Performance of Forearm FMG for Estimating Hand Gestures and Prosthetic Hand Control

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

Section: Muscle Gross Motion-based Hmismentioning

confidence: 99%

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

Esposito

Centracchio

Andreozzi

et al. 2021

“…sEMG signal processing and classification have been investigated thoroughly for a variety of applications [ 4 , 13 , 14 , 15 , 16 ]. Force myography (FMG) [ 17 ] is an emerging alternative technology that has attracted attention recently in hand gesture recognition [ 18 , 19 ]. FMG-based hand gesture recognition method utilizes an array of force-resisting sensors surrounding a specific part of the limb to capture the underlying musculotendinous complex’s volumetric changes during performing gestures [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance

Asfour

Menon

Jiang

2021

ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.

“…In contrast, FMG technique is cost effective, repeatable, electrically robust, and requires minimal signal processing and optional feature engineering [ 3 ]. In addition, FMG technique was found effective, like sEMG, in several research studies [ 4 , 5 , 6 , 7 ] as an emerging technology and has been studied in similar applications of gesture recognition, prosthetic control, activities of daily life, rehabilitation, and human machine interactions (HMI) [ 8 , 9 , 10 , 11 , 12 , 13 ]. However, there are very few studies on FMG-based deep transfer learning (DL) techniques in human robot interactions (HRI).…”

Section: Introductionmentioning

confidence: 99%

Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization

Zakia

Menon

2021

Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes time consuming and impractical. In real scenarios, a pretrained transfer learning model predicting forces quickly once fine-tuned to target distribution would be a favorable choice and hence needs to be examined. Therefore, in this study a unified supervised FMG-based deep transfer learner (SFMG-DTL) model using CNN architecture was pretrained with multiple sessions FMG source data (Ds, Ts) and evaluated in estimating forces in separate target domains (Dt, Tt) via supervised domain adaptation (SDA) and supervised domain generalization (SDG). For SDA, case (i) intra-subject evaluation (Ds ≠ Dt-SDA, Ts ≈ Tt-SDA) was examined, while for SDG, case (ii) cross-subject evaluation (Ds ≠ Dt-SDG, Ts ≠ Tt-SDG) was examined. Fine tuning with few “target training data” calibrated the model effectively towards target adaptation. The proposed SFMG-DTL model performed better with higher estimation accuracies and lower errors (R2 ≥ 88%, NRMSE ≤ 0.6) in both cases. These results reveal that interactive force estimations via transfer learning will improve daily HRI experiences where “target training data” is limited, or faster adaptation is required.