title: Combined Vision and Wearable Sensors-based System for Movement Analysis in Rehabilitation.
abstract: BACKGROUND
  Traditional rehabilitation sessions are often a slow, tedious, disempowering and non-motivational process, supported by clinical assessment tools, i.e. evaluation scales that are prone to subjective rating and imprecise interpretation of patient's performance. Poor patient motivation and insufficient accuracy are thus critical factors that can be improved by new sensing / processing technologies.
  
  
  OBJECTIVE
  We aim to develop a portable and affordable system, suitable for home rehabilitation, which combines vision-based and wearable sensors. We introduce a novel approach for examining and characterizing the rehabilitation movements, using quantitative descriptors. We propose new Movement Performance Indicators (MPIs) that are extracted directly from sensor data and quantify the symmetry, velocity, and acceleration of the movement of different body/hand parts, and that can potentially be used by therapists for diagnosis and progress assessment.
  
  
  METHODS
  First, a set of rehabilitation exercises is defined, with the supervision of neurologists and therapists for the specific case of Parkinson's disease. It comprises full-body movements measured with a Kinect device and fine hand movements, acquired with a data glove. Then, the sensor data is used to compute 25 Movement Performance Indicators, to assist the diagnosis and progress monitoring (assessing the disease stage) in Parkinson's disease. A kinematic hand model is developed for data verification and as an additional resource for extracting supplementary movement information.
  
  
  RESULTS
  Our results show that the proposed Movement Performance Indicators are relevant for the Parkinson's disease assessment. This is further confirmed by correlation of the proposed indicators with clinical tapping test and UPDRS clinical scale. Classification results showed the potential of these indicators to discriminate between the patients and controls, as well as between the stages that characterize the evolution of the disease.
  
  
  CONCLUSIONS
  The proposed sensor system, along with the developed approach for rehabilitation movement analysis have a significant potential to support and advance traditional rehabilitation therapy. The main impact of our work is two-fold: (i) the proposition of an approach for supporting the therapists during the diagnosis and monitoring evaluations by reducing subjectivity and imprecision, and (ii) offering the possibility of the system to be used at home for rehabilitation exercises in between sessions with doctors and therapists.
authors.affiliation: Sofija Spasojević, Mihailo Pupin Institute, Volgina 15, 11060 Belgrade, Serbia, E-mail: sofija.spasojevic@pupin.rs.
authors.forename: Sofija
authors.initials: S
authors.lastname: Spasojević
authors.forename: Tihomir V
authors.initials: TV
authors.lastname: Ilić
authors.forename: Slađan
authors.initials: S
authors.lastname: Milanović
authors.forename: Veljko
authors.initials: V
authors.lastname: Potkonjak
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Rodić
authors.forename: José
authors.initials: J
authors.lastname: Santos-Victor
mesh_terms: D000054:Acceleration; D000368:Aged; D000369:Aged, 80 and over; D003710:Demography; D015444:Exercise; D005260:Female; D006225:Hand / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009068:Movement; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology* / Q000534:rehabilitation*; D016059:Range of Motion, Articular; D012046:Rehabilitation / Q000295:instrumentation* / Q000379:methods*; D014785:Vision, Ocular; D014955:Wrist Joint / Q000503:physiopathology
keywords: Kinect; Rehabilitation; movement analysis; wearable sensors