Pilot study on strategies in sensor placement for robust hand/wrist gesture classification based on movement related changes in forearm volume

Chengani, Ra'na; Delva, Mona L.; Sakr, Maram; Menon, Carlo

doi:10.1109/hic.2016.7797693

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…FMG for prosthetic control has been explored for both upper extremity and lower extremity prostheses [7,8] . For the upper limb, FMG has been mostly used for gesture classification to control externally-powered prosthetic hands [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…For the upper limb, FMG has been mostly used for gesture classification to control externally-powered prosthetic hands [9,10]. The feasibility of using FMG for continuous finger movement control has also been investigated [8,11]. In lower limb prostheses, the use of FMG has been mainly focused on locomotion mode detection.…”

Section: Introductionmentioning

confidence: 99%

“…However, increasing spatial coverage beyond that did not further improve the results. They also reported that optimal placement of sensors can potentially compensate for the lower spatial coverage [8]. Delva et al investigated the effect of anthropometry and grip strength on stability of FMG signals and determined that these factors do not contribute to variability in FMG signals.…”

Section: Introductionmentioning

confidence: 99%

“…The three regression algorithms that were assessed for this study are amongst the most vastly used for regression purposes for FSR signals used in biomedical applications [8,[35][36][37]. Linear machine learning algorithms are commonly used for various types of signals for a multitude of applications due to their capability of prediction and their computational efficiency [9,35,38,39].…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

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“…Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation [3]. In recent years, interest has grown in robotic-assisted rehabilitation, which aims to restore mobility in stroke population [4][5][6][7][8][9][10][11][12][13][14][15]. However, these efforts were developed from a technology-centered perspective, where engineers develop systems needed to provide upper extremity rehabilitation or assistance regardless of users' viewpoints [16].…”

Section: Introductionmentioning

confidence: 99%

Perceptions of Existing Wearable Robotic Devices for Upper Extremity and Suggestions for Their Development: Findings From Therapists and People With Stroke (Preprint)

Elnady¹,

Mortenson²,

Menon³

2017

Preprint

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Background: Advances in wearable robotic technologies have increased the potential of these devices for rehabilitation and as assistive devices. However, the utilization of these devices is still limited. There are also questions regarding how well these devices address users' (therapists and patients) needs. Objective: this study was conducted with three main objectives:1. To describe users' perceptions about existing wearable robotic devices for upper extremity. 2. To identify if there is a need to develop new devices for upper extremity and the desired features. 3. To explore the obstacles that would influence the utilization of these new devices (if needed). Methods: Focus groups were the primary means of data collection. Data were analyzed thematically.

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A survey on the state of the art of force myography technique (FMG): analysis and assessment

Sherif,

Bassuoni,

Mehrez

2024

Med Biol Eng Comput

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Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique. Graphical abstract

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Pilot study on strategies in sensor placement for robust hand/wrist gesture classification based on movement related changes in forearm volume

Cited by 11 publications

References 10 publications

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

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

Perceptions of Existing Wearable Robotic Devices for Upper Extremity and Suggestions for Their Development: Findings From Therapists and People With Stroke (Preprint)

A survey on the state of the art of force myography technique (FMG): analysis and assessment

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