A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Abstract

During the process of rehabilitation after stroke, it is important that patients know how well they perform their exercise, so they can improve their performance in future repetitions. Standard clinical rating conducted by human observation is the prevailing way today to monitor motor recovery of the patient. Therefore, patients cannot know whether they are performing a movement properly while exercising by themselves. Adhering to the exercise regime makes the rehabilitation process more effective and efficient, and thus a system that can give the patients feedback on their performance is of great value. Here, we build a machine-learning-based automated model that give patients accurate information on the compensatory (undesirable) movements that they make. To construct the model, we recorded movements from 30 stroke patients, who each performed 18 movements, used to identify the presence of six types of compensatory movements in stroke patients' movement trajectories. We used the random-forest algorithm for training this multi-label classification model. We achieved 85% average precision across the six movement compensations. This is the first study to automatically identify movement compensations based on stroke patients' data. This model can be adapted for use in in-clinic and at-home exercise programs for patients after stroke.

A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot—methodology and usability testing

“…(iii) Fatigue management: Since stroke patients experience frequent fatigue [ 7 , 8 ] and muscle weakness, patients should have the ability to rest when needed. When the patient is fatigued and cannot complete the task without using undesirable compensatory movements [ 10 ], either the patient should rest, or the session should end. In the current system, in addition to offering the participant to rest or to pause when desired, we also added built-in stretching breaks.…”

“…That is, they sought feedback on their body movements as they performed the task, whether they involved any compensatory movements, in addition to their task performance. We are currently in the process of developing this capability [ 10 ]. (v) Personalization of the system and of the interaction: The value in adapting the rehabilitation program to the personal needs of the patient was also stressed by the participants in our study, who mentioned the importance of personalizing the design of HRI and human–computer interaction (HCI) and tailoring it to the specific task and patient needs.…”

“…However, intensive practice, which requires a large number of repetitions, is challenging both for the patient and for the therapist [ 4 , 6 ], for a variety of reasons, including the limited time in the individual therapy sessions dedicated to both acquiring and practicing new abilities, the fatigue of the patient [ 7 , 8 ] and the lack of motivation of the individual with stroke to keep on training alone [ 9 ]. Therefore, it is imperative to devise feasible, alternative methods for long-term rehabilitation [ 4 ], which do not depend solely on the availability of the therapist, to be used both in the rehabilitation center and in patients’ homes [ 10 ]. These methods need to promote and motivate patients to practice their exercise, in order to improve the function of the impaired arm [ 4 ].…”

A Robot Goes to Rehab: A Novel Gamified System for Stroke Rehabilitation using a Socially Assistive Robot: Methodology & Feasibility Testing

“…(3) Fatigue management: Since stroke patients experience frequent fatigue [8,9] and muscle weakness, patients should have the ability to rest when needed. When the patient is fatigued and cannot complete the task without using undesirable compensatory movements (10), either the patient should rest, or the session should end. (4) Feedback and Reward: Users need to receive feedback on their performance and on their results, as this is an essential component of their motor learning (50).…”

“…That is, they sought feedback on their body movements as they performed the task, whether they involved any compensatory movements, in addition to their task performance. We are currently in the process of developing this capability (10). (5) Personalization of the system and of the interaction this point is discussed at length in Sect.…”