Archivio Istituzionale della RicercaResearch Objectives To investigate the suitability of a machine learning algorithm based on data collected using two wearable 3-axis accelerometers to predict the total Functional Ability Scale (FAS) score during the performance of a battery of motor tasks taken from the Wolf Motor Function Test (WMFT).
Estimating Clinical Scores From Wearable Sensor Data In Stroke Survivors / Meagher, Claire; Sapienza, Stefano; Adans-Dester, Catherine; O’Brien, Anne; Patel, Shyamal; Vergara-Diaz, Gloria; Demarchi, Danilo; Lee, Sunghoon; Hughes, Ann-Marie; Black-Schaffer, Randie; Burridge, Jane; Zafonte, Ross; Bonato, Paolo. - In: ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION. - ISSN 0003-9993. - 98:10(2017), p. e65. [10.1016/j.apmr.2017.08.202]
Estimating Clinical Scores From Wearable Sensor Data In Stroke Survivors
Sapienza, Stefano;Demarchi, Danilo;
2017
Abstract
Research Objectives To investigate the suitability of a machine learning algorithm based on data collected using two wearable 3-axis accelerometers to predict the total Functional Ability Scale (FAS) score during the performance of a battery of motor tasks taken from the Wolf Motor Function Test (WMFT).
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https://hdl.handle.net/11583/2702994
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