Human Activity Recognition Using Machine Learning: A Review

Biswal, Ankita; Nanda, Sarmistha; Panigrahi, Chhabi Rani; Cowlessur, Sanjeev K.; Pati, Bibudhendu

doi:10.1007/978-981-33-4299-6_27

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…There have been several reviews published in the area of human activity recognition in vision [25], [26], [27], [28], [29], [30], [31], sensor [32], [33], [34], [35], [36], [37], machine learning [38], [39], [40], [41], [42], [43], and deep learning-based methodologies [44], [45], [46], [47], [48], [49], [50], [51]. Nevertheless, there needs to be a survey that focuses specifically on yogic posture recognition.…”

Section: The Role Of Computer Vision In Yogamentioning

confidence: 99%

A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

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Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson's disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.INDEX TERMS Yogic posture recognition, optimal posture, machine learning methods, deep learning methods.

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Section: The Role Of Computer Vision In Yogamentioning

confidence: 99%

A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

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“…The suggested method is particularly beneficial for monitoring individuals in lowlight situations where traditional RGB cameras are unable to provide visually detectable images. There are also survey and review papers for recognition of different activity types in the literature [15]. Dhiman and Vishwakarma [16] designed a two-stream view-invariant deep framework for human action recognition using motion stream and spatial-temporal dynamic stream.…”

Section: Related Work a Pose Estimation For Adultsmentioning

confidence: 99%

BabyPose: Real-Time Decoding of Baby’s Non-Verbal Communication Using 2D Video-Based Pose Estimation

Yurtsever

Eken

2022

IEEE Sensors J.

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People use various communication methods to interact with each other, such as speech and body movements. However, communication methods in babies differ according to their developmental periods. Babies may not be able to express themselves verbally, but they do have their own communication methods. Naturally, it becomes important for parents to understand these signs/poses. Human pose estimation is extensively employed in different applications such as video surveillance, sports analysis and medical support/aid. The goal of this research is to assist new parents by addressing pose-based real-time body movements of babies (such as arching back, head banging, kicking legs, rubbing eyes, stretching, sucking fingers) and making sense of their activities. This is the first study to estimate poses on babies. In this paper, a baby pose dataset is created from 156 video clips through online video sharing platforms. Key-points are obtained from three different pose estimators -OpenPose, AlphaPose, and KAPAO. Different LSTM models are used to recognize the babies' activities and different performance metrics are used to compare these models. The best model has 99% accuracy and 0.0712 loss ratio. Also, babies are tracked in real-time via DeepSORT algorithm. Experimental results show that the proposed system is very promising and filling a gap in making sense of baby poses and monitoring them.

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“…It plays a crucial role in various domains, including healthcare [3][4][5], sports [6,7], workout exercises [8][9][10][11][12][13][14], security [15], and human-computer interaction [16]. HAR methods can be categorized into two primary groups [10,[17][18][19][20][21]: sensor-based and vision-based. Using cameras situated in the human environment, utilizing the vision-based method, human activity features are extracted from images and video streams [22].…”

Section: Introductionmentioning

confidence: 99%

“…Using cameras situated in the human environment, utilizing the vision-based method, human activity features are extracted from images and video streams [22]. In the sensor-based method, data is gathered using sensors like accelerometers, gyroscopes, and magnetometers [20]. Sensors, unlike the equipment required for vision-based methods, are lightweight and portable [22].…”

Section: Introductionmentioning

confidence: 99%

Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy

Vazan,

Sharifi,

Farahani

et al. 2024

Preprint

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This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

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Human Activity Recognition Using Machine Learning: A Review

Cited by 14 publications

References 29 publications

A Survey on Yogic Posture Recognition

A Survey on Yogic Posture Recognition

BabyPose: Real-Time Decoding of Baby’s Non-Verbal Communication Using 2D Video-Based Pose Estimation

Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy

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