Leyan Yu
Biographic Data
| ID | 9992220 |
|---|---|
| NAME | Leyan Yu |
| GIVEN NAMES | Leyan |
| FAMILY NAME | Yu |
| SIGNATURE | YU L |
| AFFILIATIONS | Jiangxi University of Traditional Chinese Medicine |
| ORCID | 0009-0000-0634-2392 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
From adoption to continued usage
Purpose Grounded in the decision factors classification framework, this paper examines how decision object factors, decision entity factors, and context factors combine as configurations that explain user adoption and continued usage of AI therapists. Furthermore, comparing the configurations for initial adoption and continued usage reveals three mechanisms—habit, belief updating, and belief revision—that facilitate the transition from adoption t…
Research on fall detection algorithm for older adults in complex lighting environments based on improved Yolov8n
Background: With the continuous intensification of global aging, the issue of older adult(s) falls has become a significant public health challenge. Real-time and accurate fall detection technology is of great importance for ensuring the safety of the older adult(s). To address the problems of difficult discrimination of fall features caused by human movement in complex backgrounds and low detection accuracy, this study proposes a fall detection …
No prominent works on this page.
From adoption to continued usage
Purpose Grounded in the decision factors classification framework, this paper examines how decision object factors, decision entity factors, and context factors combine as configurations that explain user adoption and continued usage of AI therapists. Furthermore, comparing the configurations for initial adoption and continued usage reveals three mechanisms—habit, belief updating, and belief revision—that facilitate the transition from adoption t…
Research on fall detection algorithm for older adults in complex lighting environments based on improved Yolov8n
Background: With the continuous intensification of global aging, the issue of older adult(s) falls has become a significant public health challenge. Real-time and accurate fall detection technology is of great importance for ensuring the safety of the older adult(s). To address the problems of difficult discrimination of fall features caused by human movement in complex backgrounds and low detection accuracy, this study proposes a fall detection …
Advanced Sensor and Energy Harvesting Materials (1 work) · Attention network (1 work) · Behavioral Health and Interventions (1 work) · Context-Aware Activity Recognition Systems (1 work) · Digital Mental Health Interventions (1 work) · Grounded theory (1 work) · Human Pose and Action Recognition (1 work) · Pedestrian detection (1 work) · Pooling (1 work) · Qualitative comparative analysis (1 work)