Efficient light harvesting for accurate neural classification of human activities
- Resource Type
- Conference
- Authors
- Nicosia, Alessandro; Pau, Danilo; Giacalone, Davide; Plebani, Emanuele; Bosco, Angelo; Iacchetti, Antonio
- Source
- 2018 IEEE International Conference on Consumer Electronics (ICCE) Consumer Electronics (ICCE), 2018 IEEE International Conference on. :1-4 Jan, 2018
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Batteries
Accelerometers
Energy harvesting
Photovoltaic cells
Random access memory
Power demand
Artificial neural networks
neural networks
human activities
energy harvesting
flexible solar cells
- Language
- ISSN
- 2158-4001
Energy autonomy extension of wearable devices is an ever increasing user need and it can be achieved by inexpensive energy harvesting from the broadly available solar and artificial light. However efficient conversion, relevant storage and utilization must be carefully implemented if the device supports power-hungry applications such as Artificial Intelligence for human activity classification based on Artificial Neural Networks. In this paper, a whole hardware and software system implementation is presented, which is able to achieve system autonomy extension and at the same time high classification accuracy. Quantitative and qualitative results are shown under real working conditions.