Force myography controlled multifunctional hand prosthesis for upper-limb amputees

Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects’ data has been created. In this paper, gesture accuracies under different sampling frequencies and channel’s number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition

Lei

Zhang

Fang

et al. 2021

“…Indeed, it is well known that a contracting muscle changes its shape or dimensions (e.g., its cross-section), and small vibrations due to the progressive recruitment of motor units can be perceived (i.e., the mechanomyogram). Furthermore, muscle contraction also involves an increased blood afflux and displacement of muscletendon groups or even bones [4,[143][144][145][146][147]. Therefore, these features reflect three main mechanical components: gross motion of specific muscle groups with associated muscle swelling; muscle vibrations; and movement of musculotendinous groups.…”

Section: Hmi Control Based On Muscle Mechanical Motionmentioning

confidence: 99%

“…A study by Prakash et al proposed an FMG-controlled prosthetic hand for upper limb amputees. An FMG sensor consisting of two FSRs was applied on the residual forearm of an amputee to detect muscle contractile force, then converted into input commands to the prosthesis [144]. Esposito et al also presented an alternative approach to EMG, with comparable performances, for improved hand prosthesis control based on muscle contraction detection via an FSR-based sensor [143].…”

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

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.

“…sEMG is the most established muscle activity-based hand gesture recognition technique [11,12] whose sensors are mounted on the upper limb to detect muscles' electrical signals for gesture classification. sEMG signal processing and classification have been investigated thoroughly for a variety of applications [4,[13][14][15][16].…”

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.