Abstract
Background: Stroke survivors usually experience partial disability, due to abnormal gaits, which vary widely and require tailored rehabilitation programs. However, most gait classifications are based mainly on clinical assessments, which can be influenced by the therapist’s experience. Inertial measurement units (IMUs) are devices that combine accelerometers and gyroscopes to detect movement. IMUs have been successfully used for assessing gait characteristics. Here, we aimed to develop a Deep Neural Network (DNN) model that incorporated information from a motion capture system and multi-labeling IMUs information. This DNN was developed to recognize individual gait patterns in patients affected by stroke to facilitate the design of suitable rehabilitation strategies and promote functional recovery.Methods: We recruited ten patients, aged 20–75 years, with a first-ever, unilateral, ischemic stroke, which caused mild to moderate leg paresis 4 weeks after stroke and ten neurologically normal healthy controls. We applied a motion capture system integrated with multi-label IMUs to acquire the gait information. The motion capture system measured gait information by detecting movement of LED markers attached to each participant. In addition, the IMUs were attached to each participant’s lower limbs to measure kinematic data. These measurements were then applied to the development of a DNN model that could recognize gait characteristics in patients after a stroke and in normal controls.Results: The DNN model achieved an average accuracy of 98.28% in differentiating the stroke gait from the normal gait. Among patients with stroke, the DNN model had an average accuracy of 96.86% in classifying the gait abnormality as either a drop-foot gait or a circumduction gait. We also applied a publicly available dataset, the Physical Activity Monitoring Data Set, which contained IMU information from another independent set of participants to validate our DNN model. We found an average accuracy of 98.60%.Conclusions: We developed a DNN model based on integrated information from a motion capture system and multi-label IMU inputs. This model might assist clinicians and therapists in identifying abnormal gaits more accurately and in applying suitable training programs within the “golden time window” of rehabilitation, after the onset of stroke.
Human Gait Cycle Analysis Using Kinect V2 Sensor