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Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom.

Takač B, Català A, Rodríguez Martín D, van der Aa N, Chen W, Rauterberg M - JMIR Mhealth Uhealth (2013)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politècnica de Catalunya - BarcelonaTech, Vilanova i la Geltrú, Spain. boris.takac@estudiant.upc.edu.

ABSTRACT

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Schematic of marker positions and numbering for walks starting from the left side.
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figure9: Schematic of marker positions and numbering for walks starting from the left side.

Mentions: The experimental condition was the sensor attachment position with two possibilities, Position1 with the smartphone fixed at the iliac crest on the left hip (see Figure 4b) and Position2 with the smartphone rotated between 50° and 60° around the waist and put on the frontal left side under the belly (see Figure 4c). Position1 is the expected sensor position for the method using the AOE algorithm, while Position2 is substantially deviating from the expected position for the same method. The second method using the GROE algorithm and video orientation classifier has no expected sensor position. The test for each sensor position was split into two walks, one walk with predominantly left turns (see Figure 9) and the other one with predominantly right turns (see Figure 10). Walks were designed with multiple consecutive turns in the same direction in order to induce possible orientation bias. Participants were instructed to walk to each marked position, where they were told to stand still for 3 seconds before continuing toward the next marked point (see Multimedia Appendix 2). The procedure was repeated for each subsequent point. Each test walk lasted around one minute. Each participant first did two walks for condition Position1, followed by two walks for condition Position2.


Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom.

Takač B, Català A, Rodríguez Martín D, van der Aa N, Chen W, Rauterberg M - JMIR Mhealth Uhealth (2013)

Schematic of marker positions and numbering for walks starting from the left side.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4114461&req=5

figure9: Schematic of marker positions and numbering for walks starting from the left side.
Mentions: The experimental condition was the sensor attachment position with two possibilities, Position1 with the smartphone fixed at the iliac crest on the left hip (see Figure 4b) and Position2 with the smartphone rotated between 50° and 60° around the waist and put on the frontal left side under the belly (see Figure 4c). Position1 is the expected sensor position for the method using the AOE algorithm, while Position2 is substantially deviating from the expected position for the same method. The second method using the GROE algorithm and video orientation classifier has no expected sensor position. The test for each sensor position was split into two walks, one walk with predominantly left turns (see Figure 9) and the other one with predominantly right turns (see Figure 10). Walks were designed with multiple consecutive turns in the same direction in order to induce possible orientation bias. Participants were instructed to walk to each marked position, where they were told to stand still for 3 seconds before continuing toward the next marked point (see Multimedia Appendix 2). The procedure was repeated for each subsequent point. Each test walk lasted around one minute. Each participant first did two walks for condition Position1, followed by two walks for condition Position2.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politècnica de Catalunya - BarcelonaTech, Vilanova i la Geltrú, Spain. boris.takac@estudiant.upc.edu.

ABSTRACT

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus