Multi-sensor arm rehabilitation monitoring device

Ambar, Radzi; Poad, Hazwaj Mhd; Ali, Abdul Malik Bin Mohd; Ahmad, M. Z.; Jamil, M. Mahadi Abdul

doi:10.1109/icobe.2012.6179051

Cited by 18 publications

(9 citation statements)

References 9 publications

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“…FMG signals to detect hand movements are generally extracted from the middle of the forearm where large radial displacements can be recorded [ 22 , 24 , 25 , 31 , 34 , 39 - 42 ]. Recent studies have, however, explored the possibility of estimating hand movements by processing FMG signals recorded at the wrist.…”

Section: Introductionmentioning

confidence: 99%

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Xiao

Menon

2017

JMIR Rehabil Assist Technol

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BackgroundFunctional arm movements generally require grasping an object. The possibility of detecting and counting the action of grasping is believed to be of importance for individual with motor function deficits of the arm, as it could be an indication of the number of the functional arm movements performed by the individuals during rehabilitation. In this exploratory work, the feasibility of using armbands recording radial displacements of forearm muscles and tendons (ie, force myography, FMG) to estimate hand grasping with healthy individuals was investigated. In contrast to previous studies, this exploratory study investigates the feasibility of (1) detecting grasping when the participants move their arms, which could introduce large artifacts to the point of potentially preventing the practical use of the proposed technology, and (2) counting grasping during arm-reaching tasks.ObjectiveThe aim of this study was to determine the usefulness of FMG in the detection of functional arm movements. The use of FMG straps placed on the forearm is proposed for counting the number of grasping actions in the presence of arm movements.MethodsTen healthy volunteers participated in this study to perform a pick-and-place exercise after providing informed consent. FMG signals were simultaneously collected using 2 FMG straps worn on their wrist and at the midposition of their forearm, respectively. Raw FMG signals and 3 additional FMG features (ie, root mean square, wavelength, and window symmetry) were extracted and fed into a linear discriminant analysis classifier to predict grasping states. The transition from nongrasping to grasping states was detected during the process of counting the number of grasping actions.ResultsThe median accuracy for detecting grasping events using FMG recorded from the wrist was 95%, and the corresponding interquartile range (IQR) was 5%. For forearm FMG classification, the median accuracy was 92%, and the corresponding IQR was 3%. The difference between the 2 median accuracies was statistically significant (P<.001) when using a paired 2-tailed sign test. The median percentage error for counting grasping events when FMG was recorded from the wrist was 1%, and the corresponding IQR was 2%. The median percentage error for FMG recorded from the forearm was 2%, and the corresponding IQR was also 2%. While the median percentage error for the wrist was lower than that of the forearm, the difference between the 2 was not statistically significant based on a paired 2-tailed sign test (P=.29).ConclusionsThis study reports that grasping can reliably be counted using an unobtrusive and simple FMG strap even in the presence of arm movements. Such a result supports the foundation for future research evaluating the feasibility of monitoring hand grasping during unsupervised ADL, leading to further investigations with individuals with motor function deficits of the arm.

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Section: Introductionmentioning

confidence: 99%

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Xiao

Menon

2017

JMIR Rehabil Assist Technol

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“…Every sensor was connected to the Arduino board so that it recognized the voltage in the analog signal. The analog signal was then forwarded to the 10 bit analog-to-digital converter (ADC) for data processing (Bin Ambar et al, 2012).…”

Section: Presenting the Emboici Robotmentioning

confidence: 99%

“…The software written for the Arduino is called "sketches" and is saved with the file extension .ino (Arduino, 2012). The ATmega 328 chip assembled in the Arduino handles the program and interacts with the peripherals (Bin Ambar et al, 2012).…”

Section: Presenting the Emboici Robotmentioning

confidence: 99%

A robot for verifying the precision of total reaction time measurement

Crocetta

Kroich

Thiesen

et al. 2015

Motriz: rev. educ. fis.

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The level of variability in psychomotor behavior and the use of several distinct sets of equipments in Reaction Time (RT) assessments might jeopardize the validity and reliability of such measures. This study presents the development and verification of Emboici Robot-a robot capable of performing accurate RT assessments consisting of response to a visual stimulus by pressing a button-whose purpose is to measure the accuracy of RT assessments. We evaluated the accuracy and precision on four different days, each providing 300 measurements. These assessments generated a RT of 46.95ms (+6.04). No significant effects were found in the RTs obtained and, as a result, there is evidence that the Emboici Robotis stable, reliable, and precise. The robot can be a viable solution for verifying precision and accuracy of any given software with simple RT assessments with visual stimulus requiring as response the pressing of a button or key.

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“…Their main application has been the sensing of flex/extension and abdu/adduction movements of the hand's fingers [ 2 ]. However, they have been also used to measure rotations of other human joints, such as the elbow [ 3 ] and ankle [ 4 ], to reveal the kinematics of the trunk [ 5 ] and shoulder [ 6 ], or to furnish an aid in gait analysis [ 7 ] and orthotic appliances [ 8 ]. This is because these sensors can be used to sense bending angles both with fixed and/or changing curvature radius too [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

In Vitro Analysis of Pyrogenicity and Cytotoxicity Profiles of Flex Sensors to be Used to Sense Human Joint Postures

Saggio

Bianchi

Castelli

et al. 2014

Sensors

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Flex sensors can be usefully adopted as mechanical-electrical transducers to measure human joint movements, since their electrical resistance varies proportionally to the angle assumed by the joint under measure. Over time, these sensors have been investigated in terms of mechanical and electrical behavior, but no reports have detailed the possibility of their adoption not just on top but under the human skin of the joint. To this aim, our work investigated in vitro the pyrogenic potential and cytotoxicity of some commercially available flex sensors as a first step toward the necessary requirements regarding their biocompatibility, to predict possible foreign body reactions when used in vivo. Results demonstrated that some specific flex sensors satisfy such requirements.

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Multi-sensor arm rehabilitation monitoring device

Cited by 18 publications

References 9 publications

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

A robot for verifying the precision of total reaction time measurement

In Vitro Analysis of Pyrogenicity and Cytotoxicity Profiles of Flex Sensors to be Used to Sense Human Joint Postures

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