Asana Research Articles

Real-time yoga pose classification with 3-D pose estimation model with LSTM

Real-time yoga pose classification with 3-D pose estimation model with LSTM

Reducing Stress with Yoga: A Systematic Review Based on Multimodal Biosignals.

Stress is an enormous concern in our culture because it is the root cause of many health issues. Yoga asanas and mindfulness-based practices are becoming increasingly popular for stress management; nevertheless, the biological effect of these practices on stress reactivity is still a research domain. The purpose of this review is to emphasize various biosignals that reflect stress reduction through various yoga-based practices. A comprehensive synthesis of numerous prior investigations in the existing literature was conducted. These investigations undertook a thorough examination of numerous biosignals. Various features are extracted from these signals, which are further explored to reflect the effectiveness of yoga practice in stress reduction. The multifaceted character of stress and the extensive research undertaken in this field indicate that the proposed approach would rely on multiple modalities. The notable growth of the body of literature pertaining to prospective yoga processes is deserving of attention; nonetheless, there exists a scarcity of research undertaken on these mechanisms. Hence, it is recommended that future studies adopt more stringent yoga methods and ensure the incorporation of suitable participant cohorts.

The Healing Potential of Nidra in Ayurvedic Medicine: A Comprehensive Overview

Nidra is considered an essential aspect of maintaining overall health and well-being according to Ayurveda, the ancient Indian system of medicine. The article delves into the Ayurvedic principles and philosophies that underpin the significance of Nidra in promoting physical, mental, and emotional healing. Ayurvedic understanding of sleep and its relationship with the body's Doshas (energetic forces) - Vata, Pitta, and Kapha. It highlights how an imbalance in these Doshas can disrupt the quality and duration of sleep, leading to various health issues. Additionally, the article explores the concept of "Sukha Nidra" (restful sleep) as an integral part of Ayurvedic daily routines (dinacharya) and its impact on overall vitality. Therapeutic benefits of Nidra in addressing specific health concerns, such as stress, anxiety, insomnia, and fatigue. It explores the role of Nidra in promoting cellular rejuvenation, hormonal balance, and the restoration of the body's natural healing mechanisms. Ayurvedic practices, such as the use of herbs, oils, and lifestyle modifications, to enhance the quality of sleep are also discussed. Ayurvedic techniques and practices that can optimize Nidra and facilitate deeper, more restorative sleep. These include meditation, Pranayama (breathing exercises), yoga asanas (postures), and the adoption of a balanced Ayurvedic diet. The article emphasizes the holistic nature of Nidra, highlighting its ability to address imbalances on physical, mental, and spiritual levels. Overall, this comprehensive overview aims to shed light on the healing potential of Nidra, providing valuable insights for individuals seeking natural and holistic approaches to improve sleep quality and overall well-being.

Comparative Analysis of Heart Rate Variability Parameters between Surya Namaskar and Stationary Bike Exercise Groups.

Yoga practice has multiple health benefits. Surya Namaskar (SN) also known as sun salutation presents mental and physical health benefits equivalent to physical exercise. It consists of a sequence of 12 Hatha yoga postures with coordinated breathing. This study evaluated the effect of SN against mild intensity stationary cycling exercise (SCE) on parameters of heart rate variability (HRV). This study was conducted in the department of physiology of a nationally important institute after obtaining ethics approval. A total of 40 healthy participants (males aged 46 ± 2.3 years) were enrolled in the study and divided into two groups, namely SN group and exercise group (SG and EG). The allocation on the group was based on subjects' choice and interest in performing SN or SCE. Baseline HRV, anthropometric measurement, and blood pressure were assessed. SG was subjected to 14 rounds of SN in about 20 min and EG to a 20-min mild-intensity stationary cycling session (30%-50% maximal heart rate). Assessments were conducted preexercise and postexercise recovery. HRV was assessed with the NeuralChek device. SN led to increased SDNN, pNN50, RMSSD, and total power, indicating improved autonomic balance, cardiovascular health, and parasympathetic dominance. Conversely, SCE decreased SDNN, pNN50, and RMSSD, suggesting immediate postexercise sympathetic dominance. Spectral analysis of HRV highlighted autonomic balance differences, with SN increasing low frequency (LF%) reflecting both sympathetic and parasympathetic dominance, whereas exercise reduced LF% due to sympathetic activation. Minimal changes were observed in high-frequency percentage. SN combines flexibility, strength, and balance, providing a balanced physical activity. This balance results in a mixed autonomic response, with sympathetic activity for the physical effort and parasympathetic activity for the relaxation components. SN positively impacted HRV, promoting parasympathetic dominance and cardiovascular health, whereas exercise induced sympathetic activation with potential recovery benefits. Monitoring HRV offers insights into cardiovascular fitness and well-being. The study underscores the merits of incorporating yoga such as SN into daily activity routines.

