|Year : 2023 | Volume
| Issue : 1 | Page : 60-66
Exploring the deeper insights of Vrikshasana
D Mohan Kishore1, BR Divya2, Nandi Krishnamurthy Manjunath1
1 Division of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana, Bengaluru, Karnataka, India
2 Division of Yoga –Spirituality, Swami Vivekananda Yoga Anusandhana Samsthana, Bengaluru, Karnataka, India
|Date of Submission||10-Aug-2022|
|Date of Acceptance||22-Aug-2022|
|Date of Web Publication||03-Feb-2023|
Mr. D Mohan Kishore
Division of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana, Jigani, Bengaluru, Karnataka
Source of Support: None, Conflict of Interest: None
Vrikshasana is also known as a penance posture as it infuses a deep contemplative meditation effect and self discipline in its practitioner. This classical yoga pose is explored to gain a deeper insight into its origin, practice methods, biomechanics, and artificial intelligence (AI)-based learning in this study. Information related to Vrikshasana from classical yoga texts was considered to understand the symbolical interpretation and method of practice. To obtain the information about biomechanics and AI based learning of Vrikshasana scientific research studies from conference proceedings, published research papers, technical reports, and journals were explored. The practice of Vrikshasana infuses a strong sense of balance and postural stability while improving the strength and endurance in the lower extremity muscles. It helps in preventing the fear of fall that usually occurs as an aging problem. The anatomical and biomechanical insights provided in this study give a wide range of scope to the physiotherapists, yoga therapists, and fitness trainers in their approach to training this pose. The study also highlights the importance of the AI-based approach which promotes self-training to experience the positive benefits of pose practice and equally prevents any injury or the fear of falls.
Keywords: Artificial intelligence, biomechanics, standing asana, tree pose, Vrikshasana
|How to cite this article:|
Kishore D M, Divya B R, Manjunath NK. Exploring the deeper insights of Vrikshasana. J Appl Conscious Stud 2023;11:60-6
| Introduction|| |
In the modern context, yoga has been broadly understood as a set of principles and practices designed to promote health and well-being through the integration of body, breath, and mind (Hayes, 2010).
Many scientific studies have promoted yoga for its therapeutic value in reducing stress and anxiety and for improving autonomic functions (Sengupta, 2012) and considering it as an effective and supportive adjunct to mitigate some medical conditions (Büssing et al., 2012).
It has also been found that yoga can adversely affect the practitioner if wrongly performed (Cramer et al., 2019). Hence, in recent decades, there is a growing interest in the field of biomechanics and kinesiology of asanas. This has further led to the artificial technology-based yoga self-training methods (Kothari, 2020) offering an evidence-based yoga posture prescription that may help prevent the risk for musculoskeletal and neurological pain and injury (e.g., strains, sprains, and impingements) (Greendale et al., 2012).
Of the many known yoga asanas, the present study aims to explore a deeper insight into the Vrikshasana (tree pose) – a standing balancing posture. The key focus of this observational work is to explore the origin, the practice methods, the biomechanics, and the artificial intelligence (AI)-based learning of Vrikshasana.
In Sanskrit, “vrksa” means tree, and in yoga asana performance, it is known as standing single-leg balancing posture.
| Methodology|| |
The classical Hatha yoga texts are considered the source of studying the origin and symbolical interpretation Vrikshasana.
Keywords such as Vrikshasana, tree pose, biomechanics, artificial learning, yoga pose estimation scientific research studies from conference proceedings, published research papers, technical reports, and journals were explored.
| Origin and Symbolisms of Vrikshasana (Tree Pose)|| |
The Vrikshasana has been identified in Indian relics since at least the time of the Buddha (c. 4th to 5th Century, BCE) and old texts called Vedas (c. 1200 BCE). This posture as “standing one-legged balance” is part of the famous stone carving in the Mahabalipuram town. In some instances, this tree pose also known as “Tree of Life” is dedicated to the first “Human being” Manu and hence is also named “Manuasana” (Mayank, 2013). According to Indian mythology, Lord Ravana stood in this pose for months meditating on Lord Shiva to seek his Blessings (Javid & Javeed, 2008).
As mentioned by Kausthub Desikachar in some traditions, it is known as “Bhagirathasana” which represents the intense penance of Bhagiratha to Lord Shiva to bring the Ganges from Heaven to Earth (Bhagirathasana (Stiles, 2008)). Brihadratha Maurya, a king practiced this posture with Urdhva dhana (raised arms) for attaining the highest asceticism as mentioned in the Maitri Upanishad from the 2nd century. The method of performing this posture is described in the Gheranda Samhita (Chapter 2, verse 36)
Vrikshasana is the only standing stimulating pose found in the premodern texts and it is also compared to those of the karanas – vocabulary source from Indian classical dance (Bhavanani & Bhavanani, 2010).
The tree pose is also known as a “Penance Posture/tapakarasana” as many holy men would choose this for their practice of deep contemplation and self-discipline, because of the belief that the pose helps channel attention back to the source of creation (Mayank, 2013).
The practice of Vrikshasana induces a balance in prana, udana, and vyana vayus, thereby bringing a stability, flexibility, and integration to the body. The posture establishes good coordination between the muscles and the nervous systems. Vrikshasana prevents and cures neurological and degenerative disorders (Hebbar, Raghuram, & Manasa, 2019).
| Symbolism|| |
Tree is a long, firm, silent, and patient creature. It is believed that this balancing posture imbibes these qualities in its practitioner. Tree as a pose is depicted as a stretch between Heaven and the Earth which is themed for the promotion of connection, growth, and stability. Here, the growth lies in its opposed impulses. The symbolism of the tree calls us toward symbiotic participation in Earth's grand network of life. The power of the tree lies in its ability to integrate masculine and feminine: its upward growth is dependent upon its downward growth, and its downward growth is equally dependent upon its upward growth (Balakaran, 2022). In Hatha yoga, this balancing posture reflects the definition of the asana “Patanjali Yoga Sutras” according to Patanjali Yoga Sutra. The practitioner here balances himself on one leg and draws their attention toward their body, mind, and breath.
Since the growth of the tree is seen growing from root to upward, it symbolizes itself as the “Energy Ladder from Root chakra to the Crown Chakra.” Since it also grows from the Crown to Earth backward, it helps in the circulation of energy (prana) and builds a strong sense of balance in the body and mind (Mittag, 2019).
| Method of Performing as in Classical Texts|| |
Gheranda Samhita-Chapter 2, verse 36 says “Place the right foot at the top of the thigh and stand on the ground like a tree. This is called Vrikshasana” (Vasu, 1979).
There are different hand positions practiced in Vrikshasana each having a unique meaning. Hand position at the chest level means devotion to Guru. Hand position at the brow center means devotion to God and hands placed on the crown, over the head means devotion to the Formless Absolute (Aiyasamy, 2019).
| Anatomical Exploration of Vrikshasana|| |
The practice of Vrikshasana involves stretching of arms, back, and entire body while folding one of the legs and both hands with their pressed palms placed at the sternum. The pose not only stretches the entire body but also improves body balance, attention, and concentration. This is because of the involvement of the central nervous system (CNS) (Greendale et al., 2012; Kumari et al., 2018).
