A5045784435- Gait Recognition and Analysis
- Context-Aware Activity Recognition Systems
- Non-Invasive Vital Sign Monitoring
- Video Surveillance and Tracking Methods
- Soil Mechanics and Vehicle Dynamics
- Advanced Measurement and Detection Methods
- Balance, Gait, and Falls Prevention
- Infrared Target Detection Methodologies
- Winter Sports Injuries and Performance
- CCD and CMOS Imaging Sensors
- Sports Dynamics and Biomechanics
- Inertial Sensor and Navigation
- Anomaly Detection Techniques and Applications
- Cardiovascular Health and Disease Prevention
Polytechnic University of Turin
2023-2025
Head-worn inertial sensors represent a valuable option to characterize gait in real-world conditions, thanks the integration with glasses and hearing aids. Few methods based on head-worn allow for stride-by-stride speed estimation, but none has been developed data collected settings. This study aimed at validating two-steps machine learning method estimate initial contacts using single sensor attached temporal region. A convolutional network is used detect strides. Then, inferred from...
Recently, head-worn inertial sensors have been proposed to characterize gait. However, only few methods allow for both initial foot contacts detection and stride-by-stride gait speed estimation, none of them has validated in real-world settings. In this study, we assessed the performance a two-step machine learning algorithm estimate realworld conditions with single sensor attached temporal region head. A deep convolutional network is used detect cycles. Then, inferred from detected cycles...