ArticleBiomedical engineering online2018
Regressing grasping using force myography: an exploratory study.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- A survey on the state of the art of force myography technique (FMG): analysis and assessment.Medical & biological engineering & computing · 2024Review
- Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform.Scientific reports · 2024Article
- A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.Biosensors · 2022Review
- Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.Sensors (Basel, Switzerland) · 2021Article
- Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network.Frontiers in bioengineering and biotechnology · 2021Article
- Wrist-worn wearables based on force myography: on the significance of user anthropometry.Biomedical engineering online · 2020Article
- Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation.Sensors (Basel, Switzerland) · 2020Article
- Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications.Sensors (Basel, Switzerland) · 2019Article
- A Review of Force Myography Research and Development.Sensors (Basel, Switzerland) · 2019Review
- An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals.Sensors (Basel, Switzerland) · 2019Article
- Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study.Frontiers in bioengineering and biotechnology · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundPartial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee's life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously.
methodsTen able-bodied participants performed three hand movements while their wrist was fixed in one of six predefined positions. The angle between Thumb and Index finger ([Formula: see text]), and Thumb and Middle finger ([Formula: see text]) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated.
resultsThe first approach had a correlation of determination ([Formula: see text]) of 0.871 for [Formula: see text] and [Formula: see text]. Using the second approach [Formula: see text] and [Formula: see text] were obtained. The first approach is over two times faster than the second approach while having similar performance; thus the first approach was selected to investigate the effect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically significant. A statistically significant decrease in performance resulted when less than five wrist positions were used for training.
conclusionsThe results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase.
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