2016
DOI: 10.1249/mss.0000000000000842
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Decision Trees for Detection of Activity Intensity in Youth with Cerebral Palsy

Abstract: Purpose To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP); and 2) compare the classification accuracy of the new DT models to that achieved by previously published cut-points for youth with CP. Methods Youth with CP (GMFCS Levels I - III) (N=51) completed seven activity trials with increasing PA intensity while wearing a po… Show more

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Cited by 55 publications
(61 citation statements)
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“…More generally speaking, the present study is also limited from the algorithmic point of view, not taking yet into consideration the growing and promising area of Machine Learning and AI techniques to classify and assess movements and performance from sensors' readings. These are perspectives that of course deserve to be further studied, having already produced interesting results in the field (Ahmadi et al 2018;Hagenbuchner et al 2015;Trost et al 2016). The difficulty however is also related to the freedom of the performed movements, since the tasks of each exercise could be accomplished by different subjects in completely different ways, according to the nature and severity of each child's pathology.…”
Section: Discussionmentioning
confidence: 98%
“…More generally speaking, the present study is also limited from the algorithmic point of view, not taking yet into consideration the growing and promising area of Machine Learning and AI techniques to classify and assess movements and performance from sensors' readings. These are perspectives that of course deserve to be further studied, having already produced interesting results in the field (Ahmadi et al 2018;Hagenbuchner et al 2015;Trost et al 2016). The difficulty however is also related to the freedom of the performed movements, since the tasks of each exercise could be accomplished by different subjects in completely different ways, according to the nature and severity of each child's pathology.…”
Section: Discussionmentioning
confidence: 98%
“…Participants were asked to wear a wGT3X-BT triaxial accelerometer (ActiGraph, Pensacola, FL) for 7 days to objectively assess PA. This accelerometer was chosen as it has shown to be a reliable and valid measure of habitual physical activity in ambulant children and adolescents with CP [17,18]. The monitor was worn on the waist above the right hip or least affected side in the case of significant asymmetry, in the midaxillary line.…”
Section: Testing Proceduresmentioning
confidence: 99%
“…The wGT3X-BT accelerometer generates a variable output voltage signal, proportional to acceleration in three orthogonal planes (vertical, anteroposterior and mediolateral), which are converted to vector magnitude activity counts that are then stored on the device. PA was classified time spent in sedentary behaviour, light physical activity (LPA) and MVPA, using previously validated cut-points for adolescents with CP [18] per day was calculated by dividing total time in each intensity by the number of days on which the accelerometer was worn. TPA was calculated by summing time in LPA and MVPA.…”
Section: Testing Proceduresmentioning
confidence: 99%
“…93,115 In this regard, recent studies involving ambulatory youth with CP (GMFCS I – III) have demonstrated good concurrent validity between accelerometry data and indirect calorimetry while performing standardized physical activities of varying intensity 119 and shown that GMFCS-specific intensity-based cutpoint thresholds produce more accurate assessments of MVPA levels compared to previously published cutpoint values. 120 …”
Section: Health Promotion and Secondary Prevention In Pediatric Neuromentioning
confidence: 99%