We perform a variety of experiments to analyse movement, but also to record other health states with wearable technology.
Home monitoring studies
Clinical measurements allow only snapshots of patients’ mobility over the disease course. As responses to treatments can usually not be captured in singular visits, more frequent diagnostic updates may be desired. Therefore, we acquire and analyze data from the patient’s real-world home environment. This has become feasible due to the advance of lightweight and easy to use mobile sensor-based systems that can be worn unobtrusively in everyday life.
Tests of gait behavior, such as variability and smoothness of motion, could potentially provide information related to disease progression. These gait test are especially relevant to diseases characterized by gait impairment. We are currently testing the applicability of these tests in preclinical studies using rodents. We develop computational methods to analyze rodent behavior in collaboration with preclinical laboratories. Specifically, these computational methods are used to analyze data collected from a gait analysis system, called CatWalk.
Biomechanical gait analysis
Biomechanical gait analysis provides information about internal variables that describe the motion (kinematics or joint angles) and forces (kinetics or joint moments and muscle forces). This information is used to understand gait, for example the effect of a new prosthesis or running shoe on someone’s gait. For example, the forces provide insight in the risk of injury due to muscle or joint overload, but also for example the energy expenditure of specific muscles.
Biomechanical gait analysis is commonly performed using optical motion capture. This is a lab-based method to measure motion using infrared cameras, reflective markers, and force plates. The cameras track reflective markers placed on anatomical landmarks. The resulting three-dimensional marker positions are used compute joint angles of the motion. Force plates, either placed in the ground or in and instrumented treadmill, are used to measure ground reaction forces. These, together with the joint angles, are required to calculate the joint moments and muscle forces. Optical motion capture requires expensive experiments, and is limited to a lab-based environment.
Our lab has also developed inertial motion capture. Then, the motion capture is performed using inertial measurement units (IMUs), which measure velocity and acceleration in three dimensions. Motion capture with IMUs is challenging due to the sensor noise and drift, but these can be overcome by combining the measurements with a musculoskeletal dynamic model of the human, which ensures that the resulting motion is realistic. IMUs are cheap, wearable sensors, which allows motion capture to be performed in the natural environment outside the lab.
Analysis of Human Stress Systems
We want to analyze the human stress systems and their responses to repeated acute stress to better understand the long-term effects of stress. Acute stress can be considered adaptive while others might be maladaptive. For that reason, we conduct experiments where we expose subjects to acute stress, such as the Trier Social Stress Test (TSST), the Montreal Imaging Stress Task (MIST) or the Stroop Room (a virtual reality implementation of the Stroop Color and Word Test). Alongside, we collect markers of the sympathetic nervous system (salivary alpha-amylase, heart rate and heart rate variability), of the hypothalamic-pituitary-adrenal (HPA) axis (salivary cortisol) and inflammation biomarkers. We aim to identify which psychological processes determine or moderate adaptation of biological stress response systems using state-of-the-art signal processing and machine learning techniques and to analyze whether different stress responses predict long-term health effects.