A Case Study of a Force-myography Controlled Bionic Hand Mitigating Limb Position Effect

Ferigo, Diego; Merhi, Lukas-Karim; Pousett, Brittany; Xiao, Zhen Gang; Menon, Carlo

doi:10.1016/s1672-6529(16)60435-3

Cited by 41 publications

(34 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies by Cho et al and Ferigo et al used the same 6 grips in a static protocol on four subjects and one subject with transradial amputations and reported classification accuracies of 62.61 ± 11.5% and 81.2 ± 11.3%, respectively. Ferigo et al also used a dynamic protocol similar to the one used in dataset2 and dataset3 of this study and reported accuracy of 75.5 ± 9.2% (Cho et al, 2016;Ferigo et al, 2017). Other studies using EMG with subjects with amputation have reported classification accuracies in the range of 85-90% for 4-6 classes of movement (Peerdeman et al, 2011).…”

Section: Discussionmentioning

confidence: 53%

“…In this study, Jiang et al reported classification accuracies of as high as 83.5% for 48 static hand gestures using 8 FSRs with 12 healthy participants. Ferigo et al reported classification accuracies of 81.2 and 72.8% for 6 and 11 static gestures, respectively, in a case study with one participant with transradial amputation (Ferigo et al, 2017). Cho et al reported 62.61 and 41.73% classification accuracies for 6 and 11 static hand gestures, respectively, by placing eight FSRs on the residual limbs of 4 subjects with transradial amputations (Cho et al, 2016).…”

Section: Application Backgroundmentioning

confidence: 99%

“…The protocol used in this study consisted of six grips: relax, open, force, tripod, finger point, and key as shown in Figure 3. This set of grips was chosen to include neutral hand gestures and also functional movements important in activities of daily living (ADL) (Cho et al, 2016;Ahmadizadeh et al, 2017;Ferigo et al, 2017).…”

Section: Protocolmentioning

confidence: 99%

“…For dataset2 and dataset3, subject held the grips as he was moving his arm in the circular dynamic motion shown in Figure 4. FSR data were collected at sampling rate of 10 Hz (Ferigo et al, 2017;Jiang et al, 2017).…”

Section: Protocolmentioning

confidence: 99%

See 3 more Smart Citations

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Ahmadizadeh

Pousett²,

Menon

2019

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

Background: Various human machine interfaces (HMIs) are used to control prostheses, such as robotic hands. One of the promising HMIs is Force Myography (FMG). Previous research has shown the potential for the use of high density FMG (HD-FMG) that can lead to higher accuracy of prosthesis control. Motivation: The more sensors used in an FMG controlled system, the more complicated and costlier the system becomes. This study proposes a design method that can produce powered prostheses with performance comparable to that of HD-FMG controlled systems using a fewer number of sensors. An HD-FMG apparatus would be used to collect information from the user only in the design phase. Channel selection would then be applied to the collected data to determine the number and location of sensors that are vital to performance of the device. This study assessed the use of multiple channel selection (CS) methods for this purpose. Methods: In this case study, three datasets were used. These datasets were collected from force sensitive resistors embedded in the inner socket of a subject with transradial amputation. Sensor data were collected as the subject carried out five repetitions of six gestures. Collected data were then used to asses five CS methods: Sequential forward selection (SFS) with two different stopping criteria, minimum redundancy-maximum relevance (mRMR), genetic algorithm (GA), and Boruta. Results: Three out of the five methods (mRMR, GA, and Boruta) were able to decrease channel numbers significantly while maintaining classification accuracy in all datasets. Neither of them outperformed the other two in all datasets. However, GA resulted in the smallest channel subset in all three of the datasets. The three selected methods were also compared in terms of stability [i.e., consistency of the channel subset chosen by the method as new training data were introduced or some training data were removed (Chandrashekar and Sahin, 2014)]. Boruta and mRMR resulted in less instability compared to GA when applied to the datasets of this study. Conclusion: This study shows feasibility of using the proposed design method that can produce prosthetic systems that are simpler than HD-FMG systems but have performance comparable to theirs.

show abstract

Section: Discussionmentioning

confidence: 53%

Section: Application Backgroundmentioning

confidence: 99%

Section: Protocolmentioning

confidence: 99%

Section: Protocolmentioning

confidence: 99%

See 2 more Smart Citations

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Ahmadizadeh

Pousett²,

Menon

2019

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The pressure profile at the interface of the prosthetic socket and the residual limb contains important information that can be used for various applications in the field of prostheses. Some of the most common prosthetic applications for which the use of this pressure map has been explored include control of powered prostheses using Force Myography (FMG) [1][2][3] and prosthetic fitting [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

Ahmadizadeh

Menon

2019

Sensors

Self Cite

View full text Add to dashboard Cite

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors' responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors' responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications.

show abstract

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

show abstract

A Case Study of a Force-myography Controlled Bionic Hand Mitigating Limb Position Effect

Cited by 41 publications

References 39 publications

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Contact Info

Product

Resources

About