An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model

Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB’s toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN.

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Design of an Arm Gesture Recognition System Using Feature Transformation and Hidden Markov Models

KIPS Transactions on Software and Data Engineering ◽

10.3745/ktsde.2013.2.10.723 ◽

2013 ◽

Vol 2 (10) ◽

pp. 723-730 ◽

Cited By ~ 2

Author(s):

Se-Kyeong Heo ◽

Ye-Seul Shin ◽

Hye-Suk Kim ◽

In-Cheol Kim

Keyword(s):

Hidden Markov Models ◽

Gesture Recognition ◽

Markov Models ◽

Hidden Markov ◽

Recognition System ◽

Feature Transformation

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CrossGR

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3448100 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-23

Author(s):

Xinyi Li ◽

Liqiong Chang ◽

Fangfang Song ◽

Ju Wang ◽

Xiaojiang Chen ◽

...

Keyword(s):

Gesture Recognition ◽

Low Cost ◽

User Involvement ◽

Recognition System ◽

Training Data ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Target User ◽

Order Of Magnitude ◽

Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

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Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽

10.3390/s21072540 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2540

Author(s):

Zhipeng Yu ◽

Jianghai Zhao ◽

Yucheng Wang ◽

Linglong He ◽

Shaonan Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Gesture Recognition ◽

Recognition System ◽

Surface Emg ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Training Time ◽

Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

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