YAP_LSTM: yoga asana prediction using pose estimation and long short-term memory

YAP_LSTM: yoga asana prediction using pose estimation and long short-term memory

Importance of Yoga Asana

In routine, yoga is an exercise system comprised of eight levels of accomplishment in the areas of physical, mental, social, and spiritual intelligence. When we are physiologically healthy, our minds are clear, focused, and under control of stress. When you are healthy, you have a deeper connection with your inner self, with others, and with your surroundings, which contributes to your spiritual intelligence. "Yoga" is a Sanskrit word that means "to join, to unite." Yoga exercises have a holistic and natural effect, balancing the body, mind, consciousness, and spirit. Yoga asanas are more efficient and advantageous for humans. Yogic actions are simple, which are beneficial for the body's organs, eliminate body rigidity, and make our bodies flexible. It revitalises the body and eliminates maladies related to the body. Yoga is not merely an exercise; it is a way of life that strives for 'a healthy mind in a healthy body' The human body is a tool that is based on the secular and spiritual actions of the soul; therefore, it is necessary to use it correctly for the advancement of our selves; otherwise, problems in the human body may impede spiritual advancement. Yoga influences both the parasympathetic and sympathetic nervous systems. Asana is most essential for physical, mental, and spiritual rehabilitation functions. Yoga facilitates adaptation at the physical, psychological, and social levels of an individual's functioning. Asana eliminates maladies of the body and mental transgressions.

An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation

In this paper, SEPAM_HRNet, a high-resolution pose-estimation model that incorporates the squeeze-and-excitation and pixel-attention-mask (SEPAM) module is proposed. Feature pyramid extraction, channel attention, and pixel-attention masks are integrated into the SEPAM module, resulting in improved model performance. The construction of the model involves replacing ordinary convolutions with the plug-and-play SEPAM module, which leads to the creation of the SEPAMneck module and SEPAMblock module. To evaluate the model’s performance, the YOGA2022 human yoga poses teaching dataset is presented. This dataset comprises 15,350 images that capture ten basic yoga pose types—Warrior I Pose, Warrior II Pose, Bridge Pose, Downward Dog Pose, Flat Pose, Inclined Plank Pose, Seated Pose, Triangle Pose, Phantom Chair Pose, and Goddess Pose—with a total of five participants. The YOGA2022 dataset serves as a benchmark for evaluating the accuracy of the human pose-estimation model. The experimental results demonstrated that the SEPAM_HRNet model achieved improved accuracy in predicting human keypoints on both the common objects in context (COCO) calibration set and the YOGA2022 calibration set, compared to other state-of-the-art human pose-estimation models with the same image resolution and environment configuration. These findings emphasize the superior performance of the SEPAM_HRNet model.

Deep Learning-Based Yoga Posture Specification Using OpenCV and Media Pipe

Yoga is a years discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and wellbeing. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit from our strategy. Using the web cameras in our system, the model categorizes the yoga poses, and the image is used as input. However, the media pipe library first skeletonizes that image. Utilizing a variety of deep learning models, the input obtained from the yoga postures is improved to improve the asana. On non-skeleton photos, VGG16, InceptionV3, NASNetMobile, YogaConvo2d, and also InceptionResNetV2 came in the order of highest validation accuracy. The proposed model YogaConvo2d with skeletal pictures, which is followed by VGG16, reports validation accuracy in contrast, NASNetMobile, InceptionV3, and InceptionResNetV2.

