Investigation into the Potential to Create a Force Myography-based Smart-home Controller for Aging Populations

Delva, Mona L.; Sakr, Maram; Chegani, Rana Sadeghi; Khoshnam, Mahta; Menon, Carlo

doi:10.1109/biorob.2018.8488087

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…The emphasis of this study was not on age and gender effects, although previous research reported a relationship between age, gender and weight, and anthropometry measures such as skinfold thickness [40]. Given the potential for FMG technology to be used as an assistive tool for senior populations [41], we thus encourage future studies to consider the effects of age and gender on the relationship between anthropometric measures and FMG signal acquisition quality and modeling accuracy.…”

Section: Discussionmentioning

confidence: 99%

Wrist-worn wearables based on force myography: on the significance of user anthropometry

et al. 2020

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Background Force myography (FMG) is a non-invasive technology used to track functional movements and hand gestures by sensing volumetric changes in the limbs caused by muscle contraction. Force transmission through tissue implies that differences in tissue mechanics and/or architecture might impact FMG signal acquisition and the accuracy of gesture classifier models. The aim of this study is to identify if and how user anthropometry affects the quality of FMG signal acquisition and the performance of machine learning models trained to classify different hand and wrist gestures based on that data. Methods Wrist and forearm anthropometric measures were collected from a total of 21 volunteers aged between 22 and 82 years old. Participants performed a set of tasks while wearing a custom-designed FMG band. Primary outcome measure was the Spearman’s correlation coefficient (R) between the anthropometric measures and FMG signal quality/ML model performance. Results Results demonstrated moderate (0.3 ≤|R| < 0.67) and strong (0.67 ≤ |R|) relationships for ratio of skinfold thickness to forearm circumference, grip strength and ratio of wrist to forearm circumference. These anthropometric features contributed to 23–30% of the variability in FMG signal acquisition and as much as 50% of the variability in classification accuracy for single gestures. Conclusions Increased grip strength, larger forearm girth, and smaller skinfold-to-forearm circumference ratio improve signal quality and gesture classification accuracy.

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Section: Discussionmentioning

confidence: 99%

Wrist-worn wearables based on force myography: on the significance of user anthropometry

et al. 2020

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“…FMG is a non-invasive, wearable technology that usually employs force sensing resistors (FSRs) to detect resistance changes when the pressure changes during muscle contractions [ 43 ]. As a promising biotechnology, the FMG technique was studied in human–machine interaction (HMI) and human–robot interaction (HRI) [ 44 , 45 , 46 , 47 ]. Common industrial tasks such as object transportation or handover may require the worker to apply hand forces during interactions with the robot.…”

Section: Introductionmentioning

confidence: 99%

Detecting Safety Anomalies in pHRI Activities via Force Myography

Zakia

Menon

2023

Bioengineering

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The potential application of using a wearable force myography (FMG) band for monitoring the occupational safety of a human participant working in collaboration with an industrial robot was studied. Regular physical human–robot interactions were considered as activities of daily life in pHRI (pHRI-ADL) to recognize human-intended motions during such interactions. The force myography technique was used to read volumetric changes in muscle movements while a human participant interacted with a robot. Data-driven models were used to observe human activities for useful insights. Using three unsupervised learning algorithms, isolation forest, one-class SVM, and Mahalanobis distance, models were trained to determine pHRI-ADL/regular, preset activities by learning the latent features’ distributions. The trained models were evaluated separately to recognize any unwanted interactions that differed from the normal activities, i.e., anomalies that were novel, inliers, or outliers to the normal distributions. The models were able to detect unusual, novel movements during a certain scenario that was considered an unsafe interaction. Once a safety hazard was detected, the control system generated a warning signal within seconds of the event. Hence, this study showed the viability of using FMG biofeedback to indicate risky interactions to prevent injuries, improve occupational health, and monitor safety in workplaces that require human participation.

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“…FSRs are small, easy to use, low-cost and repeatable, which is essential for any wearable sensor. As a contemporary technique to the sEMG, FMG signals has proven their potential in similar researches of finger and gesture recognition, isometric grasp/hand force recognition, prosthetic upper limb control, rehabilitation applications, and assistive solutions to activities in daily life (ADLs) of elders [17][18][19][20][21][22]. Surprisingly, this technique is found comparable and sometimes better than the sEMG in those applications.…”

Section: Introductionmentioning

confidence: 99%

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Zakia

Menon

2020

Sensors

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Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.

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Investigation into the Potential to Create a Force Myography-based Smart-home Controller for Aging Populations

Cited by 10 publications

References 33 publications

Wrist-worn wearables based on force myography: on the significance of user anthropometry

Wrist-worn wearables based on force myography: on the significance of user anthropometry

Detecting Safety Anomalies in pHRI Activities via Force Myography

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

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