Towards Developing an Effective Hand Gesture Recognition System for Human Computer Interaction: A Literature Survey

Gesture recognition is a mathematical analysis of movement of body parts (hand / face) done with the help of computing device. It helps computers to understand human body language and build a more powerful link between humans and machines. Many research works are developed in the field of hand gesture recognition. Each works have achieved different recognition accuracies with different hand gesture datasets, however most of the firms are having insufficient insight to develop necessary achievements to meet their development in real time datasets. Under such circumstances, it is very essential to have a complete knowledge of recognition methods of hand gesture recognition, its strength and weakness and the development criteria as well. Lots of reports declare its work to be better but a complete relative analysis is lacking in these works. In this paper, we provide a study of representative techniques for hand gesture recognition, recognition methods and also presented a brief introduction about hand gesture recognition. The main objective of this work is to highlight the position of various recognition techniqueswhich can indirectly help in developing new techniques for solving the issues in the hand gesture recognition systems. Moreover we present a concise description about the hand gesture recognition systems recognition methods and the instructions for future research

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

Deep Architectures for Visual Recognition and Description

In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy

Towards Robust and Deployable Gesture and Activity Recognisers

Smartphones and wearables have become an extension of one's self, with gestures providing quick access to command execution, and activity tracking helping users log their daily life. Recent research in gesture recognition points towards common events like a user re-wearing or readjusting their smartwatch deteriorate recognition accuracy significantly. Further, the available state-of-the-art deep learning models for gesture or activity recognition are too large and computationally heavy to be deployed and run continuously in the background. This problem of engineering robust yet deployable gesture recognisers for use in wearables is open-ended. This thesis provides a review of known approaches in machine learning and human activity recognition (HAR) for addressing model robustness. This thesis also proposes variations of convolution based models for use with raw or spectrogram sensor data. Finally, a cross-validation based evaluation approach for quantifying individual and situational-variabilities is used to demonstrate that with an application-oriented design, models can be made two orders of magnitude smaller while improving on both recognition accuracy and robustness

Machine Learning for Hand Gesture Classification from Surface Electromyography Signals

Classifying hand gestures from Surface Electromyography (sEMG) is a process which has applications in human-machine interaction, rehabilitation and prosthetic control. Reduction in the cost and increase in the availability of necessary hardware over recent years has made sEMG a more viable solution for hand gesture classification. The research challenge is the development of processes to robustly and accurately predict the current gesture based on incoming sEMG data. This thesis presents a set of methods, techniques and designs that improve upon evaluation of, and performance on, the classification problem as a whole. These are brought together to set a new baseline for the potential classification. Evaluation is improved by careful choice of metrics and design of cross-validation techniques that account for data bias caused by common experimental techniques. A landmark study is re-evaluated with these improved techniques, and it is shown that data augmentation can be used to significantly improve upon the performance using conventional classification methods. A novel neural network architecture and supporting improvements are presented that further improve performance and is refined such that the network can achieve similar performance with many fewer parameters than competing designs. Supporting techniques such as subject adaptation and smoothing algorithms are then explored to improve overall performance and also provide more nuanced trade-offs with various aspects of performance, such as incurred latency and prediction smoothness. A new study is presented which compares the performance potential of medical grade electrodes and a low-cost commercial alternative showing that for a modest-sized gesture set, they can compete. The data is also used to explore data labelling in experimental design and to evaluate the numerous aspects of performance that must be traded off

Robust and Deployable Gesture Recognition for Smartwatches

Funding Information: This work was supported by the Department of Communications and Networking – Aalto University, Finnish Center for Artificial Intelligence (FCAI) and the Academy of Finland projects Human Automata (Project ID: 328813), BAD (Project ID: 318559), Huawei Technologies, and the Horizon 2020 FET program of the European Union (grant CHIST-ERA-20-BCI-001). Publisher Copyright: © 2022 ACM. Open Access fee has been paid, but the PDF version does not contain information on OA licence.Gesture recognition on smartwatches is challenging not only due to resource constraints but also due to the dynamically changing conditions of users. It is currently an open problem how to engineer gesture recognisers that are robust and yet deployable on smartwatches. Recent research has found that common everyday events, such as a user removing and wearing their smartwatch again, can deteriorate recognition accuracy significantly. In this paper, we suggest that prior understanding of causes behind everyday variability and false positives should be exploited in the development of recognisers. To this end, first, we present a data collection method that aims at diversifying gesture data in a representative way, in which users are taken through experimental conditions that resemble known causes of variability (e.g., walking while gesturing) and are asked to produce deliberately varied, but realistic gestures. Secondly, we review known approaches in machine learning for recogniser design on constrained hardware. We propose convolution-based network variations for classifying raw sensor data, achieving greater than 98% accuracy reliably under both individual and situational variations where previous approaches have reported significant performance deterioration. This performance is achieved with a model that is two orders of magnitude less complex than previous state-of-the-art models. Our work suggests that deployable and robust recognition is feasible but requires systematic efforts in data collection and network design to address known causes of gesture variability.Peer reviewe