Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning
Abstract Tai Chi, an Asian martial art, is renowned for its health benefits, particularly in promoting healthy aging among older adults, improving balance, and reducing fall risk. However, methodological challenges hinder the objective measurement of adherence to and proficiency in performing a trai...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93979-2 |
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| author | Giulia Corniani Stefano Sapienza Gloria Vergara-Diaz Andrea Valerio Ashkan Vaziri Paolo Bonato Peter M. Wayne |
| author_facet | Giulia Corniani Stefano Sapienza Gloria Vergara-Diaz Andrea Valerio Ashkan Vaziri Paolo Bonato Peter M. Wayne |
| author_sort | Giulia Corniani |
| collection | DOAJ |
| description | Abstract Tai Chi, an Asian martial art, is renowned for its health benefits, particularly in promoting healthy aging among older adults, improving balance, and reducing fall risk. However, methodological challenges hinder the objective measurement of adherence to and proficiency in performing a training protocol, critical for health outcomes. This study introduces a framework using wearable sensors and machine learning to monitor Tai Chi training adherence and proficiency. Data were collected from 32 participants with inertial measurement units (IMUs) while performing six Tai Chi movements evaluated and scored for adherence and proficiency by experts. Our framework comprises a model for identifying the specific Tai Chi movement being performed and a model to assess performance proficiency, both employing Random Forest algorithms and features from IMU signals. The movement identification model achieved a micro F1 score of 90.05%. The proficiency assessment models achieved a mean micro F1 score of 78.64%. This study shows the feasibility of using IMUs and machine learning for detailed Tai Chi movement analysis, offering a scalable method for monitoring practice. This approach has the potential to objectively enhance the evaluation of Tai Chi training protocol adherence, learnability, progression in proficiency, and safety in Tai Chi programs, and thus inform training program parameters that are key to achieving optimal clinical outcomes. |
| format | Article |
| id | doaj-art-36b88e72687a48fca75c587fd4d2edfd |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-36b88e72687a48fca75c587fd4d2edfd2025-08-20T02:49:32ZengNature PortfolioScientific Reports2045-23222025-03-0115111210.1038/s41598-025-93979-2Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learningGiulia Corniani0Stefano Sapienza1Gloria Vergara-Diaz2Andrea Valerio3Ashkan Vaziri4Paolo Bonato5Peter M. Wayne6Department of Physical Medicine and Rehabilitation, Harvard Medical School and Spaulding Rehabilitation HospitalDepartment of Physical Medicine and Rehabilitation, Harvard Medical School and Spaulding Rehabilitation HospitalDepartment of Physical Medicine and Rehabilitation, Harvard Medical School and Spaulding Rehabilitation HospitalDepartment of Electronics and Telecommunications, Politecnico di TorinoBioSensics LLCDepartment of Physical Medicine and Rehabilitation, Harvard Medical School and Spaulding Rehabilitation HospitalOsher Center for Integrative Health, Harvard Medical School and Brigham and Women’s HospitalAbstract Tai Chi, an Asian martial art, is renowned for its health benefits, particularly in promoting healthy aging among older adults, improving balance, and reducing fall risk. However, methodological challenges hinder the objective measurement of adherence to and proficiency in performing a training protocol, critical for health outcomes. This study introduces a framework using wearable sensors and machine learning to monitor Tai Chi training adherence and proficiency. Data were collected from 32 participants with inertial measurement units (IMUs) while performing six Tai Chi movements evaluated and scored for adherence and proficiency by experts. Our framework comprises a model for identifying the specific Tai Chi movement being performed and a model to assess performance proficiency, both employing Random Forest algorithms and features from IMU signals. The movement identification model achieved a micro F1 score of 90.05%. The proficiency assessment models achieved a mean micro F1 score of 78.64%. This study shows the feasibility of using IMUs and machine learning for detailed Tai Chi movement analysis, offering a scalable method for monitoring practice. This approach has the potential to objectively enhance the evaluation of Tai Chi training protocol adherence, learnability, progression in proficiency, and safety in Tai Chi programs, and thus inform training program parameters that are key to achieving optimal clinical outcomes.https://doi.org/10.1038/s41598-025-93979-2Tai ChiWearable sensorMachine learningHealthy agingFall prevention |
| spellingShingle | Giulia Corniani Stefano Sapienza Gloria Vergara-Diaz Andrea Valerio Ashkan Vaziri Paolo Bonato Peter M. Wayne Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning Scientific Reports Tai Chi Wearable sensor Machine learning Healthy aging Fall prevention |
| title | Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning |
| title_full | Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning |
| title_fullStr | Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning |
| title_full_unstemmed | Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning |
| title_short | Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning |
| title_sort | remote monitoring of tai chi balance training interventions in older adults using wearable sensors and machine learning |
| topic | Tai Chi Wearable sensor Machine learning Healthy aging Fall prevention |
| url | https://doi.org/10.1038/s41598-025-93979-2 |
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