In summary, the spine is in a neutral position in Vrikshasana (as shown in [Figure 1]), where all three curves of the spine – cervical, thoracic, and lumbar – are present and aligned. The hip is abducted flexed and externally rotated. The knee is flexed, and the ankle is dorsiflexed. By balancing on one leg, the body improves stability and balance while strengthening the ligaments and tendons in the feet which is known to be helpful for conditions such as sciatica and back pain (Jadoun and Yadav, 2020).
| Studies on Biomechanics of Vrikshasana|| |
Biomechanics is defined as the study of how a force affects biological systems. It is classified as a division of kinesiology, the study of human movement (Mitchell, 2016). The progressive method of performing a tree pose from basic, intermediate, and advanced levels (without support) engenders significantly different joint moment of forces (JMOFs) and electromyogram (EMG) activity in different joints, planes of motion, and muscle groups [Table 1]. Among the different variations of the Vrikshasana, the classical/advanced pose without support generates the greatest support moment, which is calculated based on the sum of the individual extensor moments at the hip, knee, and ankle. This support moment is considered the greatest indicator of the overall demand for the lower extremity. Hip flexors (psoas major and iliacus) are important for stride length and walking speed, and in the sagittal plane, the tree pose with wall support generates the greatest flexor JMOF (Yu et al., 2012).
|Table 1: Description of the anatomy of muscles and ligaments of Vrikshasana (Swanson, 2019)|
Click here to view
In the tree pose when the pressure of the foot is exerted on the inner thigh, it causes the body reaction force to swing in the medial/lateral direction. Due to this, the body sways out of balance, and the accelerations of the center of gravity (CoG) peak result in the accelerations of the hip increasing to support the ankle. This states that the hip strategy dominates the ankle strategy in regaining this posture (Yelluru et al., 2016). Since the tendency of the body is to sway out of balance either in medial/lateral direction, CNS damage may find it difficult to perform Vrikshasana (McCollum and Leen, 1989).
The balance and control of the human body while performing the Vrikshasana is due to the collaborative efforts between two or more independent motor mechanisms. Since neural disorders in the cerebellum can influence conflicts in this kind of motor mechanism, especially in the case of elders, knee replacement patients, and stroke patients, these biomechanical insights can be used for pose modifications (Horak and Diener, 1994).
The CoG of the body in a normal stance lies at the center of the waist and its reaction force lies between the center of the feet when body weight is equally distributed. The Vrikshasana pose demanding to stand on one foot can cause greater instability making it difficult to stand still for a longer duration. In this pose, the practitioner uses the inverter activation to correct the angular velocity shifting the CoG laterally to the left when the body sways to the right. This activates the hip abductor/adductor to increase in synchronous with the ankle inverter activation. The body can be balanced only by activating the ankle joints to maintain a proper stance with a minimum amount of sway. However, if the body sway supersedes the control of the ankle joints, the hip gets activated as a corrective measure to regain a stable stance (Yelluru et al., 2016).
In conditions such as diabetic peripheral neuropathy, slow, gentle, balancing postures such as Vrikshasana improves both static and dynamic balance along with an improvement in lower extremity strength and reduction in the fear of fall (Kanjirathingal et al., 2021).
Yoga teachers often learn that the knee is a hinge joint, and based on such anatomical classification, they believe that the hinge operates in a single plane and any lateral pressure to it can be harmful as the case in understanding the tree pose. In actuality, the knee joint is a bicondylar joint that helps the knee to accommodate and mediate lateral and rotational forces.
An appreciable knee extensor JMOF is generated in all the progressive performances of the tree pose. This is due to the extended knee position associated with all three versions of the tree pose. This results in a ground reaction force projection that is close to the knee joint axis of the rotation (Kerrigan et al., 2003; Yu et al., 2012).
People with knee pathology should practice tree pose with support as high and/or sustained knee abductor JMOFs that will increase the loading of the compressional loading across the lateral condyles and lateral patellofemoral surfaces. These loading characteristics are associated with osteoarthritis (Hunt et al., 2006) and joint pain (Messier et al., 2005); thus, the intermediate and advanced tree poses could exacerbate preexisting conditions. Tree pose with support will not diminish the frontal plane JMOF or offer protection for the knee joint (Yu et al., 2012).
