Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks.
Sensors (Basel)
; 24(13)2024 Jun 28.
Article
en En
| MEDLINE
| ID: mdl-39000985
ABSTRACT
(1) Background:
The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2)Methods:
This study consisted of two parts firstly, 70 skilled tai chi practitioners were used for movement recognition; secondly, 60 elderly males were used for an intervention study. IMU data were collected from skilled tai chi practitioners performing Bafa Wubu, and TCN models were constructed and trained to classify these movements. Elderly participants were divided into a precision intervention group and a standard intervention group, with the former receiving weekly real-time IMU feedback. Outcomes measured included balance, grip strength, quality of life, and depression. (3)Results:
The TCN model demonstrated high accuracy in identifying tai chi movements, with percentages ranging from 82.6% to 94.4%. After eight weeks of intervention, both groups showed significant improvements in grip strength, quality of life, and depression. However, only the precision intervention group showed a significant increase in balance and higher post-intervention scores compared to the standard intervention group. (4)Conclusions:
This study successfully employed IMU and TCN to identify Tai Chi movements and provide targeted feedback to older participants. Real-time IMU feedback can enhance health outcome indicators in elderly males.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Calidad de Vida
/
Redes Neurales de la Computación
/
Taichi Chuan
/
Movimiento
Límite:
Aged
/
Female
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Humans
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Male
Idioma:
En
Revista:
Sensors (Basel)
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Suiza