Recognition of Badminton Shot Action Based on the Improved Hidden Markov Model

Ma, Chao; Yu, Dayang; Feng, Hui

doi:10.1155/2021/7892902

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…The experimental results showed that the two-layer HMM classification algorithm can achieve the best performance in terms of recognition accuracy and recognition time. Ma et al [22] further proposed a frequency-weighted training method taking inputs of a single accelerometer to improve the performance of HMM on badminton hitting action recognition.…”

Section: Conventional Machine Learningmentioning

confidence: 99%

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition

Deng,

Zhang,

2023

Sensors

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Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level and sophisticated, such as in the multiple technical skills of playing badminton, it is usually a challenging task due to the difficulty of feature extraction from the sensor data. As a kind of end-to-end approach, deep neural networks have the capacity of automatic feature learning and extracting. However, most current studies on sensor-based badminton activity recognition adopt CNN-based architectures, which lack the ability of capturing temporal information and global signal comprehension. To overcome these shortcomings, we propose a deep learning framework which combines the convolutional layers, LSTM structure, and self-attention mechanism together. Specifically, this framework can automatically extract the local features of the sensor signals in time domain, take the LSTM structure for processing the badminton activity data, and focus attention on the information that is essential to the badminton activity recognition task. It is demonstrated by the experimental results on an actual badminton single sensor dataset that our proposed framework has obtained a badminton activity recognition (37 classes) accuracy of 97.83%, which outperforms the existing methods, and also has the advantages of lower training time and faster convergence.

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Section: Conventional Machine Learningmentioning

confidence: 99%

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition

Deng,

Zhang,

2023

Sensors

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“…In [ 93 ], the authors focused on recognizing badminton actions with wearable sensors. In their case, a single acceleration sensor is fixed on the end of the badminton racket handle, which is used to collect the data.…”

Section: Har Implementation In Different Sportsmentioning

confidence: 99%

An overview of Human Action Recognition in sports based on Computer Vision

Host

Ivašić-Kos

2022

Heliyon

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“…Both of them are suitable for modeling sequence models. Ma et al [16] used the Hidden Markov Model (HMM) to recognize human action. Samr and Nizar [17] used the Beta-Liouville HMM action classification method.…”

Section: Human Action Classificationmentioning

confidence: 99%

A Lightweight Human Action Classification Method for Green IoT Sport Applications

Xiao

Huang

et al. 2022

Journal of Sensors

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This paper proposes a lightweight human action classification method for Green Internet of Things (IoT) sport applications. This method classifies the human motion data collected by wearables or other IoT devices with energy-efficient techniques, by enabling a small number of sample training and incremental classification to achieve the purpose of energy-efficient. To lessen the complexity of the model and reduce the number of samples required for parameter estimation, we propose a shared Hidden Conditional Random Field (sHCRF) model. The sHCRF model adds a shared-classification layer structure to reduce the parameter computation. In the experiments, the classification accuracy of the sHCRF model is above 95%. This paper introduces an incremental learning method based on knowledge distillation. The new model suppresses the forgetting of existing classification knowledge while fitting new data to learn new classification knowledge. In the incremental scenarios, the classification accuracy of the sHCRF model is above 70%. The experimental results show that this method can lightly implement convenient and fast automatic classification of action acquisition.

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Recognition of Badminton Shot Action Based on the Improved Hidden Markov Model

Cited by 8 publications

References 23 publications

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition

An overview of Human Action Recognition in sports based on Computer Vision

A Lightweight Human Action Classification Method for Green IoT Sport Applications

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