Traditionally, PT recommends physical therapy tasks to patients based on their experience, sometimes with subjective bias. Besides, tasks and criteria cannot be updated timely before the patient’s next visit with the PT, even with patient’s significant progress. To enhance the treatment efficiency and help patients receive personalized task recommendation, we are developing a learning-based task recommendation system. For this work, we are focusing on patients with Parkinson's disease.


The overall treatment process for patients with Parkinson's disease can be divided into two stages. 1) Initial evaluation: patients perform some mini-tasks and the PT recommend initial tasks for patients based on their performance on those tasks. 2) Follow-up task recommendation: patients practice the tasks that PT recommended to him/her at home. When the patient visits the PT again, the PT will check the patient’s performance on the current tasks and modify the tasks/criteria accordingly. 

 

physical therapy (traditional).pngTraditional procedure for treatment of patients with Parkinson's disease

physical therapy (machine learning).pngLearning-based task recommendation system for patients with Parkinson's disease

 

Traditionally, tasks and criteria are modified manually by the PT. However, PT’s recommendation may be impacted by his/her subjective bias. Moreover, tasks and criteria cannot be updated timely before the patient’s next visit with the PT, even with patient’s significant progress. Also, the traditional approach necessitates patient-PT live sessions, which may limit participation by patients with insurance limitations and/or inability to travel to PT location. Therefore, we propose to develop an automated task recommendation model using machine learning techniques. This model will be trained offline using the performance data of multiple patients as well as the corresponding PT recommendations, and can be used by PTs to remotely update patient tasks.