Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom
Identifieur interne : 001957 ( Main/Merge ); précédent : 001956; suivant : 001958Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom
Auteurs :Source :
- JMIR mHealth and uHealth [ 2291-5222 ] ; 2013.
Abstract
Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment.
The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation.
We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view.
We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used.
The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position.
Url:
DOI: 10.2196/mhealth.2539
PubMed: 25098265
PubMed Central: 4114461
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom</title></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25098265</idno><idno type="pmc">4114461</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4114461</idno><idno type="RBID">PMC:4114461</idno><idno type="doi">10.2196/mhealth.2539</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">002075</idno><idno type="wicri:Area/Pmc/Curation">002075</idno><idno type="wicri:Area/Pmc/Checkpoint">001079</idno><idno type="wicri:Area/Ncbi/Merge">003187</idno><idno type="wicri:Area/Ncbi/Curation">003187</idno><idno type="wicri:Area/Ncbi/Checkpoint">003187</idno><idno type="wicri:Area/Main/Merge">001957</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom</title></analytic><series><title level="j">JMIR mHealth and uHealth</title><idno type="eISSN">2291-5222</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec sec-type="background"><title>Background</title><p>Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment.</p></sec><sec sec-type="objective"><title>Objective</title><p>The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation.</p></sec><sec sec-type="methods"><title>Methods</title><p>We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view.</p></sec><sec sec-type="results"><title>Results</title><p>We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used.</p></sec><sec sec-type="conclusions"><title>Conclusions</title><p>The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Fahn, S" uniqKey="Fahn S">S Fahn</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Macht, M" uniqKey="Macht M">M Macht</name></author><author><name sortKey="Kaussner, Y" uniqKey="Kaussner Y">Y Kaussner</name></author><author><name sortKey="Moller, Jc" uniqKey="Moller J">JC Möller</name></author><author><name sortKey="Stiasny Kolster, K" uniqKey="Stiasny Kolster K">K Stiasny-Kolster</name></author><author><name sortKey="Eggert, Km" uniqKey="Eggert K">KM Eggert</name></author><author><name sortKey="Kruger, H P" uniqKey="Kruger H">H-P Krüger</name></author><author><name sortKey="Ellgring, H" uniqKey="Ellgring H">H 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