title: Pull Test estimation in Parkinson's disease patients using wearable sensor technology.
abstract: Postural instability is one of the main motor impairment of Parkinson's disease (PD). The Pull Test is the most common clinical examination to assess postural instability in PD. However, the subjectivity and low discriminative power of this test presents as a major drawback. In this paper we propose a novel methodology to estimate the Pull Test scores from patients with PD. We capture the relationship between the Pull Test outcomes and patients' foot motion patterns, using wearable sensors mounted on their shoes. 139 idiopathic Parkinson's disease patients performed four motor function tests, including walking and repetitive foot motions, while acceleration and orientation data was recorded. A total of 684 features were extracted from the acceleration and orientation signals. Feature selection and classification algorithms were utilized to estimate the Pull Test score for each participant. Further, we estimate which motor function test would better predict the Pull Test score, depending on the patient's phenotype (i.e. bradykinetic, tremor-dominant or equivalent). When combining all phenotypes and all tests, the mean of the classification probability distribution achieved was 0.75 (CI: [0.69-0.82]). Foot circling was the best predictive test for the equivalent patients (mean = 0.79, CI: [0.69-0.87]) and the bradykinetic patients (mean: 0.75, CI: [0.64-0.85]), while 2×10 m. walk with stop-and-go proved superior for the tremor-dominant patients (mean: 0.75, CI: [0.64-0.85]). Overall, these results suggest that inertial data from patient's foot motion can be used to estimate postural instability in PD patients.
authors.forename: Cristian F
authors.initials: CF
authors.lastname: Pasluosta
authors.forename: Jens
authors.initials: J
authors.lastname: Barth
authors.forename: Heiko
authors.initials: H
authors.lastname: Gassner
authors.forename: Jochen
authors.initials: J
authors.lastname: Klucken
authors.forename: Bjoern M
authors.initials: BM
authors.lastname: Eskofier
mesh_terms: D000465:Algorithms; D019992:Databases as Topic; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D010827:Physiology / Q000295:instrumentation* / Q000379:methods*; D011336:Probability