Biomechanical Evaluation of Joint Torque and Muscle Activation Parameters for Single Leg Squat Activity

Research Article

Ann Yoga Phys Ther. 2017; 2(3): 1031.

Biomechanical Evaluation of Joint Torque and Muscle Activation Parameters for Single Leg Squat Activity

Surya Kiran K*, Arjun R, Govinda A and Omkar SN

Department of Aerospace Engineering, Indian Institute of Science, India

*Corresponding author: Surya Kiran K, Department of Aerospace Engineering, Indian Institute of Science, India

Received: June 30, 2017; Accepted: August 17, 2017; Published: August 24, 2017


Background: Yoga, originated in ancient India, is a spiritual and ascetic discipline, a part of which, including variety of aasanas, and other yogic techniques through adoption of specific bodily postures, widely practiced for health and relaxation. �Eka Paada Kaakasana� or Single leg squat activity is one such aasana in yoga that helps in maintaining the lower extremity antigravity muscles. It is a dynamic posture in which muscles and joints of the lower human body are actively used to perform the activity. Hence, it is necessary to understand the muscle activation patterns and the joint mechanical parameters of the body.

Aim: The purpose of this study is to understand human body dynamics for �Eka Paada Kaakasana� or Single leg Squat activity. Human motion capture data is recorded through experimentation using inertial measuring units. Analyzing the activity, the moments/torque of the joints and muscle activation pattern of the lower human body (Ankle, Knee and Hip) is obtained.

Materials and Methods: x-IMUs, a combination of 3 accelerometers, magnetometers and gyroscopes is used to measure the angular displacement and position in 3D plane of different segments of the lower body. LifeMOD�, virtual human simulation software is used to simulate the motion. A mathematical model is formulated in Matlab to validate the results.

Results and Conclusion: The results obtained in the form of joint torques in various lower body is validated using mathematical model. Subsequently the major skeletal muscles actively participating the activity are studied using muscle activation patterns.

Keywords: Lagrangian dynamics; Muscle activation; Eka paada kaakasana; Inertial measurement unit; LifeMOD™


Human body is a complex mechanical system capable of performing a wide variety of activities. Technological advancements such as optical sensor human motion tracking and body mounted measurement sensors integrated with a suitable virtual simulation models has helped us understand the biomechanical system satisfactorily [1]. Angular displacement, angular velocity and angular acceleration are a few biomechanical parameters to mention that helps us to understand the activity that takes place in various human segments during a motion [2]. These kinematic data parameters are basic data set for all inverse dynamic calculations and by knowing them, together with some basic anthropometry data, force, work, moment and other parameters can be calculated.

Yoga is practiced worldwide and providing a scientific base of approach and quantifying various biomechanical parameters becomes necessary to understand yoga and train better. Squatting is one exercise that is important component of athlete training [3]. �Eka Paada Kaakasana� or Single leg squat (Figure 1), a dynamic yoga posture is studied in this project.

Steps to follow to perform this aasana:

Optical sensor human motion tracking is one of the most well known ways of human motion tracking. This method of tracking has a number of limitations. A specified software and hardware is required to carry out this process. A large space is required to carry out experimentation using this process [4,5]. Taking all these limitations into consideration, a more cost effective and efficient way of human tracking using inertial based motion sensors is adapted in this project [5].

These results are in turn validated using a mathematical model formulated using Lagrangian Dynamics. The main advantage of Lagrangian mechanics is that we don�t have to consider the forces of constraints and given the total kinetic and potential energies of the system we can choose some generalized coordinates and blindly calculate the equation of motions totally analytically unlike Newtonian case where one has to consider the constraints and the geometrical nature of the system. The Lagrangian dynamic equations used in this study are obtained from [11,12], which are used widely for similar kind of validations.

Muscle Activation is movement of muscle fibres in response to force or load. This is an important aspect of study, since it helps us understand the way the muscle functions while performing an activity. A study is carried out on muscle activation while performing pull ups, high speed and, low speed yoga and gait using surface EMG [6-8]. Limited studies are carried out on this basis to understand yoga which is one of the motivations to carry out this project.

Surface Electromyography (EMG) is an electro diagnostic medicine technique for evaluating and recording the electrical activity produced by skeletal muscles [9]. Surface EMG can have limited applications due to inherent problems associated with it. Adipose tissue (fat) can affect EMG recordings. As adipose tissue increases, the amplitude of the surface EMG signal directly above the centre of the active muscle decreases. EMG signal recordings are typically more accurate with individuals who have lower body fat, and more compliant skin, such as young people when compared to old. Muscle cross talk occurs when the EMG signal from one muscle interferes with that of another limiting reliability of the signal of the muscle being tested. Surface EMG is limited due to lack of deep muscles reliability. Deep muscles require intramuscular wires that are intrusive and painful in order to achieve an EMG signal. Surface EMG can only measure superficial muscles and even then it is hard to narrow down the signal to a single muscle [10]. Life MOD� is one such software platform that helps us overcome these limitations and help us obtain muscle activation without any difficulties.

