Human Motion Assistance using Walking-aid Robot and Wearable Sensors

2013 ◽  
Keyword(s):  
Wearable Sensors ◽  
Human Motion ◽  
Walking Aid

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 A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2761 ◽  
Author(s):  
Wenxia Xu ◽  
Jian Huang ◽  
Lei Cheng
Keyword(s):  
Human Locomotion ◽  
Detection Algorithm ◽  
Human Motion ◽  
Lower Limbs ◽  
Support Vector ◽  
Normal Walking ◽  
Coordinated Motion ◽  
Fusion Algorithm ◽  
Robot System ◽  
Walking Aid

Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user’s normal walking state and abnormal walking state for a walking-aid robot system. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user’s upper and lower limbs. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user’s falling mode when he/she is in an abnormal walking state. The experimental results verify the effectiveness of the proposed algorithms.

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 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).

Coupling piezoelectric and piezoresistive effects in flexible pressure sensors for human motion detection from zero to high frequency

2021 ◽  
Author(s):  
Lijun Lu ◽  
Ning Zhao ◽  
Jingquan Liu ◽  
Bin Yang
Keyword(s):  
Motion Detection ◽  
Human Activities ◽  
High Frequency ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Health Condition ◽  
Human Motion ◽  
Human Motion Detection ◽  
Single Mechanism ◽  
Flexible Pressure Sensors

Flexible wearable sensors have received considerable popularity due to the potential application in monitoring human activities and health condition. However, traditional pressure sensors are always relying on single mechanism (such...

scholarly journals Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Haipiao Cai ◽  
Shihong Duan ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Domain Knowledge ◽  
Wearable Sensors ◽  
Hierarchical Classification ◽  
Physical Nature ◽  
Human Motion ◽  
Streaming Data ◽  
Temporal Information ◽  
Knowledge Based ◽  
Human Motions

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature and temporal information of human motions. Consequently, they suffer from data dependencies and encounter the curse of dimension and the overfitting issue. Their models are hard to be intuitively understood. Given a specific motion set, if structured domain knowledge could be manually obtained, it could be used for better recognizing certain motions. In this study, we start from a deep analysis on natural physical properties and temporal recurrent transformation possibilities of human motions and then propose a useful Recurrent Transformation Prior Knowledge-based Decision Tree (RT-PKDT) model for recognition of specific human motions. RT-PKDT utilizes temporal information and hierarchical classification method, making the most of sensor streaming data and human knowledge to compensate the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as SVM, BP neural networks, and Bayesian Network, obtaining an accuracy of 96.68%.

scholarly journals Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges

2019 ◽  
Vol 25 (1) ◽  
pp. 9-24 ◽  
Author(s):  
S. Zohreh Homayounfar ◽  
Trisha L. Andrew
Keyword(s):  
Large Scale ◽  
Large Range ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Human Locomotion ◽  
Human Motion ◽  
Human Gait ◽  
Wearable Electronics ◽  
New Horizons ◽  
User Friendly

The emergence of flexible wearable electronics as a new platform for accurate, unobtrusive, user-friendly, and longitudinal sensing has opened new horizons for personalized assistive tools for monitoring human locomotion and physiological signals. Herein, we survey recent advances in methodologies and materials involved in unobtrusively sensing a medium to large range of applied pressures and motions, such as those encountered in large-scale body and limb movements or posture detection. We discuss three commonly used methodologies in human gait studies: inertial, optical, and angular sensors. Next, we survey the various kinds of electromechanical devices (piezoresistive, piezoelectric, capacitive, triboelectric, and transistive) that are incorporated into these sensor systems; define the key metrics used to quantitate, compare, and optimize the efficiency of these technologies; and highlight state-of-the-art examples. In the end, we provide the readers with guidelines and perspectives to address the current challenges of the field.

Sign in / Sign up

Export Citation Format

Share Document