Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors
Balance assessment is crucial for health monitoring and rehabilitation evaluation of neurological diseases like Parkinson’s disease (PD) and stroke. The Berg Balance Scale (BBS) is a widely used clinical tool for balance evaluation. However, its dependence on trained therapists for subjective, time-...
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MDPI AG
2025-04-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/4/395 |
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| author | Zhangli Lu Huiying Zhou Honghao Lyu Haiteng Wu Shaohua Tian Geng Yang |
| author_facet | Zhangli Lu Huiying Zhou Honghao Lyu Haiteng Wu Shaohua Tian Geng Yang |
| author_sort | Zhangli Lu |
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| description | Balance assessment is crucial for health monitoring and rehabilitation evaluation of neurological diseases like Parkinson’s disease (PD) and stroke. The Berg Balance Scale (BBS) is a widely used clinical tool for balance evaluation. However, its dependence on trained therapists for subjective, time-consuming assessments limits its scalability. Current researchers have proposed several automated assessment systems. However, they suffer from difficulty in use in clinical settings and the need for feature engineering. The rapid advancement of wearable inertial measurement units (IMUs) provides an objective tool for motion analysis that is suitable for use in clinical environments. Thus, to address the limitations of manual scoring and complexities of capturing gait features, we proposed an automated BBS assessment system using an attention-based deep learning algorithm with IMU data, integrating convolutional neural networks (CNNs) for spatial feature extraction, bidirectional long short-term memory (Bi-LSTM) networks for temporal modeling, and attention mechanisms to emphasize informative features. Validated with 20 healthy subjects (young and elderly) and 20 patients (PD and stroke), the system achieved a mean absolute error (MAE) of 1.1627 and root mean squared error (RMSE) of 1.5333. Requiring only 5 min of walking data, this approach provided an efficient, objective solution for balance assessment to assist healthcare physicians as well as patients in their own health monitoring. The key limitations included: a limited generalizability to severely impaired patients who were unable to walk independently, and the inability to predict the score of individual tasks. |
| format | Article |
| id | doaj-art-c1bf57cdff30485e8b096761dac4d197 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-c1bf57cdff30485e8b096761dac4d1972025-08-20T02:28:18ZengMDPI AGBioengineering2306-53542025-04-0112439510.3390/bioengineering12040395Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU SensorsZhangli Lu0Huiying Zhou1Honghao Lyu2Haiteng Wu3Shaohua Tian4Geng Yang5State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaZhejiang Key Laboratory of Intelligent Operation and Maintenance Robot, Hangzhou Shenhao Technology, Hangzhou 310000, ChinaZhejiang Key Laboratory of Intelligent Operation and Maintenance Robot, Hangzhou Shenhao Technology, Hangzhou 310000, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaBalance assessment is crucial for health monitoring and rehabilitation evaluation of neurological diseases like Parkinson’s disease (PD) and stroke. The Berg Balance Scale (BBS) is a widely used clinical tool for balance evaluation. However, its dependence on trained therapists for subjective, time-consuming assessments limits its scalability. Current researchers have proposed several automated assessment systems. However, they suffer from difficulty in use in clinical settings and the need for feature engineering. The rapid advancement of wearable inertial measurement units (IMUs) provides an objective tool for motion analysis that is suitable for use in clinical environments. Thus, to address the limitations of manual scoring and complexities of capturing gait features, we proposed an automated BBS assessment system using an attention-based deep learning algorithm with IMU data, integrating convolutional neural networks (CNNs) for spatial feature extraction, bidirectional long short-term memory (Bi-LSTM) networks for temporal modeling, and attention mechanisms to emphasize informative features. Validated with 20 healthy subjects (young and elderly) and 20 patients (PD and stroke), the system achieved a mean absolute error (MAE) of 1.1627 and root mean squared error (RMSE) of 1.5333. Requiring only 5 min of walking data, this approach provided an efficient, objective solution for balance assessment to assist healthcare physicians as well as patients in their own health monitoring. The key limitations included: a limited generalizability to severely impaired patients who were unable to walk independently, and the inability to predict the score of individual tasks.https://www.mdpi.com/2306-5354/12/4/395inertial measurement unit (IMU)Berg Balance Scale (BBS)gait analysisdeep learningbalance assessment |
| spellingShingle | Zhangli Lu Huiying Zhou Honghao Lyu Haiteng Wu Shaohua Tian Geng Yang Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors Bioengineering inertial measurement unit (IMU) Berg Balance Scale (BBS) gait analysis deep learning balance assessment |
| title | Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors |
| title_full | Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors |
| title_fullStr | Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors |
| title_full_unstemmed | Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors |
| title_short | Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors |
| title_sort | berg balance scale scoring system for balance evaluation by leveraging attention based deep learning with wearable imu sensors |
| topic | inertial measurement unit (IMU) Berg Balance Scale (BBS) gait analysis deep learning balance assessment |
| url | https://www.mdpi.com/2306-5354/12/4/395 |
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