keyword(s):  
Upper Limb ◽  
Sampling Frequency ◽  
Isometric Actions ◽  
Monitoring Applications ◽  
Minimum Requirements ◽  
Data Content ◽  
Acquisition Device ◽  
Force Myography ◽  
Machine Interface ◽  
Insight Into

Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised.

scholarly journals A Review of Force Myography Research and Development

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4557 ◽  
Author(s):  
Zhen Gang Xiao ◽  
Carlo Menon
Keyword(s):  
Research And Development ◽  
Muscle Stiffness ◽  
Machine Learning Algorithms ◽  
Research Progress ◽  
Force Myography ◽  
Machine Interface ◽  
Art Research ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Insight Into

Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.

On the use of jerk and snap in condition monitoring of machinery – review and case studies

2021 ◽  
Vol 63 (8) ◽  
pp. 457-464
Author(s):  
S Lahdelma
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Early Stage ◽  
Time Derivative ◽  
Additional Insight ◽  
Rolling Bearings ◽  
Monitoring Applications ◽  
Derivatives Of ◽  
Insight Into ◽  
Peak Value

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cut-off frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

Use Cases for Assessing, Testing, and Validating the Human Machine Interface of Automated Driving Systems

2018 ◽  
Vol 62 (1) ◽  
pp. 1873-1877 ◽  
Author(s):  
Frederik Naujoks ◽  
Sebastian Hergeth ◽  
Katharina Wiedemann ◽  
Nadja Schömig ◽  
Andreas Keinath
Keyword(s):  
Traffic Safety ◽  
Test Methods ◽  
Use Cases ◽  
Highway Traffic ◽  
Automated Vehicles ◽  
Automated Driving ◽  
National Highway Traffic Safety ◽  
Minimum Requirements ◽  
Machine Interface ◽  
Increasing Demand

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.

scholarly journals A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot

2020 ◽  
Vol 14 ◽  
Author(s):  
Yonghao Song ◽  
Siqi Cai ◽  
Lie Yang ◽  
Guofeng Li ◽  
Weifeng Wu ◽  
...  
Keyword(s):  
Upper Limb ◽  
Online Control ◽  
Human Machine Interface ◽  
Machine Interface

INDIVIDUATION AND SEGMENTATION: INSIGHT INTO UPPER-LIMB MOTOR DYSFUNCTION

2007 ◽  
Vol 40 ◽  
pp. S393
Author(s):  
M.T. Wininger ◽  
N.-H. Kim ◽  
S. Escaldi ◽  
W. Craelius
Keyword(s):  
Upper Limb ◽  
Motor Dysfunction ◽  
Insight Into

scholarly journals Design of a Passive Upper Limb Exoskeleton for Macaque Monkeys

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Junkai Lu ◽  
Kevin Haninger ◽  
Wenjie Chen ◽  
Suraj Gowda ◽  
Masayoshi Tomizuka ◽  
...  
Keyword(s):  
Upper Limb ◽  
Neural Control ◽  
Time Data ◽  
Upper Limb Exoskeleton ◽  
Contact Points ◽  
Shoulder Complex ◽  
Machine Interface ◽  
Set Up ◽  
Six Degree Of Freedom ◽  
Exoskeleton Design

Integrating an exoskeleton as the external apparatus for a brain–machine interface (BMI) has the advantage of providing multiple contact points to determine body segment postures and allowing control to and feedback from each joint. When using macaques as subjects to study the neural control of movement, an upper limb exoskeleton design with unlikely singularity is required to guarantee safe and accurate tracking of joint angles over all possible range of motion (ROM). Additionally, the compactness of the design is of more importance considering macaques have significantly smaller body dimensions than humans. This paper proposes a six degree-of-freedom (DOF) passive upper limb exoskeleton with 4DOFs at the shoulder complex. System kinematic analysis is investigated in terms of its singularity and manipulability. A real-time data acquisition system is set up, and system kinematic calibration is conducted. The effectiveness of the proposed exoskeleton system is finally demonstrated by a pilot animal test in the scenario of a reach and grasp task.

scholarly journals A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2050 ◽  
Author(s):  
Zhichuan Tang ◽  
Shouqian Sun ◽  
Sanyuan Zhang ◽  
Yumiao Chen ◽  
Chao Li ◽  
...  
Keyword(s):  
Upper Limb ◽  
Brain Machine Interface ◽  
Upper Limb Exoskeleton ◽  
Machine Interface
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