According to the biomechanics of performing a tree pose, the force on joints tends to stimulate and nourish them and hence one may choose to practice the tree pose with the opposite foot directly over the knee as in the stork test (Mitchell, 2016).
Hip abductor strength is targeted with exercise programs to improve balance and reduce fall risk. To reduce the frontal plane demands and gluteus medius activity associated with the advanced/classical tree pose, the tree poses with support were opted. However, the biomechanical evidence did not support this in one of the studies (Yu et al., 2012).
The surface EMG examining the ankle musculature of the anterior tibialis (TA) and gastrocnemius (GA) was found to be more active during Vrikshasana performance as compared to that in the thigh rectus femoris, and biceps femoris muscles which suggests that this practice can be useful in strengthening the TA and GA muscles to prevent the falls in the elderly population (Kathleen et al., 2019).
| Vrikshasana Through Machine Learning and Artificial Intelligence|| |
With aging, sedentary lifestyles, injuries, and accidents, human beings get prone to musculoskeletal problems. As observed in many research studies, yoga has been used to modify to accommodate a practitioner's capabilities and limitations (Yu et al., 2012). To experience the maximum positive result, it becomes utmost important to practice yoga poses correctly as any incorrect posture can either be unproductive or possibly detrimental. This has led to the necessity of either an instructor supervising the posture or an AI-based learning to identify the yoga poses for providing personalized feedback to help individuals improve their form (Kothari, 2020).
[Table 2] summarizes a few of the different robust recognition systems that have been constantly getting improved in yoga pose estimation and supporting deep learning.
|Table 2: Summary of the different robust recognition systems for yoga asanas (Mohammed, Garrapally, Manchala, Reddy, and Naligenti, 2016)|
Click here to view
Various machine learning and deep learning approaches accurately classify yoga poses on prerecorded videos and in real time. Various pose estimation and key point detection methods in detail explain different deep learning models used for pose classification.
In the AI/machine learning process of yoga, the yoga pose is classified into pose estimation, key point detection, and pose classification based on deep learning. Further the human pose estimation is understood through generative, discriminative (learning based and exemplar), and top down and bottom up approaches. The key point detection uses OpenPose, PoseNet, and PitPat. The classification of a pose with deep learning is through a multilayer perception, recurrent neural network (long short-term memory), and convolutional neural network (CNN).
In the machine learning of Vrikshasana (tree), the key points of the user such as eyes, ears, nose, and limbs are compared with the trained model. The yoga asana which will have the highest confidence will be displayed on the screen. As the user is performing Vrikshasana (tree), its correct posture along with the steps will be displayed so that it could assist the user in performing the yoga asana. The use of yoga posture estimation in fitness can help people avoid injuries and increase their effectiveness. State-of-the-art picture categorization algorithms like K-Nearest Neighbors (KNN) are put to the test in this system along with PoseNet. The results were quite impressive with an accuracy of 98.51%. The movement of the yoga asanas can be analyzed using video and image analysis to check their correctness. Model designs such as PoseNet and KNN Classifier are appropriate for the video-based analysis (Bhosale, Nandeshwar, Bale, and Sankhe, 2022).
| Summary and Conclusion|| |
Vrikshasana is a single-leg standing posture that stretches the entire body and improves the body and the breath for balance, attention, and concentration. Symbolically, as a “Penance Posture,” many holy men chose this for their practice of deep contemplation and self-discipline.
It is understood as the “Energy Ladder from Root chakra to the Crown Chakra,” infusing a strong sense of balance in body and mind.
The biomechanical insights provided in this study explain the mechanisms of balance and control of the human body while performing Vrikshasana and the collaborative effort of the involved motor mechanisms. It gives us an insight into how different disorders can influence conflict in these motor mechanisms and what makes a practitioner find difficulty in performing this pose. It has been observed that Vrikshasana improves both static and dynamic balance along with improvement in lower extremity strength and reduction in the fear of falls (Kanjirathingal et al., 2021). The anatomical explorations and studies of biomechanics of the Vrikshasana practice in different conditions give a better insight into the postural performance of Vrikshasana. This study enables clinicians, physical therapists, yoga therapists, and fitness trainers to bring out varied approaches, adaptations, and modifications that can be introduced while training the participants/patients in this practice.
