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1.
Sensors (Basel) ; 21(5)2021 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-33668275

RESUMO

Although the interaction technology for virtual reality (VR) systems has evolved significantly over the past years, the text input efficiency in the virtual environment is still an ongoing problem. We deployed a word-gesture text entry technology based on gesture recognition in the virtual environment. This study aimed to investigate the performance of the word-gesture text entry technology with different input postures and VR experiences in the virtual environment. The study revealed that the VR experience (how long or how often using VR) had little effect on input performance. The hand-up posture has a better input performance when using word-gesture text entry technology in a virtual environment. In addition, the study found that the perceived exertion to complete the text input with word-gesture text entry technology was relatively high. Furthermore, the typing accuracy and perceived usability for using the hand-up posture were obviously higher than that for the hand-down posture. The hand-up posture also had less task workload than the hand-down posture. This paper supports that the word-gesture text entry technology with hand-up posture has greater application potential than hand-down posture.


Assuntos
Gestos , Interface Usuário-Computador , Realidade Virtual , Mãos , Humanos , Postura
2.
Sensors (Basel) ; 21(4)2021 Feb 22.
Artigo em Inglês | MEDLINE | ID: mdl-33671525

RESUMO

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.


Assuntos
Gestos , Aprendizado de Máquina , Algoritmos , Eletromiografia , Mãos , Humanos , Processamento de Sinais Assistido por Computador
3.
Anim Cogn ; 24(2): 281-297, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33675439

RESUMO

Dogs excel at understanding human social-communicative gestures like points and can distinguish between human informants who vary in characteristics such as knowledge or familiarity. This study explores if dogs, like human children, can use human social informants' past accuracy when deciding whom to trust. Experiment 1 tested whether dogs would behave differently in the presence of an accurate (vs. inaccurate) informant. Dogs followed an accurate informant's point significantly above chance. Further, when presented with an inaccurate point, dogs were more likely to ignore it and choose the correct location. Experiment 2 tested whether dogs could use informant past accuracy to selectively follow the point of the previously accurate informant. In test trials when informants simultaneously pointed at different locations (only one of which contained a treat), dogs chose the accurate informant at chance levels. Experiment 3 controlled for non-social task demands (e.g. understanding of hidden baiting and occlusion events) that may have influenced Experiment 2 performance. In test trials, dogs chose to follow the accurate (vs. inaccurate) informant. This suggests that like children, dogs may be able to use informants' past accuracy when choosing between information sources.


Assuntos
Gestos , Confiança , Animais , Cães , Humanos , Probabilidade , Reconhecimento Psicológico
4.
Sensors (Basel) ; 21(4)2021 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-33671364

RESUMO

Recording human gestures from a wearable sensor produces valuable information to implement control gestures or in healthcare services. The wearable sensor is required to be small and easily worn. Advances in miniaturized sensor and materials research produces patchable inertial measurement units (IMUs). In this paper, a hand gesture recognition system using a single patchable six-axis IMU attached at the wrist via recurrent neural networks (RNN) is presented. The IMU comprises IC-based electronic components on a stretchable, adhesive substrate with serpentine-structured interconnections. The proposed patchable IMU with soft form-factors can be worn in close contact with the human body, comfortably adapting to skin deformations. Thus, signal distortion (i.e., motion artifacts) produced for vibration during the motion is minimized. Also, our patchable IMU has a wireless communication (i.e., Bluetooth) module to continuously send the sensed signals to any processing device. Our hand gesture recognition system was evaluated, attaching the proposed patchable six-axis IMU on the right wrist of five people to recognize three hand gestures using two models based on recurrent neural nets. The RNN-based models are trained and validated using a public database. The preliminary results show that our proposed patchable IMU have potential to continuously monitor people's motions in remote settings for applications in mobile health, human-computer interaction, and control gestures recognition.


