2021
DOI: 10.3390/s21196513
|View full text |Cite
|
Sign up to set email alerts
|

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks

Abstract: Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to class… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
7
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
10

Relationship

2
8

Authors

Journals

citations
Cited by 15 publications
(7 citation statements)
references
References 52 publications
0
7
0
Order By: Relevance
“…In terms of the selection of experimental subjects, the experimental participants presented in some papers [ 48 , 50 , 53 , 54 , 56 , 57 , 58 , 60 , 65 , 69 , 70 , 71 , 73 , 74 , 75 ] selected disease patients or a combination of disease patients and healthy participants for the experimental research. Another approach [ 42 , 46 , 47 , 49 , 51 , 55 , 59 , 62 , 66 , 67 , 68 , 72 , 76 ] was to recruit disease-simulated subjects to imitate patients for exercise experiments. There was a certain gap between the information collected by simulated subjects and the real data of patients, and it was difficult to guarantee the authenticity and validity of the research results.…”
Section: Discussionmentioning
confidence: 99%
“…In terms of the selection of experimental subjects, the experimental participants presented in some papers [ 48 , 50 , 53 , 54 , 56 , 57 , 58 , 60 , 65 , 69 , 70 , 71 , 73 , 74 , 75 ] selected disease patients or a combination of disease patients and healthy participants for the experimental research. Another approach [ 42 , 46 , 47 , 49 , 51 , 55 , 59 , 62 , 66 , 67 , 68 , 72 , 76 ] was to recruit disease-simulated subjects to imitate patients for exercise experiments. There was a certain gap between the information collected by simulated subjects and the real data of patients, and it was difficult to guarantee the authenticity and validity of the research results.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, the estimation of various walking intensities based on wearable plantar pressure sensors was performed using an artificial neural network, another machine learning method. Namely, that contemporary study was in line with our technique since it used plantar pressure images and the data-labeling method [ 25 ]. Finally, artificial intelligence is useful in other fields of medicine as well, such as the prevention of intraoperative anaphylaxis [ 26 ].…”
Section: Discussionmentioning
confidence: 99%
“…This may be affected by the short walking time that requires a more dominant leg (right foot). It produces more pressure on the footprint images, for determining the left and right foot results is more accurate than long-time walking [46]. Loss of foot features in plantar pressure images may affect the left foot YOLO series detection.…”
Section: Discussionmentioning
confidence: 99%