For self-training-based learning, this study has summarized the different AI approaches for pose estimation. The key point detection methods applied in the AI/machine-based learning in the practicing of Vrikshasana ensure maximum positive benefits while equally preventing any injury or the fear of falls.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Balakaran, D. R. (2022). The Stories Behind the Poses
. London, United Kingdom: Leaping Hare Press.
Bhosale, V., Nandeshwar, P., Bale, A., & Sankhe, J. (2022). Yoga pose detection and correction using posenet and KNN. International Research Journal of Engineering and Technology, 9 (4)
Büssing, A., Michalsen, A., Khalsa, S. B. S., Telles, S., & Sherman, K. J. (2012). Effects of yoga on mental and physical health: A short summary of reviews. Evidence-Based Complementary and Alternative Medicine, 2012,
Chen, H. T., He, Y. Z., Hsu, C. C., Chou, C. L., Lee, S. Y., & Lin, B. S. P. (2014). Yoga posture recognition for self-training. In Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., & O'Connor, N., editors. MultiMedia Modeling.
Cham: Springer International Publishing.
Cramer, H., Quinker, D., Schumann, D., Wardle, J., Dobos, G., & Lauche, R. (2019). Adverse effects of yoga: A national cross-sectional survey. BMC Complementary and Alternative Medicine, 19 (1)
Greendale, G. A., Kazadi, L., Mazdyasni, S., Ramirez, E., Wang, M. Y., Yu, S. S., & Salem, G. (2012). Yoga Empowers seniors study (YESS): Design and Asana series. Journal of Yoga & Physical Therapy, 2 (1)
Hayes, M. (2010). Prescribing yoga. Primary Care: Clinics in Office Practice, 37 (1)
Horak, F., & Diener, H. (1994). Cerebellar control of postural scaling and central set in stance. Journal of Neurophysiology, 72
Hunt, M. A., Birmingham, T. B., Giffin, J. R., & Jenkyn, T. R. (2006). Associations among knee adduction moment, frontal plane ground reaction force, and lever arm during walking in patients with knee osteoarthritis. Journal of Biomechanics, 39 (12)
Jadoun, S., & Yadav, S. K. (2020). Anatomical exploration of Vrikshasana. International Journal of Trend in Scientific Research and Development, 4 (6)
Kanjirathingal, J. P., Mullerpatan, R. P., Nehete, G., & Raghuram, N. (2021). Effect of yogasana intervention on standing balance performance among people with diabetic peripheral neuropathy: A pilot study. International Journal of Yoga, 14 (1)
Kathleen, K. K., Giannico, K., LeLesnett, G., & Romano, A. (2019). A comparison of EMG output of four lower extremity muscles during selected yoga postures. Journal of Bodywork and Movement Therapies, 23 (2)
Kerrigan, D. C., Xenopoulos-Oddsson, A., Sullivan, M. J., Lelas, J., & Riley, P. O. (2003). Effect of a hip flexor- stretching program on gait in the elderly. Archives of Physical Medicine and Rehabilitation, 84 (1)
Kothari, Shruti. 2020. “Yoga Pose Classification Using Deep Learning.”
Master's Projects San José State University.
Bhagirathasana (Stiles, 2008) Stiles, M. (2008). Ayurvedic Yoga Therapy - Page 112. New Delhi, India: Lotus Press.