RETRACTED ARTICLE: Segmentation quality assessment network-based object detection and optimized CNN with transfer learning for yoga pose classification for health care

RETRACTED ARTICLE: Segmentation quality assessment network-based object detection and optimized CNN with transfer learning for yoga pose classification for health care

AYURVEDIC INSIGHTS: UNDERSTANDING THE PATHOGENESIS AND NATURAL MANAGEMENT OF HYPERTENSION

Hypertension, or high blood pressure, is a prevalent health concern worldwide. As medical advancements con-tinue, alternative systems of medicine like Ayurveda offer valuable insights into understanding and managing this condition. This article provides an in-depth exploration of the intersection between hypertension and Ayur-veda, emphasizing the holistic approach of this ancient healing system. The article begins by elucidating the pathogenesis of hypertension according to Ayurveda's Shat Kriya Kala, which encompasses six stages of disease progression. It delves into how doshic imbalances, particularly Pitta and Vata, accumulate and disrupt the cardi-ovascular system, leading to increased blood pressure. Furthermore, the article offers a comprehensive treatment protocol derived from Ayurvedic principles. Lifestyle modifications, such as regular exercise, balanced routines, stress management, and adequate sleep, play a crucial role in managing hypertension. Dietary recommendations focus on reducing salt intake, incorporating fresh fruits and vegetables, and including healthy fats. Herbal reme-dies like Ashwagandha, Arjuna, and Brahmi, known for their beneficial effects on blood pressure regulation, are discussed. Stress management techniques, including yoga asanas, pranayama, and meditation, are highlighted as effective tools for managing hypertension. Additionally, the article emphasizes the importance of personalized treatment and the role of qualified Ayurvedic practitioners in guiding individuals toward tailored interventions. It also suggests considering therapies like Panchakarma to detoxify the body and restore doshic equilibrium. By embracing Ayurveda's holistic approach, individuals can gain a deeper understanding of hypertension and ex-plore natural methods to restore balance and promote optimal blood pressure levels. Integrating the wisdom of Ayurveda with modern medical knowledge offers a comprehensive and personalized approach to hypertension management, contributing to overall well-being and vitality.

Exploring the uses of yoga nidra: An integrative review.

The purpose of this paper was to review and synthesize published research articles that have utilized yoga nidra as an intervention. Yoga nidra is a form of guided meditation that has emerged in the literature in the past two decades as an intervention for a variety of medical conditions such as stress and mental health. It differs from traditional yoga, in that it does not require yoga poses. It is a noninvasive, cost-effective approach that is also easily accessible so it can be done in the privacy and comfort of the home. The integrative review methodology by Whittemore and Knafl (2005) provided the framework for this review. The databases CINAHL, PubMed, SCOPUS, and PsycINFO were used to search for articles. Inclusion criteria consisted of journal articles in English with no limitations on dates of publication. Studies were excluded if any form of traditional yoga requiring poses was used as an intervention. Also excluded were all types of meditation that were not yoga nidra, systematic reviews, studies that utilized multiple intervention types (i.e., traditional yoga and yoga nidra), and commentaries/brief reports. Twenty-nine studies met the inclusion criteria. Quality appraisal was completed for each study. The 29 studies that were reviewed consisted of 12 randomized controlled trials, 13 quasi-experimental studies, 3 mixed-methods studies, and 1 qualitative study. Outcome variables were categorized according to themes and results were systemically synthesized and reported by theme: (a) stress, (b) mood, (c) well-being, (d) psychologic dysfunction, (e) biomarkers, (f) sleep, and (g) miscellaneous. Yoga nidra was found to be effective in most of these studies. However, there was some clinical heterogeneity in the sample populations and intervention session lengths, frequencies, and durations, making it difficult to draw conclusions about yoga nidra intervention based solely on the findings presented in this review. More studies are needed overall, particularly ones with larger sample sizes and stronger experimental designs. Yoga nidra has the potential to be a useful, noninvasive, nonpharmacologic treatment or adjunct for a variety of conditions, particularly mental health.

Yoga Posture Recognition by Learning Spatial-Temporal Feature with Deep Learning Techniques

Yoga posture recognition remains a difficult issue because of crowded backgrounds, varied settings, occlusions, viewpoint alterations, and camera motions, despite recent promising advances in deep learning. In this paper, the method for accurately detecting various yoga poses using DL (Deep Learning) algorithms is provided. Using a standard RGB camera, six yoga poses — Sukhasana, Kakasana, Naukasana, Dhanurasana, Tadasana, and Vrikshasana — were captured on ten people, five men and five women. In this study, a brand-new DL model is presented for representing the spatio-temporal (ST) variation of skeleton-based yoga poses in movies. It is advised to use a variety of representation learners to pry video-level temporal recordings, which combine spatio-temporal sampling with long-range time mastering to produce a successful and effective training approach. A novel feature extraction method using Open Pose is described, together with a DenceBi-directional LSTM network to represent spatial-temporal links in both the forward and backward directions. This will increase the efficacy and consistency of modeling long-range action detection. To improve temporal pattern modeling capability, they are stacked and combined with dense skip connections. To improve performance, two modalities from look and motion are fused with a fusion module and compared to other deep learning models are LSTMs including LSTM, Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time datasets of yoga poses show that the suggested DenseBi-LSTM model performs better and yields better results than state-of-the-art techniques for yoga pose detection.

