scholarly journals Environment Recognition System for Biped Robot Walking Using Vision Based Sensor Fusion

Robot Vision ◽  
10.5772/9300 ◽  
2010 ◽  
Author(s):  
Tae-Koo Kang ◽  
Hee-Jun Song ◽  
Gwi-Tae Park
Keyword(s):  
Sensor Fusion ◽  
Biped Robot ◽  
Recognition System ◽  
Environment Recognition

Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot

2021 ◽  
Vol 21 (2) ◽  
pp. 2241-2255 ◽  
Author(s):  
Tzuu-Hseng S. Li ◽  
Ping-Huan Kuo ◽  
Chuan-Han Cheng ◽  
Chia-Ching Hung ◽  
Po-Chien Luan ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Biped Robot ◽  
Gait Pattern

Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots

2008 ◽  
Vol 5 ◽  
pp. 113-120 ◽  
Author(s):  
Atsushi Kanda ◽  
Masanori Sato ◽  
Kazuo Ishii
Keyword(s):  
Mobile Robots ◽  
Recognition System ◽  
Multiple Classification ◽  
Environment Recognition

scholarly journals Sensor Fusion for Recognition of Activities of Daily Living

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4029 ◽  
Author(s):  
Jiaxuan Wu ◽  
Yunfei Feng ◽  
Peng Sun
Keyword(s):  
Time Series ◽  
Sensor Fusion ◽  
Daily Living ◽  
Single Point ◽  
Recognition System ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Life Patterns ◽  
Functional Capabilities ◽  
Correction Technique

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.

Outdoor environment recognition system on Robotic Communication Terminals supporting mobility of elderly and disabled people

2006 ◽  
Vol 37 (8) ◽  
pp. 56-67
Author(s):  
Kentaro Kayama ◽  
Ikuko Eguchi Yairi ◽  
Seiji Igi
Keyword(s):  
Recognition System ◽  
Outdoor Environment ◽  
Disabled People ◽  
Environment Recognition

scholarly journals Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6256
Author(s):  
Boon Giin Lee ◽  
Teak-Wei Chong ◽  
Wan-Young Chung
Keyword(s):  
Deep Learning ◽  
American Sign Language ◽  
Sign Language ◽  
Sensor Fusion ◽  
Recognition Rate ◽  
Recognition System ◽  
Hearing Impaired ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Impaired People

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

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