Gesture recognition using Markov Systems and wearable wireless inertial sensors

Arsenault, Dennis L.; Whitehead, Anthony

doi:10.1109/tce.2015.7389796

Cited by 21 publications

(5 citation statements)

References 10 publications

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“…Nowadays, hand gesture recognition employs traditional methods like linear classifiers [12], [32], Hidden Markov Models (HMM) [13], [34], and Dynamic Time Warping (DTW) [9], [35]. Linear classifiers [12], [32], [33] analyze gesture feature vectors but may struggle with complex gesture patterns with subtle differences [36].…”

Section: B Human Motion Gesture Recognitionmentioning

confidence: 99%

Toward Intuitive 3D Interactions in Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach

Qi,

Cibrian,

Raswan

et al. 2024

IEEE Access

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Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using 3D position and orientation data from handheld controllers for gesture classification. To evaluate the performance of the proposed model, we designed 13 dual-hand gestures representing fundamental 3D interaction tasks: translation, rotation, scaling, and selection, and then collected data from 26 participants using a VR system. The model's performance was rigorously tested under various hand-tracking scenarios, including dual-hand versus single-hand inputs and complete versus partial motion features. Benchmarking against four state-of-the-art neural networks revealed that CWT-CNN-TCN reliably detects dual-hand gestures with limited tracking data and outperforms the benchmarks. This result paves the way for a dual-hand gesture-based interface that enriches intuitive 3D interactions in VR.

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Section: B Human Motion Gesture Recognitionmentioning

confidence: 99%

Toward Intuitive 3D Interactions in Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach

Qi,

Cibrian,

Raswan

et al. 2024

IEEE Access

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“…Tennis stroke detection and classification is motion detection and classification problem, which can be observed also as a hand-gesture classification case [21]. For this type of tasks, some popular methods are widely used, like hidden Marko models (HMM) or dynamic time warping (DTW) and similar (like QDTW) [22].…”

Section: Introductionmentioning

confidence: 99%

Smart Wearables for Tennis Game Performance Analysis

Kos¹,

Kramberger²

2020

Sports Science and Human Health - Different Approaches

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For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete's body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player's wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes' arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed.

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“…To recognize both linear and rotational gestures, methods that rely on both accelerometers and gyroscopes were proposed. A research applied the Markov chain algorithm to monitor the movement of the arms using accelerometer and gyroscope sensors worn on the forearms [29]. In another recent work paper, a real-time gesture recognition technique, named Continuous Hand Gestures (CHG), was reported [30].…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Han

Yoon

2019

Sensors

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Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90–96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.

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Gesture recognition using Markov Systems and wearable wireless inertial sensors

Cited by 21 publications

References 10 publications

Toward Intuitive 3D Interactions in Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach

Toward Intuitive 3D Interactions in Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach

Smart Wearables for Tennis Game Performance Analysis

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

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