Robust Pose Recognition Using Deep Learning

Mohanty, Amiya Ranjan; Ahmed, Alfaz; Goswami, Trishita; Das, Arpita; Vaishnavi, P.; Sahay, Rajiv R.

doi:10.1007/978-981-10-2107-7_9

Cited by 32 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Jalal et al [12] also proposed a real-time tracking system for human pose recognition utilizing ridge body parts' features from depth information. Recent works propose deep learning to tackle with this matter [13,14] where approaches such as convolutional neural networks or stacked auto encoders demonstrated good performance.…”

Section: Related Workmentioning

confidence: 99%

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

et al. 2019

View full text Add to dashboard Cite

Human communication relies on several aspects beyond the speech. One of them is gestures as they express intentions, interests, feelings, or ideas and complement the speech. Social robots need to interpret these messages to allow a more natural Human-Robot Interaction. In this sense, our aim is to study the effect of position and speed features in dynamic gesture recognition. We use 3D information to extract the user’s skeleton and calculate the normalized position for all of its joints, and using the temporal variation of such positions, we calculate their speeds. Our three datasets are composed of 1355 samples from 30 users. We consider 14 common gestures in HRI involving upper body movements. A set of classification techniques is evaluated testing these three datasets to find what features perform better. Results indicate that the union of both speed and position achieves the best results among the three possibilities, 0.999 of F-score. The combination that performs better to detect dynamic gestures in real time is finally integrated in our social robot with a simple HRI application to run a proof of concept test to check how the proposal behaves in a realistic scenario.

show abstract

Section: Related Workmentioning

confidence: 99%

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Over the past decade, numerous works have been done in yoga posture recognition using vision and sensor technology. Motion capture sensors [81], [82], [83], [84], [85], [86], [87], [88], accelerometers [89], [90], gyroscopes [91], magnetometers [89], motion sensors [92], pressure sensors [93], other visual [24], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108] and wearable sensors [97], [109] are some of the most utilized in postural estimations. Radio Frequency Identification [RFID] [110] and Wireless Fidelity (Wi-Fi) [111], for example, are location-based sensors.…”

Section: F Yoga Posture Recognition (Ypr)mentioning

confidence: 99%

“…Data collection [24], [81], [82], [83], [84], [85], [86], [87], [88], [91], [92], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [106], [109], [110], [111], [112], [113], [114], [115], [116], keypoint extraction [81], [82], [83], [84], [85], [86], [87], [88], [91], [92], [94], [95], [96], [97], [98], [99], [100], [101], [102]<...…”

Section: F Yoga Posture Recognition (Ypr)unclassified

A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Another system presented in reference [ 3 ] for 3 yoga poses achieved 82.84% accuracy. [ 5 ] The system used deep learning techniques for the classification of yoga poses. In traditional machine learning, [ 22 ] models require extracted features and engineering, but deep learning understands data and extracts features.…”

Section: Related Workmentioning

confidence: 99%

“…They are, however, utilizing a depth sensor-based camera that is normally not obtainable. Using convolutional neural network (CNN) and stacked autoencoder (SAE) methods, Mohanty et al [ 5 ] implemented an image recognition approach for detecting Indian traditional dance and yoga postures from photographs. They did, though, only analyze their competence on still photos, never on videos.…”

Section: Introductionmentioning

confidence: 99%

Yoga Pose Estimation and Feedback Generation Using Deep Learning

Thoutam

Srivastava

Badal

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one’s own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one’s health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity.

show abstract

Robust Pose Recognition Using Deep Learning

Cited by 32 publications

References 18 publications

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

A Survey on Yogic Posture Recognition

Yoga Pose Estimation and Feedback Generation Using Deep Learning

Contact Info

Product

Resources

About