Our Goal for mStroke

Our goal has been to establish a remote extended monitoring and mobile health system for risk-related stroke measures to proactively provide patients, caregivers, and health professionals with previously unavailable real-time data at the body structure, activity, and participation levels, whereby patient compliance and progress can be monitored and rehabilitation and/or medical intervention may be triggered to support stroke patients’ optimal long-term recovery.

 

About mStroke

mStroke is a real-time quantitative assessment of stroke rehabilitation using wireless sensors. mStroke system will evaluate recovery of post-stroke patients after they leave the hospital, and will provide trustworthy customized activity analysis and statistical interpretation to support health care providers in delivering improved health services beyond usual stroke care. We anticipate that this system will have a significant impact on stroke rehabilitation (intervention and research) and patients’ long-term recovery. mStroke monitors and evaluates motor control, fall risk, and gait speed of patients post stroke using wearable Bluetooth Low-Energy (BLE) devices.

figures showing the concept of the mStroke application and mobile interface of the app
 

* Joint project with Dr. Li Yang (Computer Science) and Dr. Nancy Fell (Physical Therapy)

 

Functional Reach Test (FRT)

The FRT function in mStroke has been tested independently on two groups of healthy adult subjects, totaling 40 people in all. In the first group, only one NODE (positioned on the chest) is used to estimate trunk flexion and torso twist angles. In terms of Mean Absolute Error (MAE), the performance of the FRT function with consideration of trunk flexion and torso twist is 2.93cm in comparison to the clinical benchmark. In the second group, two NODEs are utilized to estimate trunk flexion, torso twist, and thigh movement angles. The performance using two NODEs can be improved by 17.6% compared with the performance using one NODE.

  Austin Harris performing a functional reach test

Performance comparison between clinical benchmark and mStroke 

 

 

NIHSS (Motor Arm/Motor Leg)

The National Institute of Health Stroke Scale (NIHSS) is a widely used tool for clinical evaluation of post-stroke patients that is designed to be a quick and reliable measurement of post-stroke patient capabilities. It is comprised of 15 items which are used to evaluate the effect of acute cerebral infarction on the levels of consciousness, language, neglect, visual-field loss, extraocular movement, motor strength, ataxia, dysarthia, and sensory loss. mStroke is designed to administer two of the items from of the NIHSS Stroke Scale: Motor Arm and Motor Leg. Both of these clinical measures center around a patient’s movement and thus are perfect candidates for the mStroke system. Results on 60 subjects each doing 4 tests are shown below.

 

    ma2               MotorLef

 

Fall Recognition

To initiate fall recognition in mStroke, we have acquired motion data from 14 healthy subjects. Each subject was asked to perform a total of 21 activities randomly chosen from three activities of daily living (reaching up, reaching down, and walking) and four falls with different directions. We demonstrate recognition performances based on different feature selection approaches, supervised learning algorithms, sensor configurations, and the feature numbers. All the recognition accuracies are above 90%.

 

Fall recognition results table

 

Gait

An important part of physical rehabilitation, especially for stroke patients, is determining how well they walk, known as Gait Analysis. In a typical Gait test, the patient is instructed to walk a set distance at a comfortable speed, using whatever walking implements they find necessary. This walk is timed, and the time is compared to known, normative data for patients of a similar class. Other interesting features may be examined as well: step symmetry (is one leg doing more work than the other?), cadence (how quickly are steps taken?), stride and step length, and so on.