Accurate and Efficient Real-World Fall Detection Using Time Series Techniques DOI
Timilehin B. Aderinola, Luca Palmerini, Ilaria D’Ascanio

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 52 - 79

Published: Dec. 31, 2024

Language: Английский

Fall Detection Systems for Internet of Medical Things Based on Wearable Sensors: A Review DOI
Zhiyuan Jiang, Mohammed A. A. Al‐qaness, Dalal AL-Alimi

et al.

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(21), P. 34797 - 34810

Published: July 1, 2024

Language: Английский

Citations

5
MicroFallNet: A Lightweight Model for Real-Time Fall Detection on Smart Wristbands DOI
Jun Hu,

Feiyan Cheng,

Meng Liu

et al.

Pervasive and Mobile Computing, Journal Year: 2025, Volume and Issue: unknown, P. 102046 - 102046

Published: March 1, 2025

Language: Английский

Citations

0
The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models DOI Creative Commons

Shina Samuel Kolawole,

Gautam Siddharth Kashyap,

Olamide Emmanuel Kolawole

et al.

EAI Endorsed Transactions on Pervasive Health and Technology, Journal Year: 2025, Volume and Issue: 11

Published: April 2, 2025

The research paper aims to assess ML models for video-recorded gaits with an aim of classifying people into high or low risks fall groups. Several algorithms were tried employing OpenPose CV, RF showing the best outcomes: 93% accuracy along F1-score as well balanced sensitivity (93.50%) specificity (92.50%). Some important determining factors speed per unit distance, angle among other statistical measures. In comparison wearables-based DL approaches plus traditional detection methods, this study’s approach showed higher and adaptability within health care settings.

Language: Английский

Citations

0
A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices DOI
Myung-Kyu Yi, Seong Oun Hwang

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110138 - 110138

Published: April 16, 2025

Language: Английский

Citations

0
A novel approach to enhanced fall detection using STFT and magnitude features with CNN autoencoder DOI
Tomorn Soontornnapar,

Tuchsanai Ploysuwan

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

Language: Английский

Citations

0
Accurate and Efficient Real-World Fall Detection Using Time Series Techniques DOI
Timilehin B. Aderinola, Luca Palmerini, Ilaria D’Ascanio

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 52 - 79

Published: Dec. 31, 2024

Language: Английский

Citations

0