scholarly journals TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting

2018 ◽  
Vol 5 (6) ◽  
pp. 4441-4453 ◽  
Author(s):  
Hui Huang ◽  
Xian Li ◽  
Si Liu ◽  
Shiyan Hu ◽  
Ye Sun
Keyword(s):  
Energy Harvesting ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Motion Sensor ◽  
Self Powered

scholarly journals Design of Optimal Deep Learning based Human Activity Recognition on Sensor Enabled Internet of Things Environment

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fahd N. Al-Wesabi ◽  
Amani Abdulrahman Albraikan ◽  
Anwer Mustafa Hilal ◽  
Asma Abdulghani Al-Shargabi ◽  
Saleh Alhazbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Internet Of Things Environment

scholarly journals Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Mohammad Iqbal ◽  
Chandrawati Putri Wulandari ◽  
Wawan Yunanto ◽  
Ghaluh Indah Permata Sari
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Activity Patterns ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Sequential Patterns ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Human Activity Patterns

Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP).  The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.

HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices

2018 ◽  
Vol 17 (6) ◽  
pp. 1353-1368 ◽  
Author(s):  
Sara Khalifa ◽  
Guohao Lan ◽  
Mahbub Hassan ◽  
Aruna Seneviratne ◽  
Sajal K. Das
Keyword(s):  
Energy Harvesting ◽  
Kinetic Energy ◽  
Activity Recognition ◽  
Human Activity ◽  
Wearable Devices ◽  
Human Activity Recognition
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