scholarly journals Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 187 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Tai Chan
Keyword(s):  
Motion Segmentation ◽  
Wearable Sensors ◽  
Human Motion ◽  
Significant Change

Human motion segmentation based on structure constraint matrix factorization

2021 ◽  
Vol 65 (1) ◽  
Author(s):  
Hongbo Gao ◽  
Fang Guo ◽  
Juping Zhu ◽  
Zhen Kan ◽  
Xinyu Zhang
Keyword(s):  
Matrix Factorization ◽  
Motion Segmentation ◽  
Human Motion ◽  
Constraint Matrix ◽  
Structure Constraint

scholarly journals Human motion component and envelope characterization via wireless wearable sensors

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Kaitlyn R. Ammann ◽  
Touhid Ahamed ◽  
Alice L. Sweedo ◽  
Roozbeh Ghaffari ◽  
Yonatan E. Weiner ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Human Motion

scholarly journals Near-Linear Responsive and Wide-Range Pressure and Stretch Sensor Based on Hierarchical Graphene-Based Structures via Solvent-Free Preparation

Polymers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1814 ◽  
Author(s):  
Jian Wang ◽  
Ryuki Suzuki ◽  
Kentaro Ogata ◽  
Takuto Nakamura ◽  
Aixue Dong ◽  
...  
Keyword(s):  
Organic Solvents ◽  
Large Strain ◽  
Rapid Development ◽  
Wearable Sensors ◽  
Human Motion ◽  
Solvent Free ◽  
Gauge Factor ◽  
Wearable Electronics ◽  
Human Motion Detection ◽  
Wide Range

Flexible and wearable electronics have huge potential applications in human motion detection, human–computer interaction, and context identification, which have promoted the rapid development of flexible sensors. So far the sensor manufacturing techniques are complex and require a large number of organic solvents, which are harmful not only to human health but also to the environment. Here, we propose a facile solvent-free preparation toward a flexible pressure and stretch sensor based on a hierarchical layer of graphene nanoplates. The resulting sensor exhibits many merits, including near-linear response, low strain detection limits to 0.1%, large strain gauge factor up to 36.2, and excellent cyclic stability withstanding more than 1000 cycles. Besides, the sensor has an extraordinary pressure range as large as 700 kPa. Compared to most of the reported graphene-based sensors, this work uses a completely environmental-friendly method that does not contain any organic solvents. Moreover, the sensor can practically realize the delicate detection of human body activity, speech recognition, and handwriting recognition, demonstrating a huge potential for wearable sensors.

scholarly journals Human Motion Recognition by Textile Sensor Based on Machine Learning Algorithms

2018 ◽  
Author(s):  
Chi Cuong Vu ◽  
Jooyong Kim
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Learning Algorithms ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Identification Rate ◽  
Human Motion Recognition ◽  
Textile Sensor ◽  
Wearable Products ◽  
Walled Carbon Nanotubes

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

scholarly journals Low-Rank Transfer Human Motion Segmentation

2019 ◽  
Vol 28 (2) ◽  
pp. 1023-1034 ◽  
Author(s):  
Lichen Wang ◽  
Zhengming Ding ◽  
Yun Fu
Keyword(s):  
Motion Segmentation ◽  
Human Motion ◽  
Low Rank

scholarly journals High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1238 ◽  
Author(s):  
Irvin López-Nava ◽  
Angélica Muñoz-Meléndez
Keyword(s):  
Daily Living ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Research Problem ◽  
Human Motion ◽  
Lower Limbs ◽  
Smart Devices ◽  
Joint Orientation ◽  
Human Actions ◽  
Living Environments

Action recognition is important for various applications, such as, ambient intelligence, smart devices, and healthcare. Automatic recognition of human actions in daily living environments, mainly using wearable sensors, is still an open research problem of the field of pervasive computing. This research focuses on extracting a set of features related to human motion, in particular the motion of the upper and lower limbs, in order to recognize actions in daily living environments, using time-series of joint orientation. Ten actions were performed by five test subjects in their homes: cooking, doing housework, eating, grooming, mouth care, ascending stairs, descending stairs, sitting, standing, and walking. The joint angles of the right upper limb and the left lower limb were estimated using information from five wearable inertial sensors placed on the back, right upper arm, right forearm, left thigh and left leg. The set features were used to build classifiers using three inference algorithms: Naive Bayes, K-Nearest Neighbours, and AdaBoost. The F- m e a s u r e average of classifying the ten actions of the three classifiers built by using the proposed set of features was 0.806 ( σ = 0.163).

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