Assuntos
Gestos , Redes Neurais de Computação , Dispositivos Eletrônicos Vestíveis , Mãos , Humanos , Movimento (Física) , Tecnologia sem Fio , Punho , Articulação do Punho
5.
Sensors (Basel) ; 21(4)2021 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-33668488

RESUMO

There has been a conscious shift towards developing increasingly inclusive applications. However, despite this fact, most research has focused on supporting those with visual or hearing impairments and less attention has been paid to cognitive impairments. The purpose of this study is to analyse touch gestures used for touchscreens and identify which gestures are suitable for individuals living with Down syndrome (DS) or other forms of physical or cognitive impairments. With this information, app developers can satisfy Design for All (DfA) requirements by selecting adequate gestures from existing lists of gesture sets. Twenty touch gestures were defined for this study and a sample group containing eighteen individuals with Down syndrome was used. A tool was developed to measure the performance of touch gestures and participants were asked to perform simple tasks that involved the repeated use of these twenty gestures. Three variables are analysed to establish whether they influence the success rates or completion times of gestures, as they could have a collateral effect on the skill with which gestures are performed. These variables are Gender, Type of Down syndrome, and Socioeconomic Status. Analysis reveals that significant difference is present when a pairwise comparison is performed, meaning individuals with DS cannot perform all gestures with the same ease. The variables Gender and Socioeconomic Status do not influence success rates or completion times, but Type of DS does.


Assuntos
Síndrome de Down , Gestos , Design Universal , Atenção , Humanos
6.
Sensors (Basel) ; 21(3)2021 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-33540786

RESUMO

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.


Assuntos
Gestos , Mãos , Reconhecimento Automatizado de Padrão , Cor , Humanos , Aprendizagem , Aprendizado de Máquina Supervisionado
7.
Sci Data ; 8(1): 63, 2021 02 18.
Artigo em Inglês | MEDLINE | ID: mdl-33602931

RESUMO

Control of contemporary, multi-joint prosthetic hands is commonly realized by using electromyographic signals from the muscles remaining after amputation at the forearm level. Although this principle is trying to imitate the natural control structure where muscles control the joints of the hand, in practice, myoelectric control provides only basic hand functions to an amputee using a dexterous prosthesis. This study aims to provide an annotated database of high-density surface electromyographic signals to aid the efforts of designing robust and versatile electromyographic control interfaces for prosthetic hands. The electromyographic signals were recorded using 128 channels within two electrode grids positioned on the forearms of 20 able-bodied volunteers. The participants performed 65 different hand gestures in an isometric manner. The hand movements were strictly timed using an automated recording protocol which also synchronously recorded the electromyographic signals and hand joint forces. To assess the quality of the recorded signals several quantitative assessments were performed, such as frequency content analysis, channel crosstalk, and the detection of poor skin-electrode contacts.


Assuntos
Eletromiografia , Gestos , Mãos/fisiologia , Adulto , Membros Artificiais , Eletrodos , Feminino , Antebraço/fisiologia , Humanos , Contração Isométrica , Masculino , Pessoa de Meia-Idade , Movimento/fisiologia , Músculo Esquelético/fisiologia , Desenho de Prótese
8.
Acta Psychol (Amst) ; 212: 103226, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33310344

RESUMO

The tendency to imitate the actions of others appears to be a fundamental aspect of human social interaction. Emotional expressions are a particularly salient form of social stimuli (Vuilleumier & Schwartz, 2001) but their relationship to imitative behaviour is currently unclear. In this paper we report the results of five studies which investigated the effect of a target's dynamic emotional stimuli on participants' tendency to respond compatibly to the target's actions. Experiment one examined the effect of dynamic emotional expressions on the automatic imitation of opening and closing hand movements. Experiment two used the same basic paradigm but added gaze direction as an additional factor. Experiment three investigated the effect of dynamic emotional expressions on compatibility responses to handshakes. Experiment four investigated whether dynamic emotional expressions modulated response to valenced social gestures. Finally, experiment five compared the effects of dynamic and static emotional expressions on participants' automatic imitation of finger lifting. Across all five studies we reliably elicited a compatibility effect however, none of the studies found a significant modulating effect of emotional expression. This null effect was also supported by a random effects meta-analysis and a series of Bayesian t-tests. Nevertheless, these results must be caveated by the fact that our studies had limited power to detect effect sizes below d = 0.4. We conclude by situating our findings within the literature, suggesting that the effect of emotional expressions on automatic imitation is, at best, minimal.


