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 Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6246
Author(s):  
Wenyu Cai ◽  
Dongyang Zhao ◽  
Meiyan Zhang ◽  
Yinan Xu ◽  
Zhu Li
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Recognition Algorithm ◽  
Self Organizing Map ◽  
Sitting Posture ◽  
Long Time ◽  
Posture Recognition ◽  
Self Organizing

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

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

Android-Based Skin Cancer Recognition System Using Convolutional Neural Network

2021 ◽  
pp. 59-85
Author(s):  
Sercan Demirci ◽  
Durmuş Özkan Şahin ◽  
Ibrahim Halil Toprak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Early Diagnosis ◽  
Learning Algorithm ◽  
Recognition System ◽  
Machine Learning Algorithm ◽  
The Sun ◽  
Human Beings ◽  
The World

Skin cancer, which is one of the most common types of cancer in the world, is a malignant growth seen on the skin due to various reasons. There was an increase in the number of the cases of skin cancer nearly 200% between 2004-2009. Since the ozone layer is depleting, harmful rays reflected from the sun cannot be filtered. In this case, the likelihood of skin cancer will increase over the years and pose more risks for human beings. Early diagnosis is very significant as in all types of cancers. In this study, a mobile application is developed in order to detect whether the skin spots photographed by using the machine learning technique for early diagnosis have a suspicion of skin cancer. Thus, an auxiliary decision support system is developed that can be used both by the clinicians and individuals. For cases that are predicted to have a risk higher than a certain rate by the machine learning algorithm, early diagnosis could be initiated for the patients by consulting a physician when the case is considered to have a higher risk by machine learning algorithm.

scholarly journals Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3115 ◽  
Author(s):  
Yang Wei ◽  
Hao Wang ◽  
Kim Fung Tsang ◽  
Yucheng Liu ◽  
Chung Kit Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Health Assessment ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Tree Health ◽  
Environmental Feature ◽  
Feature Based

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

scholarly journals Voice Recognition System Through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 4478-4483
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Voice Recognition ◽  
Recognition System ◽  
New Method ◽  
Human Voice ◽  
Developing Area ◽  
Voice Processing ◽  
The Voice

Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.

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