Hand gesture recognition using machine learning and the Myo armband

This paper presents a lightweight convolutional neural network (CNN) model for hand gesture recognition and implements it on a nano drone for flight control. Specifically, the nano drone captures images and determines its flight direction based on the hand gesture recognition results of the captured images. Due to limited hardware resources, the nano drone has only a 64KB L1 cache, a 512KB L2 cache, and a RISC-V CPU, which makes CNN deployment challenging. To address this issue, we design and optimize a 10-layer simplified CNN model and quantize the computations to INT8. Experimental results show that the proposed model achieves 90% accuracy in 5-class hand gesture recognition while maintaining a compact size of only 72.5 KB. Furthermore, it operates at a recognition speed of 7 FPS, enabling real-time performance.

MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform

A hidden Markov model (HMM) has been widely used as a cost-effective solution for time series based applications such as hand gesture recognition. A large number of parameters in an HMM often result in a degradation of classification accuracy due to overfitting. Furthermore, the accuracy is often affected by the initialization values of the parameters. Left-right banded (LRB) HMM is one of the representative models which improve the classification accuracy by reducing the number of parameters. However, LRB HMM still suffers from false negative misclassification when it is applied for hand gesture recognition. Misclassification in hand gesture recognition arise from similar trajectories of multiple gestures that are represented by hidden states. This paper proposes a new initialization process that reduces not only state transition parameters but also hidden state observation parameters. To this end, the gesture trajectory is predicted and then the parameters are masked out when their probabilities are negligibly small in the predicted trajectory. Experiment results show that the confusion rate is nearly 14% lower than the LRB HMM while maintaining the classification accuracy.

The subject of the study is the methods of controlling a robotic manipulator based on surface electromyography (EMG) signals using individualized normalization and classification of gestures by a convolutional neural network (CNN). The aim of the work is to create an effective control system that combines pre-normalization of EMG signals and deep learning to increase the accuracy of gesture recognition and stability in real time. For this purpose, the following were used: signal collection using the Myo Armband, pre-processing (min-max and zero-mean), signal conversion into images through a sliding window, CNN training (ResNet, Adam optimization) and comparison with SVM and Random Forest models. The results obtained showed a classification accuracy of 97.27% in the test environment and 91.71% in real time. The CNN model outperformed traditional methods by 18–19%, and zero-mean increased the accuracy by 2.34% compared to min-max normalization. The system remained stable even with variations in the bracelet position due to individual normalization. Conclusions: the proposed system demonstrates high accuracy, reliability and adaptability in real time. The scientific novelty lies in the combination of individualized normalization of EMG signals with ResNet, which provides stability and exceeds the accuracy of traditional algorithms. In the future, it is planned to expand the set of gestures, study more complex conditions and optimize neural networks for embedded systems.

In this paper, we propose a hand gesture-based interface for navigating 3D virtual space using Leap Motion and machine learning. Using the interface, the user can control an avatar in a first-person perspective by making it move in 4 directions, rotate its viewpoint, grasp/release an object, and adjust moving/rotating speed. After defining 9 types of hand gesture, we used the leap motion device to capture 150 hand skeletal datasets for each type, and converted them into feature datasets, with which we developed a heuristic algorithm and a machine learning (multilayer perceptron) model for hand gesture recognition. Experimental results showed that the machine learning model was much more accurate than the heuristic algorithm. We also implemented 3 methods for 3D virtual space navigation: the use of keyboard and mouse (KB) and the use of the leap motion device with the heuristic algorithm (HA) and the machine learning model (ML). After conducting a user study in which each of 15 subjects was guided to find a target object and move it to a start position with the methods, we found from the user study that the method ML outperformed the others in task time and in most of qualitative aspects.

Hand gestures have been widely applied to interface as the way of interaction between human and computers. Since a human hand can express various shapes of gestures, previous models for recognizing them cannot distinguish them accurately since they use only single model for recognition. For efficient hand gesture recognition with its enhanced performance, we propose the fuzzy c-means clustering based mixture-of-experts (FME). The proposed method uses multiple local experts obtained via fuzzy c-means clustering and decisions from them are combined with the gating network. To evaluate the performance of the proposed method, we conduct experiments including comparisons with alternative models for hand gesture recognition. As the result of experiments, the proposed model shows improved gesture recognition performance, especially performance on similar hand gesture recognition.

Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.

This study presents a gesture-based authentication system utilizing triboelectric nanogenerator (TENG) sensors. As self-powered devices capable of generating high-voltage outputs without external power, TENG sensors are well-suited for low-power IoT sensors and smart device applications. The proposed system recognizes single tap, double tap, and holding gestures. The electrical characteristics of the sensor were evaluated under varying pressure conditions, confirming a linear relationship between applied force and output voltage. These results demonstrate the sensor's high sensitivity and precision. A threshold-based classification algorithm was developed by analyzing signal features enabling accurate gesture recognition in real time. To enhance the practicality and scalability of the system, the algorithm was further configured to automatically segment raw sensor signals into gesture intervals and assign corresponding labels. From these segments, time-domain statistical features were extracted to construct a training dataset. A random forest classifier trained on this dataset achieved a high classification accuracy of 98.15% using five-fold cross-validation. The system reduces security risks commonly associated with traditional keypad input, offering a user-friendly and reliable authentication interface. This work confirms the feasibility of TENG-based gesture recognition for smart locks, IoT authentication devices, and wearable electronics, with future improvements expected through AI-based signal processing and multi-sensor integration.

Dynamic gesture recognition is one of the most interesting and challenging areas of Human-Robot-Interaction (HRI). Problems like image segmentation, temporal and spatial feature extraction and real time recognition are the most promising issues to name in this context. This work proposes a deep neural model to recognize dynamic gestures with minimal image preprocessing and real time recognition in an experimental set up using a humanoid robot. We conducted two experiments with command gestures in an offline fashion and for demonstration in a Human-Robot-Interaction (HRI) scenario. Our results showed that the proposed model achieves high classification rates of the gestures executed by different subjects, who perform them with varying speed. With our additional audio feedback we demonstrate that our system performs in real time.

In this paper, we purpose a Hand gesture recognition model which can be used in real time application. This model is based on the mediapipe frame work of the google, Tensor flow in openCv and python and classification using feed forward neural network with keras model. The structure of the proposed work consists of 3 modules: Grabbing the frames, detecting hand landmarks and classification. The proposed model has the accuracy 95.7% at recognizing 10 kinds of hand gestures(Thumbs up, Thumbs down, Peace, Smile, Rock, Ok, Fist, livelong, call me, stop). A hand gesture recognition model that reacts rapidly and with generally acceptable accuracy is one of this work’s primary achievements and pre trained model for feature extraction. The unique approach of the suggested approach is that it detects hand landmarks using Google’s MediaPipe, which is faster and more accurate than traditional methods that rely on geometry, form, and edge data. For modelling sequence data and for recognising gestures, the LSTM model has proven to be quite successful.

This research focuses on real-time gesture learning and recognition. Events arrive in a continuous stream without explicitly given boundaries. To obtain temporal accuracy, weneed to consider the lag between the detection of an eventand any effects we wish to trigger with it. Two methodsfor real time gesture recognition using a Nintendo Wii controller are presented. The first detects gestures similar to agiven template using either a Euclidean distance or a cosinesimilarity measure. The second method uses novel information theoretic methods to detect and categorize gestures inan unsupervised way. The role of supervision, detection lagand the importance of haptic feedback are discussed.

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image.

<p><em>Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning. In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. </em><em>This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant</em></p>

This paper discusses real-time gesture recognition from moving images that is efficient for human-friendly man–machine interaction. With the proposed method, the eigenspace is built for multi-input images taken from different directions; in so doing, 3D data can be acquired without such problems of stereo methods as those of correspondence or calibration. Besides, the new method supports real-time recognition of such gestures that cannot be processed using single input images because of self-occlusion or other complications. In addition, 3D interpretation ensures more accurate recognition of gesture parameters (speed, size). The proposed method was applied to a real-time interactive system (virtual conducting system), and proved to be efficient. © 1999 Scripta Technica, Syst Comp Jpn, 30(13): 61–72, 1999

Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.