YOGA POSE DETECTION USING OPENPOSE AND MEDIAPIPE

An abstract i.e. idea behind this yoga pose detection project using deep learning or neural network learning is that yoga popularity is increasing day by day because of its benefits. Doing yoga helps us physically, mentally as well as spiritually. Because of this many people nowadays are doing it regularly. The main idea of this project is to help the people to recognize which yoga pose they are doing with the help of this detection technique. Yoga which involves 8 rungs and limbs of it, which includes Yama, Niyama, Asana, Pranayama, Dharana, Dhyana and Samadhi. To easily help people understand which pose they are performing via images, video recording by classifying it, we are implementing this project because of this people will incline towards doing more as they will get help to identify which pose they are doing very easily. Key Words: CNN, Deep learning, Feature Extraction,Yoga Pose Detection, openPose , mediaPipe

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

SVM-based Real-time Pose Detection and Correction System refer to a computer system that uses machine learning techniques to detect and correct a person's yoga pose in real-time. This system can act as a virtual yoga assistant, helping people improve their yoga practice by providing immediate feedback on their form and helping to prevent injury. This paper presents a yoga tracker and correction system that uses computer vision and machine learning algorithms to track and correct yoga poses. The system comprises a camera and a computer vision module that captures images of the yoga practitioner and identifies the poses being performed. The machine learning module analyzes the images to provide feedback on the quality of the poses and recommends corrections to improve form and prevent injuries. This paper proposed a customized support vector machine (SVM) based real-time pose detection and correction system that suggests yoga practices based on specific health conditions or diseases. Paper aims to provide a reliable and accessible resource for individuals seeking to use yoga as a complementary approach to managing their health conditions. The system also includes a practitioner’s interface that enables practitioners to receive personalized recommendations for their yoga practice. The system is developed using Python and several open-source libraries, and was tested on a dataset of yoga poses. The hyper parameter gamma tuned to optimize the classification accuracy on our dataset produced 87% which is better than other approaches. The experiment results demonstrate the effectiveness of the system in tracking and correcting yoga poses, and its potential to enhance the quality of yoga practice.

Yoga Pose Rating using Pose Estimation and Cosine Similarity

One type of exercise that many people do today is yoga. However, doing yoga yourself without an instructor carries a risk of injury if not done correctly. This research proposes an application in the form of a website that can assess the accuracy of a person's yoga position, by using ResNet for pose estimation and cosine similarity for calculating the similarity of positions. The application will recognize a person's body pose and then compare it with the poses of professionals so that the accuracy of their position can be assessed. There are three types of datasets used, the first is the COCO dataset to train a pose estimation model so that it can recognize someone's pose, the second is a reference dataset that contains yoga poses performed by professionals, and the third is a dataset that contains pictures of yoga poses that are considered correct. There are 9 yoga poses used, namely Child's Pose, Swimmers, Downdog, Chair Pose, Crescent Lunge, Planks, Side Plank, Low Cobra, Namaste. The optimal pose estimation model has a precision value of 87% and a recall of 88.2%. The model was obtained using the Adam optimizer, 30 epochs, and a learning rate of 0.0001.

IYogacare: Real-Time Yoga Recognition and Self-Correction for Smart Healthcare

Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistances are implemented in yoga pose identification. In this work, a self-assistance-based yoga posture identification technique is developed, which helps users to perform Yoga with the cor rection feature in Real-time. The work also presents Yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed which include 10 Yoga postures with around 400-900 images of each pose and also contain 5 mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and a hand for mudra poses. Two different algorithms have been used for creating a skeleton one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as features for different machine learning and deep learning models. among all the models XGBoost with RandomSearch CV is the most accurate and gives 99.2% accuracy. The complete design framework is described in the present paper.