Assuntos
Expressão Facial , Gestos , Teorema de Bayes , Emoções , Humanos , Comportamento Imitativo
9.
J Urol ; 205(1): 271-275, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33095096

RESUMO

PURPOSE: Deconstruction of robotic surgical gestures into semantic vocabulary yields an effective tool for surgical education. In this study we disassembled tissue dissection into basic gestures, created a classification system, and showed its ability to distinguish between experts and novices. MATERIALS AND METHODS: Videos of renal hilum preparation during robotic assisted partial nephrectomies were manually reviewed to identify all discrete surgical movements. Identified dissection movements were classified into distinct gestures based on the consensus of 6 expert surgeons. This classification system was then employed to compare expert and novice dissection patterns during the renal hilum preparation. RESULTS: A total of 40 robotic renal hilum preparation videos were reviewed, representing 16 from 6 expert surgeons (100 or more robotic cases) and 24 from 13 novice surgeons (fewer than 100 robotic cases). Overall 9,819 surgical movements were identified, including 5,667 dissection movements and 4,152 supporting movements. Nine distinct dissection gestures were identified and classified into the 3 categories of single blunt dissection (spread, peel/push, hook), single sharp dissection (cold cut, hot cut and burn dissect) and combination gestures (pedicalize, 2-hand spread, and coagulate then cut). Experts completed 5 of 9 dissection gestures more efficiently than novices (p ≤0.033). In consideration of specific anatomical locations, experts used more peel/push and less hot cut while dissecting the renal vein (p <0.001), and used more pedicalize while dissecting the renal artery (p <0.001). CONCLUSIONS: Using this novel dissection gesture classification system, key differences in dissection patterns can be found between experts/novices. This comprehensive classification of dissection gestures may be broadly applied to streamline surgical education.


Assuntos
Competência Clínica , Gestos , Nefrectomia/métodos , Procedimentos Cirúrgicos Robóticos/métodos , Cirurgiões/educação , Humanos , Rim/cirurgia , Nefrectomia/educação , Nefrectomia/estatística & dados numéricos , Procedimentos Cirúrgicos Robóticos/educação , Procedimentos Cirúrgicos Robóticos/estatística & dados numéricos , Cirurgiões/psicologia , Cirurgiões/estatística & dados numéricos , Gravação em Vídeo
10.
Sensors (Basel) ; 20(24)2020 Dec 09.
Artigo em Inglês | MEDLINE | ID: mdl-33316916

RESUMO

In recent years, we have witnessed a growing adoption of serious games in telerehabilitation by taking advantage of advanced multimedia technologies such as motion capture and virtual reality devices. Current serious game solutions for telerehabilitation suffer form lack of personalization and adaptiveness to patients' needs and performance. This paper introduces "RehaBot", a framework for adaptive generation of personalized serious games in the context of remote rehabilitation, using 3D motion tracking and virtual reality environments. A personalized and versatile gaming platform with embedded virtual assistants, called "Rehab bots", is created. Utilizing these rehab bots, all workout session scenes will include a guide with various sets of motions to direct patients towards performing the prescribed exercises correctly. Furthermore, the rehab bots employ a robust technique to adjust the workout difficulty level in real-time to match the patients' performance. This technique correlates and matches the patterns of the precalculated motions with patients' motions to produce a highly engaging gamified workout experience. Moreover, multimodal insights are passed to the users pointing out the joints that did not perform as anticipated along with suggestions to improve the current performance. A clinical study was conducted on patients dealing with chronic neck pain to prove the usability and effectiveness of our adjunctive online physiotherapy solution. Ten participants used the serious gaming platform, while four participants performed the traditional procedure with an active program for neck pain relief, for two weeks (10 min, 10 sessions/2 weeks). Feasibility and user experience measures were collected, and the results of experiments show that patients found our game-based adaptive solution engaging and effective, and most of them could achieve high accuracy in performing the personalized prescribed therapies.


