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Methods Inf Med 2017; 56(02): 95-111
DOI: 10.3414/ME16-02-0013
REHAB
Schattauer GmbH

Combined Vision and Wearable Sensors-based System for Movement Analysis in Rehabilitation

Sofija Spasojević
1   School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
2   Mihailo Pupin Institute, University of Belgrade, Belgrade, Serbia
3   Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
,
Tihomir V. Ilić
4   Department of Neurophysiology, Medical Faculty of Military Medical Academy, University of Defense, Belgrade, Serbia
,
Slađan Milanović
5   Institute for Medical Research, Department of Neurophysiology, University of Belgrade, Serbia
,
Veljko Potkonjak
1   School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
,
Aleksandar Rodić
2   Mihailo Pupin Institute, University of Belgrade, Belgrade, Serbia
,
José Santos-Victor
3   Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
› Institutsangaben
Funding: This work is partially funded by the Ministry of Education, Science and Technology Development of the Republic of Serbia under the contracts TR-35003, III-44008, III-44004 and #ON175012. This work was partially funded by the EU Project POETICON++ and the Portuguese FCT Project [UID/EEA/50009/2013]. The work is complementary supported by the Alexander von Humboldt project ..Emotionally Intelligent Robots – EIrobots”, Contract no. 3.4-IP-DEU/112623.
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Publikationsverlauf

received: 06. März 2016

accepted: 22. Oktober 2016

Publikationsdatum:
25. Januar 2018 (online)

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Summary

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.

Objectives: 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.

Keywords

Rehabilitation - movement analysis - Kinect - wearable sensors