Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision

Guler, Alp; Kardaris, Nikolaos; Chandra, Siddhartha; Pitsikalis, Vassilis; Werner, Christian; Hauer, Klaus; Tzafestas, Costas S.; Maragos, Petros; Kokkinos, Iasonas

doi:10.1007/978-3-319-48881-3_29

Cited by 12 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In references [ 28 , 29 ], features used are angles between joints so that features are scaled. In reference [ 29 ], features mainly used are hip and knee angles.…”

Section: Related Workmentioning

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

“…In references [ 28 , 29 ], features used are angles between joints so that features are scaled. In reference [ 29 ], features mainly used are hip and knee angles.…”

Section: Related Workmentioning

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

show abstract

“…The algorithm uses information (time of flight, thermal, and 2D) from three cameras to detect and track the trunk, arms, and head; then, the position is classified by the body trunk lean direction (upright and forward) and the orientation patterns, like towards, neutral, or away from another person. Another example is presented by Guler et al in [ 64 , 65 ]; authors used an RGB-D camera and laser sensor to recognize the human gestures when a robot interacts with a person. The algorithm extracts the skeleton and estimates the joint angles using geometry.…”

Section: Algorithms Used For the Bodymentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

View full text Add to dashboard Cite

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

show abstract

“…Two-stage approaches, e.g. [9,10,12], firstly detect joint positions in 2D and subsequently lift joints into 3D by relying on prior knowledge about the 3D human pose. The advantage of such approaches is that they can exploit large datasets constructed for the prediction of 2D landmarksthe disadvantage is that errors in the 2D stage can propagate to the 3D predictions and can often not be recovered from.…”

Section: Introductionmentioning

confidence: 99%