TY  - JOUR
PY  - 2024//
TI  - Tag-free indoor fall detection using transformer network encoder and data fusion
JO  - Scientific reports
A1  - Khan, Muhammad Zakir
A1  - Usman, Muhammad
A1  - Ahmad, Jawad
A1  - Rahman, Muhammad Mahboob Ur
A1  - Abbas, Hasan
A1  - Imran, Muhammad
A1  - Abbasi, Qammer H.
SP  - e16763
EP  - e16763
VL  - 14
IS  - 1
N2  - This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5%. This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.<p />  <p>Language: en</p>
LA  - en
SN  - 2045-2322
UR  - http://dx.doi.org/10.1038/s41598-024-67439-2
ID  - ref1
ER  -