Sitting posture recognition based on human body pressure and CNN

2019 ◽  
Author(s):  
Wenjun Liu ◽  
Yunfei Guo ◽  
Jun Yang ◽  
Yun Hu ◽  
Dapeng Wei
Keyword(s):  
Human Body ◽  
Sitting Posture ◽  
Posture Recognition

scholarly journals Detection of sitting posture using hierarchical image composition and deep learning

2021 ◽  
Vol 7 ◽  
pp. e442
Author(s):  
Audrius Kulikajevas ◽  
Rytis Maskeliunas ◽  
Robertas Damaševičius
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Human Body ◽  
Assisted Living ◽  
Hierarchical Network ◽  
Kinematic Parameters ◽  
Image Composition ◽  
Sitting Posture ◽  
Human Posture ◽  
Posture Recognition

Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem. The DRHN network accepts the RGB-Depth frame sequences and produces a representation of semantically related posture states. We achieved 91.47% accuracy at 10 fps rate for sitting posture recognition.

scholarly journals A Study on Sitting Posture Recognition using Machine Learning

2016 ◽  
Vol 65 (9) ◽  
pp. 1557-1563 ◽  
Author(s):  
Sangyong Ma ◽  
Sangpyo Hong ◽  
Hyeon-min Shim ◽  
Jang-Woo Kwon ◽  
Sangmin Lee
Keyword(s):  
Machine Learning ◽  
Sitting Posture ◽  
Posture Recognition

Sitting Posture Recognition and Location Estimation for Human-Aware Environment

2013 ◽  
pp. 317-331
Author(s):  
Yusuke Manabe ◽  
Kenji Sugawara
Keyword(s):  
Ubiquitous Computing ◽  
Real Space ◽  
Location Estimation ◽  
Information Platform ◽  
Sitting Posture ◽  
Software Application ◽  
Intelligent Software ◽  
Important Idea ◽  
Posture Recognition ◽  
Generation Information

Realization of human-computer symbiosis is an important idea in the context of ubiquitous computing. Symbiotic Computing is a concept that bridges the gap between situations in Real Space (RS) and data in Digital Space (DS). The main purpose is to develop an intelligent software application as well as establish the next generation information platform to develop the symbiotic system. In this paper, the authors argue that it is necessary to build ’Mutual Cognition’ between human and system. Mutual cognition consists of two functions: ’RS Cognition’ and ’DS Cognition’. This paper examines RS Cognition, which consists of many software functions for perceiving various situations like events or humans’ activities in RS. The authors develop two perceptual functions, sitting posture recognition and human’s location estimation for a person, as RS perception tasks. In the resulting experiments, developed functions are quite competent to recognize a human’s activities.

Sitting Posture Recognition and Location Estimation for Human-Aware Environment

2011 ◽  
Vol 3 (1) ◽  
pp. 34-49 ◽  
Author(s):  
Yusuke Manabe ◽  
Kenji Sugawara
Keyword(s):  
Real Space ◽  
Location Estimation ◽  
Information Platform ◽  
Sitting Posture ◽  
Software Application ◽  
Digital Space ◽  
Intelligent Software ◽  
Important Idea ◽  
Posture Recognition ◽  
Generation Information

Realization of human-computer symbiosis is an important idea in the context of ubiquitous computing. Symbiotic Computing is a concept that bridges the gap between situations in Real Space (RS) and data in Digital Space (DS). The main purpose is to develop an intelligent software application as well as establish the next generation information platform to develop the symbiotic system. In this paper, the authors argue that it is necessary to build ’Mutual Cognition’ between human and system. Mutual cognition consists of two functions: ’RS Cognition’ and ’DS Cognition’. This paper examines RS Cognition, which consists of many software functions for perceiving various situations like events or humans’ activities in RS. The authors develop two perceptual functions, sitting posture recognition and human’s location estimation for a person, as RS perception tasks. In the resulting experiments, developed functions are quite competent to recognize a human’s activities.

