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

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.

Keywords: Freezing of Gait; Parkinson disease; context-aware system; indoor localization; person orientation.

Figure 1

Block diagram for the concept of the ubiquitous monitoring system. The wearable system independently detects FoG based on inertial data (blue rectangle). Gait-based detection is complemented by the user's spatial context from the vision sensor system (red rectangle) in the areas of the home where such a system is present.

Figure 2

Example of a test bed with two scenes being independently covered by Kinect sensors. Mock-up of a living room on the left and a dining room on the right. Images in the top row depict the point-of-view of the cameras when they are mounted in the overhead position. The bottom row displays colored point clouds of scenes that are obtained from depth sensing. Green trapezoid indicates the area in which it is possible to track people.

Figure 3

Workflow diagram for FoG detection using the distributed sensor system.

Figure 4

Frame definitions. a) Smartphone reference axes. b) Smartphone in the correct predetermined orientation at the expected position and orientation on the waist. c) Smartphone in the non-expected position and orientation on the waist. There is an angle of error in the transverse body plane between the device's real (green arrow) and expected (yellow arrow) orientation.

Figure 5

Overhead view of the relations between the different frames in the system.

Figure 6

The top row shows eight headings for one person at the same position in reference to the camera. The bottom row contains examples of related height templates used in orientation classification with neural network.

Figure 7

Coordinate frames in the process of fusion of vision and inertial information for orientation estimation. a) The moment in time when the external heading reference is available. b) Using the calculated correction angle to get person's heading at times when only the inertial orientation estimation is available.

Figure 8

The experiment venue. Markers on the floor indicate the start and end points and numbered reference points for standing in a predefined orientation. Additional markers also show which part of the area is covered by which Kinect device.

Figure 9

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

Figure 10

Schematic of marker positions and numbering for walks starting from the right side.