Sensors, Vol. 21, Pages 7784: Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning

Sensors, Vol. 21, Pages 7784: Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning Sensors doi: 10.3390/s21237784 Authors: Johan Wasselius Eric Lyckegård Finn Emma Persson Petter Ericson Christina Brogårdh Arne G. Lindgren Teresa Ullberg Kalle Åström Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detection times between 15 min and 120 min. Motion data were collected using triaxial accelerometer bracelets worn on both arms for 24 h. Eighty-four stroke patients with unilateral arm motor impairment and 101 healthy subjects participated in the study. Accelerometer data were divided into data windows of different lengths and analyzed using multiple machine learning algorithms. The results show that all algorithms performed well in separating the two groups early enough to be clinically relevant, based on wrist-worn accelerometers. The two evaluated deep learning mod...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research