The Key Role of Headrest Optimization in Driver Comfort

2019 ◽  
Vol 24 (3) ◽  
pp. 592-599
Author(s):  
Hamid Gheibollahi ◽  
Masoud Masih-Tehrani ◽  
Mohammadmehdi Niroobakhsh
Keyword(s):  
Human Body ◽  
The Body ◽  
Human Body Model ◽  
Sitting Posture ◽  
Conventional Vehicle ◽  
Body Model ◽  
Basic Model ◽  
Proposed Model ◽  
Stiffness And Damping

In this study, adding a headrest to the conventional vehicle driver seat is investigated to improve the driver comfort and decrease the driver damages. For this purpose, a conventional biomechanical human body model of wholebody vibrations is provided and modified by adding a head degree of freedom to the body model and a headrest to the seat model. The basic model is in the sitting posture, lumped parameters and has nine DOFs for the human body, on contrary to the proposed model which has ten DOFs. The new human body DOF is the twisting motion of the head and neck. This new DOF is generated because of headrest adding to the driver’s seat. To determine the head discomforts, the Seat to Head (STH) indexes are studied in two directions: horizontal and vertical. The Genetic Algorithm (GA) is used to optimize the STH in different directions. The optimization variables are stiffness and damping parameters of the driver’s seat which are 12 for the basic model and are 16 for a new seat. The integer programming is used for time reduction. The results show that new seat (equipped by headrest) has very better STH in both directions.

Self-Assisted Care System for Human Mobility

2004 ◽  
Vol 16 (5) ◽  
pp. 464-472
Author(s):  
Teruhisa Onishi ◽  
◽  
Tatsuo Arai ◽  
Kenji Inoue ◽  
Yasushi Mae ◽  
...  
Keyword(s):  
Human Body ◽  
Human Mobility ◽  
The Body ◽  
Wearable Device ◽  
End Effector ◽  
Sitting Posture ◽  
Holding Device ◽  
Physically Challenged ◽  
Care System

Since humans are bipedal, it is easy to become bedridden when the hip and legs become weakened or disabled due to aging or other causes. If such physically challenged people are enabled to get up from bed and move to a nearby location, however, they could use their arms and hands to do certain tasks such as taking meals or writing. In this paper, we propose a wearable device for supporting the human body that can be easily worn or removed by means of a body-holding device. The hoisting device has an arm which, as an end-effector, supports the body and allows the user to rise from bed and lie down again. In addition, a three-point fixing mechanism is used to maintain the user’s sitting posture at a desk, and to realize an integrated self-assisted care system.

scholarly journals AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion

2019 ◽  
Author(s):  
Katia Bourahmoune ◽  
Toshiyuki Amagasa
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Well Being ◽  
Recognition System ◽  
Training System ◽  
Sensor Data ◽  
Physical And Mental Health ◽  
Sitting Posture ◽  
Posture Monitoring ◽  
Posture Recognition

Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.

scholarly journals Smart Chair for Monitoring of Sitting Behavior

2017 ◽  
Vol 2 (2) ◽  
pp. 274 ◽  
Author(s):  
Mengjie Huang ◽  
Ian Gibson ◽  
Rui Yang
Keyword(s):  
Human Body ◽  
Human Subjects ◽  
Healthcare Services ◽  
Human Computer Interactions ◽  
Intelligent Environment ◽  
Sitting Posture ◽  
Behavior Monitoring ◽  
Artificial Neural Network Classifier ◽  
Class 1 ◽  
Posture Monitoring

<p class="1">Sitting is a common behavior of human body in daily life. It is found that poor sitting postures can link to pains and other complications for people in literature. In order to avoid the adverse effects of poor sitting behavior, we have developed a highly practical design of smart chair system in this paper, which is able to monitor the sitting behavior of human body accurately and non-invasively. The pressure patterns of eight standardized sitting postures of human subjects were acquired and transmitted to the computer for the automatic sitting posture recognition with the application of artificial neural network classifier. The experimental results showed that it can recognize eight sitting postures of human subjects with high accuracy. The sitting posture monitoring in the developed smart chair system can help or promote people to achieve and maintain healthy sitting behavior, and prevent or reduce the chronic disease caused by poor sitting behavior. These promising results suggested that the presented system is feasible for sitting behavior monitoring, which can find applications in many areas including healthcare services, human-computer interactions and intelligent environment.</p>

scholarly journals Design and Implementation of Sensor-Embedded Chair for Continuous Sitting Posture Recognition

2020 ◽  
Vol E103.D (5) ◽  
pp. 1067-1077
Author(s):  
Teruhiro MIZUMOTO ◽  
Yasuhiro OTODA ◽  
Chihiro NAKAJIMA ◽  
Mitsuhiro KOHANA ◽  
Motohiro UENISHI ◽  
...  
Keyword(s):  
Sitting Posture ◽  
Design And Implementation ◽  
Posture Recognition
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