Kumari, K., Mishra, V., & Ojha, S. (2018). Comparative study on foot pressure distribution of vrikshasana between visual and non-visual yogic practice. Indian Journal of Physical Education, Sports Medicine & Exercise Science, 18 (2)
Luo, Z., Yang, W., Ding, Z. Q., Liu, L., Chen, M., Ye, S. H.,… Ling. (2011). “”'Left Arm up!' Interactive Yoga Training in Virtual Environment,.” In 2011 IEEE Virtual Reality Conference
, 2011, Virtual: ScholarBank@NUS Repository., 261-62.
McCollum, G., & Leen, T. (1989). Form and exploration of mechanical stability limits in erect stance. Journal of Motor Behavior, 21
Messier, S. P., DeVita, P., Cowan, R. E., Seay, J., Young, H. C., & Marsh, A. P. (2005). Do older adults with knee osteoarthritis place greater loads on the knee during gait? A preliminary study. Archives of Physical Medicine and Rehabilitation, 86 (4)
Mitchell, L. R. (2016). Biomechanics
. London, England Uinted Kingdom: Handspring Publishing.
Mohammed, S. W., Garrapally, V., Manchala, S., Reddy, S. N., & Naligenti, S. K. (2016). Recognition of yoga asana from real-time videos using blaze-pose. International Journal of Computing and Digital Systems, 5 (3)
Mohanty, A., Ahmed, A., Goswami, T., Das, A., Vaishnavi, P., & Sahay, R. R. (2017). Robust Pose Recognition Using Deep Learning,. In Proceedings of International Conference on Computer Vision and Image Processing.
, Roorkee: Springer, 93-105.
Patil, S., Pawar, A., Peshave, A., Ansari, A. N., & Navada, A. (2011). Yoga Tutor Visualization and Analysis Using Surf Algorithm.” In 2011 IEEE Control and System Graduate Research Colloquium,
Mumbai, India: IEEE, 43-46.
Roebuck, V. (2003). The Upanishads
. New York: Penguin Books India.
Sengupta, P. (2012). Health impacts of yoga and pranayama: A state-of-the-art review. International Journal of Preventive Medicine, 3 (7)
Simpson, D. (2021). The Truth of Yoga A Comprehensive Guide to Yoga's History,
Texts, Philosophy, and Practices. New York, United States: Farrar, Straus and Giroux.
Swanson, A. (2019). Science of Yoga
. New York: DK Publishing.
Aiyasamy, P. (2019). Science of Yoga – A Comprehensive Approach. Chennai, India: Notion Press.
Ananda Balayogi Bhavanani, D. B. (2001). Bharatnatyam and Yoga. Yoganjali Natyalayam, 1-17.
Toshev, A., & Szegedy, C. (2014). DeepPose: Human pose estimation via deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
. Columbus, OH, USA.
Mittag, M. (2019). Hatha Yoga: The Complete Book - Page 252. Aachen, Germany: Meyer and Meyer Sport.
Vasu, R. B. S. C. (1979). The Gheranda Samhita
. New Delhi: Sri Satguru Publications.
Javid, A., & Javeed, T. (2008). World Heritage Monuments and Related Edifices in India - Page 158. New York, United States: Algora.
Wu, B. S., Hsieh, C. C., & Lee, C. C. (2011). A distance computer vision assisted yoga learning system. Journal of Computers, 6 (11)
Yadav, S., Singh, A., Gupta, A., & Raheja, J. (2019). Real-time yoga recognition using deep learning. Neural Computing and Applications, 31
Yelluru, S., Shanbhag, R., & Omkar, S. (2016). Understanding vrikshasana using body mounted sensors: A statistical approach. International Journal of Yoga, 9 (1)
Yu, S. S. Y., Wang, M. Y., Samarawickrame, S., Hashish, R., Kazadi, L., Greendale, G. A., & Salem, G. J. (2012). The physical demands of the tree (Vriksasana) and one-leg balance (Utthita Hasta Padangusthasana) poses performed by seniors: A biomechanical examination. Evidence-Based Complementary and Alternative Medicine, 2012
[Table 1], [Table 2]