You somehow think predicting failure to 100% recover walking ability is useful and comforting to your survivor? Will you stop with prediction research and just do research that provides recovery?
Wearable sensors improve prediction of post-stroke walking function following inpatient rehabilitation
JTEHM-00086-2022 1
Abstract:
Objective: A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients
reintegrate safely in the home and community.(And this research does nothing for that) However, not all patients have the same potential for recovery, making it difficult
to set realistic therapy goals(Yeah, using the tyranny of low expectations so you never have to explain why you can't get them 100% recovered) and to anticipate future needs for short- or long-term care. The objective of this study was to test the
value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation.
reintegrate safely in the home and community.(And this research does nothing for that) However, not all patients have the same potential for recovery, making it difficult
to set realistic therapy goals(Yeah, using the tyranny of low expectations so you never have to explain why you can't get them 100% recovered) and to anticipate future needs for short- or long-term care. The objective of this study was to test the
value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation.
Method:
Supervised machine learning algorithms were trained to classify patients as either
household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments.
Results:
household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments.
Results:
Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%. Conclusions: This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors.
Clinical Impact:
Accurately predicting a patient’s functional recovery early in the rehabilitation process
would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment
would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment
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