Empowering Elderly Safety: 1D-CNN and IoT-Enabled Fall Detection System
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8661-3_38
Reference22 articles.
1. Salah OZ, Selvaperumal SK, Abdulla R (2022) Accelerometer-based elderly fall detection system using edge artificial intelligence architecture. Int J Electr Comput Eng 12(4):4430
2. Tanwar R et al (2022) Pathway of trends and technologies in fall detection: a systematic review. In: Healthcare, vol 10, no 1. Multidisciplinary Digital Publishing Institute
3. Xia K, Huang J, Wang H (2020) LSTM-CNN architecture for human activity recognition. IEEE Access 8:56855–56866
4. Thakur D, Biswas S (2022) Attention-Based deep learning framework for hemiplegic gait prediction with smartphone sensors. IEEE Sens J 22(12):11979–11988
5. Casilari E, Oviedo-Jiménez MA (2015) Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PloS one 10(11):e0140929
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