Postural/antigravity muscles [10] are the group of muscles that are primely studied in this paper to understand its activity while performing this aasana and in turn to gain knowledge and benefits of training with this posture for rehabilitation and various other purposes.

Experimentation and Methods Followed

Data recording

The test consisted of one individual, weight 65 kg, height 165cm; subject was healthy, without any acute or chronic problems of the musculoskeletal system. From the case history of the subject no neurological, visual or vestibular deficits were found. The test individual was fitted with 5 x-IMUs, one each on upper torso, right thigh, and shank and left thigh and shank. Initial calibrations were made in the x-IMUs using the GUI provided. The individual was trained to perform �Eka Paada Kaakasana� a number of times to make him familiar with the way the measurement unit works. Then the data was recorded in the x-IMUs at 128Hz with a Butterworth filter of order 6.

The x-IMU was designed to be the most versatile Inertial Measurement Unit (IMU) and Attitude Heading Reference System (AHRS) platform available. Its host of on-board sensors, algorithms, configureurable auxiliary port and real-time communication via USB, Bluetooth or UART make it both a powerful sensor and controller. The on-board SD card, battery charger (via USB), real-time clock/ calendar and motion trigger wake up also make the x-IMU an ideal standalone data logger.

The x-IMU GUI can be used to configureure settings, view real-time sensor data, perform calibration and export data to user software; e.g. Microsoft Excel. The x-IMU MATLAB library provides all the tools required to import, organize and plot data in MATLAB. User software can be developed using the x-IMU API.

Quaternion and Euler angles are obtained from the x-IMUs. The quaternion obtained is converted into (x,y,z) co-ordinates data to obtain the position of the segments in 3D plane.

Mathematical modeling

Lagrangian dynamics is a base for the mathematical model created in our project. The equations of motion were derived from the basic equation for a better understanding of what are the important governing parameters in the equation. In performing this yoga posture, 2 different set of equations comes into picture as one leg is in contact with the ground throughout the posture and the other is lifted above the ground. For mathematical formulation one leg is considered to be fixed at the ground and the other to be fixed at the hip joint. In this experimentation left leg is considered to be in contact with the ground and right leg free.









  1. Gururaja D, Harano K, Toyotake I, Kobayashi H. Effect of yoga on mental health: Comparative study between young and senior subjects in Japan. Int J Yoga. 2011; 4: 7-12.
  2. Büssing A, Michalsen A, Khalsa SBS, Telles S, Sherman KJ. Effects of Yoga on Mental and Physical Health. Evidence-Based Complementary and Alternative Medicine. 2012; 165410: 7.
  3. Balasubramaniam M, Telles S, Doraiswamy PM. Yoga on our minds: a systematic review of yoga for neuropsychiatric disorders. Front Psychiatry. 2013; 3: 117.
  4. Hagen I, Nayar US. Yoga for children and young people’s mental health and well-being: research review and reflections on the mental health potentials of yoga. Front Psychiatry. 2014; 5: 35.
  5. Kumar Taneja D. Yoga and Health. Indian J Community Med. 2014; 39: 68- 72.
  6. Chu AHY, Koh D, Moy FM, Müller-Riemenschneider F. Do workplace physical activity interventions improve mental health outcomes? Occup Med. 2014; 64: 235-245.
  7. Singh, Amarendra N. Application of Yoga Therapy to Psychosamatic Disorders. International Medical Journal. 2015; 22: 277-282.
  8. Arun Kumar N. Effects of yoga on mental and physical health. IJPESH. 2016; 3: 268-270.
  9. Raja S. How Yoga Helps to Children to Avoid Stress. Research and Reviews: J Nurs Health Sci. 2016; 2.
  10. Kusuma A. Yoga-Meditation and positive mental health. IJHS. 2016; 4.
  11. Varambally S, Gangadhar BN. Current status of yoga in mental health services. Int Rev Psychiatry. 2016; 28: 233-235.
  12. Davari F, Sadiri N, Shariat A, Kim Geok S. Yoga for mental health of children. RPF. 2016; 4.
  13. Goyal M, Singh S, Sibinga EM, Gould NF, Rowland-Seymour A, Sharma R, et al. Meditation programs for psychological stress and well-being. JAMA Intern Med. 2014; 174: 357-368.
  14. Meena R, Ananda Balayogi B, Madanmohan T. Effect of a 12-week yoga therapy program on mental health status in elderly women inmates of a hospice. IJOY. 2017; 10: 24-28.

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Citation:Balaji Deekshitulu PV. Yoga on Mental and Physical Health. Ann Yoga Phys Ther. 2017; 2(3): 1030.

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