Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

Hisham, Muhammad Nafis; Hassan, Mohd Fadzil Abu; Ibrahim, Norazlin; Zin, Zalhan Mohd

doi:10.14569/ijacsa.2022.0130709

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…The smaller the MSE, the more accurate the model is in predicting the key point location. In the literature [32], MSE is used to determine the constant value that minimizes the error and how close the estimate or prediction is to the actual data. R2 measures how well the forecast data match the baseline data.…”

Section: Resultsmentioning

confidence: 99%

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion

Hu,

Zhang,

Wang

et al. 2024

Sensors

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The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human–computer interaction, sports rehabilitation, and virtual reality.

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Section: Resultsmentioning

confidence: 99%

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion

Hu,

Zhang,

Wang

et al. 2024

Sensors

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“…This system could be used in fitness or rehabilitation centers for patient treatment. Another study [ 33 ] presented a human tracking model for squat exercises using open-source MediaPipe technology. The model detects and tracks vital body joints, analyzing critical joint motions for abnormal movements.…”

Section: Literature Reviewmentioning

confidence: 99%

PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition

Khan,

Lee

2024

Sensors

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Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

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“…The classifier obtained an accuracy score of 92.92%. Authors in [60] use a mono camera along with MediaPipe to extract 3D data from 2D video data and classify squats using Double Exponential Smoothing (DES) while authors in [61] discuss how to apply pattern recognition and ML techniques to identify whole-body movement patterns during the performance of deep squats and hurdle steps. Rungsawasdisap et al [62], [63] describe a method to recognize the squat action and classify it into six different types of squats using HMM and CNN, respectively.…”

Section: ) Exercise Evaluation and Analysismentioning

confidence: 99%

AI Trainer: Autoencoder Based Approach for Squat Analysis and Correction

Chariar,

Rao,

Irani

et al. 2023

IEEE Access

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Artificial intelligence and computer vision have widespread applications in workout analysis. It has been extensively used in sports and the athlete industry to identify errors and improve performance. Furthermore, these methods prevent injuries caused by a lack of instructors or costly infrastructure. One such exercise is the squat, which is a movement in which a standing person descends to a posture with their torso vertical and their knees firmly bent, then returns to their original upright position. Each person's squat is distinct, with varying limb lengths causing their form to change when observed. It has been observed that the mobility of various joints and muscular strength have a role in this. A squat improves the user by increasing overall leg strength, strengthening knee and hip joints, and lowering the risk of heart disease due to cardiovascular development. This paper presents a method for classifying squat types and recommending the right squat version. This study uses MediaPipe and a deep learning-based technique to decide if squatting is good or bad. A stacked Bidirectional Gated Recurrent Unit (Bi-GRU) model with an attention layer is proposed to consistently and fairly assess each user, categorizing squats into seven classes. This stacked Bi-GRU model with an attention unit is then compared to other cutting-edge models, both with and without the attention layer. The model outperforms other models by attaining an accuracy of 94% and is demonstrated to work the best and most consistently for our dataset. Furthermore, the individual executing the incorrect squat is corrected to the best of their ability, depending on their performance and body proportions, by providing the correct form.

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Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

Cited by 9 publications

References 22 publications

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion

PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition

AI Trainer: Autoencoder Based Approach for Squat Analysis and Correction

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