Assuntos
Telerreabilitação , Jogos de Vídeo , Realidade Virtual , Terapia por Exercício , Gestos , Humanos
11.
Sensors (Basel) ; 21(1)2020 Dec 27.
Artigo em Inglês | MEDLINE | ID: mdl-33375501

RESUMO

As an important research direction of human-computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on four channel surface electromyography (sEMG) signals is proposed. First, the S-transform is applied to four channel sEMG signals to enhance the time-frequency detail characteristics of the signals. Then, multiscale singular value decomposition is applied to the multiple time-frequency matrix output of S-transform to obtain the time-frequency joint features with better robustness. The corresponding singular value permutation entropy is calculated as the eigenvalue to effectively reduce the dimension of multiple eigenvectors. The gesture features are used as input into the deep belief network for classification, and nine kinds of gestures are recognized with an average accuracy of 93.33%. Experimental results show that the multiscale singular value permutation entropy feature is especially suitable for the pattern classification of the deep belief network.


Assuntos
Gestos , Reconhecimento Automatizado de Padrão , Eletromiografia , Entropia , Humanos , Processamento de Sinais Assistido por Computador
12.
Sensors (Basel) ; 20(24)2020 Dec 11.
Artigo em Inglês | MEDLINE | ID: mdl-33322594

RESUMO

Presently, miniaturized sensors can be embedded in any small-size wearable to recognize movements on some parts of the human body. For example, an electrooculography-based sensor in smart glasses recognizes finger movements on the nose. To explore the interaction capabilities, this paper conducts a gesture elicitation study as a between-subjects experiment involving one group of 12 females and one group of 12 males, expressing their preferred nose-based gestures on 19 Internet-of-Things tasks. Based on classification criteria, the 912 elicited gestures are clustered into 53 unique gestures resulting in 23 categories, to form a taxonomy and a consensus set of 38 final gestures, providing researchers and practitioners with a larger base with six design guidelines. To test whether the measurement method impacts these results, the agreement scores and rates, computed for determining the most agreed gestures upon participants, are compared with the Condorcet and the de Borda count methods to observe that the results remain consistent, sometimes with a slightly different order. To test whether the results are sensitive to gender, inferential statistics suggest that no significant difference exists between males and females for agreement scores and rates.


Assuntos
Dedos , Gestos , Nariz , Adulto , Feminino , Humanos , Internet das Coisas , Masculino , Movimento
13.
Sensors (Basel) ; 20(24)2020 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-33339247

RESUMO

The typical configuration of virtual reality (VR) devices consists of a head-mounted display (HMD) and handheld controllers. As such, these units have limited utility in tasks that require hand-free operation, such as in surgical operations or assembly works in cyberspace. We propose a user interface for a VR headset based on a wearer's facial gestures for hands-free interaction, similar to a touch interface. By sensing and recognizing the expressions associated with the in situ intentional movements of a user's facial muscles, we define a set of commands that combine predefined facial gestures with head movements. This is achieved by utilizing six pairs of infrared (IR) photocouplers positioned at the foam interface of an HMD. We demonstrate the usability and report on the user experience as well as the performance of the proposed command set using an experimental VR game without any additional controllers. We obtained more than 99% of recognition accuracy for each facial gesture throughout the three steps of experimental tests. The proposed input interface is a cost-effective and efficient solution that facilitates hands-free user operation of a VR headset using built-in infrared photocouplers positioned in the foam interface. The proposed system recognizes facial gestures and incorporates a hands-free user interface to HMD, which is similar to the touch-screen experience of a smartphone.


