Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection

Freezing of gait (FOG) is a disabling symptom commonly occurring in later stages of Parkinson? s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOGare aggravated mobility and higher a nity to falls, which have a direct e fect on the quality of life of the individual. There does not exist completely e fective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the oor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal e fectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this speci c user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection can be achieved by using contextual information of the patient? s position, orientation, basic posture and movement on a semantically annotated twodimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsof Kinect cameras placed in the patient? s home, that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development e forts involved the production of sof ware modules (for position tracking, orientation tracking, activity recognition) that run on top of themiddle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this sof ware module also provides mapping and semantic annotation of FOG speci c zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one speci c person - the patient. To enable tracking of the patient between di ferent non-overlapped cameras in the distributed system, a new re-identi cation approach based on appearance model learning with one-class Support VectorMachine (SVM) was developed. Evaluation of the re-identi cation methodwas conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera.