Ambulatory Movement and Activity Analysis for Patients with Neurological Disorders Based on Wearable Inertial Sensors

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The assessment of human motion is central in the diagnosis and treatment of neurological disorders. Understanding patient-specific motion patterns is vital for physicians to diagnose diseases, to assess their severity and to evaluate the effects of medication. However, pure visual motion inspection, currently common practice, is subject to limited precision. It depends heavily on the experience of the physician and the ability of patients and caregivers to describe their observations. Paroxysmal symptoms, like epileptic seizures, often happen without notice of others and the patients themselves are often amnestic for their seizures. Camera-based motion analysis systems have greatly enhanced the information that can be derived from a single seizure. However, their setup typically requires a stationary, clinical environment. Therefore, these systems do not permit to analyze patient movements in their everyday, ambulatory routine over extended periods of time. Valuable information is thus not accessible for physicians and disease symptoms may remain unevaluated.The aim of this project is to develop a motion analysis system for neurological diseases that allows continuously acquiring and evaluating motion data of patients during their everyday life. A key research focus is to recognize patient-specific movement patterns based on wearable inertial sensors attached to the body. Methods for learning patient-specific motion models will be developed that allow for an automatic recognition of crucial events, such as seizures, and that can reconstruct the patient movements from the sensor data for a later inspection by a physician. The information captured by inertial sensors on a patient's body is very limited, as compared to using video cameras. Therefore, the information derived from the inertial sensors will be combined with learned, prior knowledge on human motion. For this purpose, a training setup will be implemented where high-detail movement data for each individual patient is recorded simultaneously with the inertial sensors and an optical motion capture system. The motion capture data will be used to learn the relationship between the sensor measurements and the patient¿s movements in 3D. Previous research has demonstrated the feasibility of using inertial sensors for recognition of human activities and for body pose estimation, assisted by person-specific motion models. We will concentrate on three neurological diseases: Epilepsy, Parkinson¿s Disease (PD) and Multiple Sclerosis (MS). For epilepsy, motor seizures will be focused and recorded in an established clinical setting for pre-surgical epilepsy monitoring. In the case of PD, the system will be used to learn and recognize patient-specific motor control issues (e.g. tremor, hyperkinesia) that can be caused by suboptimal medication. For MS patients, the system will learn patient motions during diagnostic movement sequences for a long-term assessment of patient capabilities.