A Machine Learning App for Monitoring Physical Therapy at Home
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/1/158 |
| _version_ | 1797358101038366720 |
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| author | Bruno Pereira Bruno Cunha Paula Viana Maria Lopes Ana S. C. Melo Andreia S. P. Sousa |
| author_facet | Bruno Pereira Bruno Cunha Paula Viana Maria Lopes Ana S. C. Melo Andreia S. P. Sousa |
| author_sort | Bruno Pereira |
| collection | DOAJ |
| description | Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research. |
| first_indexed | 2024-03-08T14:56:58Z |
| format | Article |
| id | doaj.art-0495ec4e7cfa4f879245dc5dea09270b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:56:58Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0495ec4e7cfa4f879245dc5dea09270b2024-01-10T15:08:49ZengMDPI AGSensors1424-82202023-12-0124115810.3390/s24010158A Machine Learning App for Monitoring Physical Therapy at HomeBruno Pereira0Bruno Cunha1Paula Viana2Maria Lopes3Ana S. C. Melo4Andreia S. P. Sousa5Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalInstituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalInstituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalCenter for Rehabilitation Research, Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalCenter for Rehabilitation Research, Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalCenter for Rehabilitation Research, Human Movement System (Re)habilitation Area, School of Health, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, PortugalShoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.https://www.mdpi.com/1424-8220/24/1/158pose estimationexercise evaluationmobile healthremote monitoringrehabilitation |
| spellingShingle | Bruno Pereira Bruno Cunha Paula Viana Maria Lopes Ana S. C. Melo Andreia S. P. Sousa A Machine Learning App for Monitoring Physical Therapy at Home Sensors pose estimation exercise evaluation mobile health remote monitoring rehabilitation |
| title | A Machine Learning App for Monitoring Physical Therapy at Home |
| title_full | A Machine Learning App for Monitoring Physical Therapy at Home |
| title_fullStr | A Machine Learning App for Monitoring Physical Therapy at Home |
| title_full_unstemmed | A Machine Learning App for Monitoring Physical Therapy at Home |
| title_short | A Machine Learning App for Monitoring Physical Therapy at Home |
| title_sort | machine learning app for monitoring physical therapy at home |
| topic | pose estimation exercise evaluation mobile health remote monitoring rehabilitation |
| url | https://www.mdpi.com/1424-8220/24/1/158 |
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