title: Posture classification via wearable strain sensors for neurological rehabilitation.
abstract: Stroke and other neurological accidents account for a wide fraction of the healthcare costs in industrialised societies. The last step in the chain of recovery from a neurological event often includes motor rehabilitation. While current motion-sensing technologies are inadequate for automated monitoring of rehabilitation exercises at home, conductive elastomers are a novel strain-sensing technology which can be embedded unobtrusively into a garment's fabric. A sensorized garment was realized to simultaneously measure the strains at multiple points of a shirt covering the thorax and upper limb. Supervised learning techniques were employed to analyse the strain measures in order to reconstruct upper-limb posture and provide real-time feedback on exercise progress.
authors.affiliation: Consorzio di Bioingegneria e Informatica Medica, Italy. toni.giorgino@unipv.it
authors.forename: Toni
authors.initials: T
authors.lastname: Giorgino
authors.forename: Federico
authors.initials: F
authors.lastname: Lorussi
authors.forename: Danilo
authors.initials: D
authors.lastname: De Rossi
authors.forename: Silvana
authors.initials: S
authors.lastname: Quaglini
mesh_terms: D000465:Algorithms; D003020:Clothing; D003198:Computer Simulation; D004867:Equipment Design; D015444:Exercise; D006801:Humans; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009422:Nervous System Diseases / Q000534:rehabilitation*; D011187:Posture; D011381:Programming Languages; D012984:Software; D013784:Textiles; D013909:Thorax / Q000473:pathology