Assuntos
Face , Gestos , Interface Usuário-Computador , Realidade Virtual , Mãos , Humanos
14.
Sensors (Basel) ; 20(24)2020 Dec 18.
Artigo em Inglês | MEDLINE | ID: mdl-33353008

RESUMO

Classification algorithms require training data initially labelled by classes to build a model and then to be able to classify the new data. The amount and diversity of training data affect the classification quality and usually the larger the training set, the better the accuracy of classification. In many applications only small amounts of training data are available. This article presents a new time series classification algorithm for problems with small training sets. The algorithm was tested on hand gesture recordings in tasks of person identification and gesture recognition. The algorithm provides significantly better classification accuracy than other machine learning algorithms. For 22 different hand gestures performed by 10 people and the training set size equal to 5 gesture execution records per class, the error rate for the newly proposed algorithm is from 37% to 75% lower than for the other compared algorithms. When the training set consists of only one sample per class the new algorithm reaches from 45% to 95% lower error rate. Conducted experiments indicate that the algorithm outperforms state-of-the-art methods in terms of classification accuracy in the problem of person identification and gesture recognition.


Assuntos
Gestos , Reconhecimento Automatizado de Padrão , Algoritmos , Mãos , Humanos , Aprendizado de Máquina , Reconhecimento Psicológico
15.
PLoS One ; 15(10): e0239139, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33001994

RESUMO

Studies have documented that traditional motor skills (i.e. motor habits) are part of the cultural way of life that characterises each society. Yet, it is still unclear to what extent motor skills are inherited through culture. Drawing on ethnology and motor behaviour, we addressed this issue through a detailed description of traditional pottery skills. Our goal was to quantify the influence of three kinds of constraints: the transcultural constraints of wheel-throwing, the cultural constraints induced via cultural transmission, and the potters' individual constraints. Five expert Nepalese potters were invited to produce three familiar pottery types, each in five specimens. A total of 31 different fashioning hand positions were identified. Most of them (14) were cross-cultural, ten positions were cultural, five positions were individual, and two positions were unique. Statistical tests indicated that the subset of positions used by the participants in this study were distinct from those of other cultural groups. Behaviours described in terms of fashioning duration, number of gestures, and hand position repertoires size highlighted both individual and cross-cultural traits. We also analysed the time series of the successive hand positions used throughout the fashioning of each vessel. Results showed, for each pottery type, strong reproducible sequences at the individual level and a clearly higher level of variability between potters. Overall, our findings confirm the existence of a cultural transmission in craft skills but also demonstrated that the skill is not fully determined by a cultural marking. We conclude that the influence of culture on craft skills should not be overstated, even if its role is significant given the fact that it reflects the socially transmitted part of the skill. Such research offers insights into archaeological problems in providing a representative view of how cultural constraints influence the motor skills implied in artefact manufacturing.


Assuntos
Arte , Características Culturais , Destreza Motora , Adulto , Comparação Transcultural , Gestos , Mãos , Hinduísmo , Humanos , Masculino , Pessoa de Meia-Idade , Nepal
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 649-652, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018071

RESUMO

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03% accuracy on our G. dataset (12 gestures) and 94.53% on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation.


Assuntos
Gestos , Redes Neurais de Computação , Algoritmos , Eletromiografia , Humanos , Reconhecimento Psicológico
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 682-685, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018079

RESUMO

Surface electromyography has become one of the popular methods for recognizing hand gestures. In this paper, the performance of four classification methods on sEMG signals have been investigated. These methods are developed by combinations of two feature extraction methods, including Mean Absolute Value and Short-Time Fourier Transform, and two classifiers, including Support Vector Machine and Convolutional Neural Network. These classification methods achieved an accuracy over 97 % on the NinaPro dataset 1. In addition, a new dataset, which includes the Activities of Daily Living, was proposed and an accuracy over 98 % was obtained by applying the presented classification methods.This methodology can provide the basis for a robust quantitative technique to evaluate hand grasps of stroke patients in performing activities of daily living that in turn can lead to a more efficient rehabilitation regimen.


