2023
DOI: 10.1109/jbhi.2023.3271463
|View full text |Cite
|
Sign up to set email alerts
|

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
3
1
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(1 citation statement)
references
References 31 publications
0
1
0
Order By: Relevance
“…To meet the computational demands of deep learning models, the integration of edge computing devices gives a promising solution. With the help of edge computing, the wearable sensor-based fall detection can achieve enhanced efficiency and responsiveness [ [25] , [26] , [27] , [28] , [29] , [30] ]. These devices can pre-process sensor data, extract relevant features, and even execute lightweight versions of deep learning models locally in the wearable sensors or at a nearby server.…”
Section: Introductionmentioning
confidence: 99%
“…To meet the computational demands of deep learning models, the integration of edge computing devices gives a promising solution. With the help of edge computing, the wearable sensor-based fall detection can achieve enhanced efficiency and responsiveness [ [25] , [26] , [27] , [28] , [29] , [30] ]. These devices can pre-process sensor data, extract relevant features, and even execute lightweight versions of deep learning models locally in the wearable sensors or at a nearby server.…”
Section: Introductionmentioning
confidence: 99%