A quality improvement study on the feasibility and potential benefits of a yogic breathing program for cancer survivors and caregivers during treatment in a lodging facility

BackgroundComplementary and integrative health approaches with a focus on relieving side effects of cancer treatment are popular among cancer patients. Previous studies have investigated the combined effects of yoga postures, breathing, and meditation, but the specific effects of the breathing component are under-reported. Our previous studies indicate that yogic breathing can improve salivary biomarker expression related to stress, immune response, and tumor suppression. We aim to assess the acceptability and feasibility of a yogic breathing program in cancer patients and caregivers during the treatment period. MethodsIn this quality improvement study, we designed a 20-minute yogic breathing regimen and introduced them to all-site cancer patients and their caregivers during the cancer treatment period at a lodging facility, Hope Lodge in Charleston, SC. All interested participants were included as there were no eligibility criteria set for the study. The availability of the class was advertised via intercom, displays, and word of mouth. Participants were taught five different breathing exercises, and after completion of the exercises in a single session, a self-reported quality improvement questionnaire was administered assessing sociodemographic/clinical factors, expectations about the session, and ratings of satisfaction with the session. ResultsDuring the nine months of the data collection period, 52 participants provided feedback of which patients and caregivers were almost equal numbers. Participants’ perception of intervention acceptance, symptom management, satisfaction with the sessions, and future needs for practice indicate that the yogic breathing sessions help improve some of the key symptoms of cancer experience such as stress. ConclusionFindings indicate that yogic breathing is acceptable to patients and caregivers and may help alleviate some of the side effects resulting from cancer treatment, and the intervention is feasible at lodging facilities during treatment. Currently, the yogic breathing sessions are conducted on a weekly basis by Hope Lodge volunteers trained by the study team.

Tratak and its benefits: A Review

At the present time several Yogic practices, meditation, Pranayama, and Mantra, etc. are playing important role in treating physiological and psychological problems. The ancient Indian Yoga text, Hatha Yoga Pradipika, describes six cleansing techniques. The objective of cleansing techniques is to purify and prepare the body for the practice of yoga postures, and meditation. Trataka (Yogic visual concentration technique) is one of these techniques. In this study, ancient texts and contemporary research findings are analysed to gain a better understanding of Trataka.

Electromyographic analysis of trunk and hip muscles during Yoga poses prescribed for treating chronic low back pain

Yoga is effective for the management of chronic low back pain as it improves muscle strength and endurance. The objective of the current study was to assess trunk and hip muscle activation during Yoga poses usually prescribed for patients with chronic LBP. The study included 22 healthy Yoga trained subjects (mean age: 24.4 ± 2.6 years; 16 females, 6 males). The testing involved collecting surface electromyography data from Rectus Abdominis (RA) and Transverse Abdominis (TA), Gluteus Medius (GM), and Erector Spinae (ES) as subjects attained and held 16 different Yoga poses in standing, kneeling, supine, or prone positions in random order. The signal of each muscle was processed and normalized to its maximum voluntary isometric contraction (MVC). Statistical comparisons were made across selected poses and phases (attaining and holding) for each muscle using repeated-measures ANOVA. The data was also descriptively analyzed for sorting muscle activity. The activation of trunk flexors was significantly higher during boat pose (>50% MVC) followed by plank pose (∼30% MVC), activation of ES was significantly higher during reverse boat (41.7% ± 3.3 MVC) as compared to bow, snake, backward-sway, and warrior poses. The GM activation was significantly less in standing poses than during side-lying and Tiger poses (32–42% MVC). The cat-camel, kneeling camel, downward dog, backward-sway, swaying-palm tree, and warrior poses activated all tested muscles fairly (<20% MVC). The study helps the grading of Yoga positions according to the challenge imposed. The challenging poses may be used to develop graded rehabilitation programs to improve muscle strength/endurance.

Yoga Pose Trainer and Analyser: Machine Learning (TensorFlow and Open CV)

The "Yoga Pose Trainer and Analyzer" is a sophisticated software system designed to enhance the yoga experience for practitioners of all levels. This project combines computer vision, machine learning, and user-friendly interfaces to provide valuable insights and guidance during yoga sessions. Users can interact with the system, receiving real-time feedback on their yoga poses and personalized recommendations to improve their practice. The core functionalities of the system include pose recognition and classification. Using advanced computer vision techniques, the system identifies key points on the user's body and classifies their pose, accurately determining which yoga asana (pose) they are performing. Additionally, users can specify their goals or desired poses, and the system tailors their practice accordingly. The software provides a comprehensive library of yoga poses, offering detailed instructions and highlighting the benefits of each pose. Users can track their progress over time, set goals, and receive guidance on how to achieve their objectives. Safety and privacy considerations are paramount, with the system promoting proper form to prevent injury and ensuring user data security.