Assuntos
Atividades Cotidianas , Gestos , Eletromiografia , Humanos , Redes Neurais de Computação , Reconhecimento Psicológico
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3302-3305, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018710

RESUMO

Electromyogram (EMG) pattern recognition has been utilized with the traditional machine and deep learning architectures as a control strategy for upper-limb prostheses. However, most of these learning architectures, including those in convolutional neural networks, focus the spatial correlations only; but muscle contractions have a strong temporal dependency. Our primary aim in this paper is to investigate the effectiveness of recurrent deep learning networks in EMG classification as they can learn long-term and non-linear dynamics of time series. We used a Long Short-Term Memory (LSTM-based) neural network to perform multiclass classification with six grip gestures at three different force levels (low, medium, and high) generated by nine amputees. Four different feature sets were extracted from the raw signals and fed to LSTM. Moreover, to investigate a generalization of the proposed method, three different training approaches were tested including 1) training the network with feature extracted from one specific force level and testing it with the same force level, 2) training the network with one specific force level and testing it with two remained force levels, and 3) training the network with all of the force levels and testing it with a single force level. Our results show that LSTM-based neural network can provide reliable performance with average classification errors of around 9% across all nine amputees and force levels. We demonstrate the applicability of deep learning for upperlimb prosthesis control.


Assuntos
Gestos , Memória de Curto Prazo , Algoritmos , Amputados , Eletromiografia , Humanos , Redes Neurais de Computação
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3339-3342, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018719

RESUMO

In recent years, many electromyography (EMG) benchmark databases have been made publicly available to the myoelectric control research community. Many small laboratories that lack the instrumentation, access, and experience needed to collect quality EMG data have used these benchmark datasets to explore and propose new signal processing and pattern recognition algorithms. It is widely accepted that noise contamination can affect the performance of myoelectric control systems, and so useful datasets should maintain good signal quality to ensure accurate results for proposed EMG-based gesture recognition systems. Despite the availability and adoption of benchmarks datasets, however, the quality of the EMG signals in these benchmarks has not yet been examined. In this study, the signal quality of the Non-Invasive Adaptive Prosthetics (NinaPro) dataset, the most widely known publicly available benchmark database to date, was comprehensively investigated with the goals of: 1) reporting the level of noise contamination in each NinaPro sub-dataset, 2) proposing signal quality criteria for assessing EMG datasets, 3) analyzing the effect of signal quality on classification performance, and 4) examining the quality of the data labels.


Assuntos
Benchmarking , Gestos , Bases de Dados Factuais , Eletromiografia , Processamento de Sinais Assistido por Computador
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3448-3451, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018745

RESUMO

Recent advancements in wearable technologies have increased the potential for practical gesture recognition systems using electromyogram (EMG) signals. However, despite the high classification accuracies reported in many studies (> 90%), there is a gap between academic results and industrial success. This is in part because state-of-the-art EMG-based gesture recognition systems are commonly evaluated in highly-controlled laboratory environments, where users are assumed to be resting and performing one of a closed set of target gestures. In real world conditions, however, a variety of non-target gestures are performed during activities of daily living (ADLs), resulting in many false positive activations. In this study, the effect of ADLs on the performance of EMG-based gesture recognition using a wearable EMG device was investigated. EMG data for 14 hand and finger gestures, as well as continuous activity during uncontrolled ADLs (>10 hours in total) were collected and analyzed. Results showed that (1) the cluster separability of 14 different gestures during ADLs was 171 times worse than during rest; (2) the probability distributions of EMG features extracted from different ADLs were significantly different (p <; 0.05). (3) of the 14 target gestures, a right angle gesture (extension of the thumb and index finger) was least often inadvertently activated during ADLs. These results suggest that ADLs and other non-trained gestures must be taken into consideration when designing EMG-based gesture recognition systems.


Assuntos
Gestos , Dispositivos Eletrônicos Vestíveis , Atividades Cotidianas , Algoritmos , Eletromiografia , Humanos , Reconhecimento Automatizado de Padrão
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