The TIM motion lab is located in the main building of the Leiden University Medical Center, and offer the possibility to evaluate and treat various types of movement disorders. The lab studies the application new technological developments to develop and test novel measurement techniques that are potentially more accurate and less cumbersome for patients than current practice. Below you can find an overview of currently running projects in the lab.
Project Holocue: Smart Wearable Assistive Cueing Device for Reducing Freezing of Gait
Freezing of gait is a sudden inability to generate effective stepping in people with Parkinson’s disease. Cues, such as stripes on the ground, may help alleviate and sometimes even prevent freezing of gait. However, different people prefer different types of cues: some need 3D cues to step over, while others need 2D cues to step on. Recent technological breakthroughs have led to a prototype of a smart wearable assistive cueing device, called Holocue, which offers cueing options by presenting holograms. With Holocue, users can activate visual cues of their preference using voice commands. Holocue, which works indoors and outdoors, also captures data of users’ movement and environment. With these data, Holocue may predict when and where freezing of gait is likely to occur, followed by preventive automatic cue activation.
This proof-of-concept study consists of three parts. In part 1, we will explore the potential of Holocue with on-demand cueing for reducing freezing. This will be examined in both standardized laboratory and home settings. In part 2, we will test prediction models of freezing using Holocue movement and environment data. In part 3, we will improve the Holocue application based on the experience and feedback of the users in part 1 and the findings of part 2, before studying its potential for reducing freezing in free-living environments.
If we can demonstrate a positive effect of Holocue for reducing freezing in free-living environments, Holocue may become an aid for people with freezing of gait. This would improve quality of life, as no medication is currently available to alleviate this disabling motor symptom.
Markerless tracking of arm and hand movements
Within the NEURAS project, one the precursors of TIM, the possibilities of ‘markerless’ movement tracking devices were already explored. The setup comprises a kinectTM sensor for whole-arm movements combined with a 3Gear Systems ‘handtracker’ for finger movements. Task instructions are presented in a virtual environment displayed on a LED screen placed in front of the participant. This setup is used for the assessment of the reachable workspace and evaluation of goal-directed movements, while a cognitive dual task is administered to evaluate attentional load and task prioritization.
Within TIM this will be further developed and extended to include augmented reality (AR)for evaluation of reach-and-grasp tasks as well as pattern-recognition algorithms (for off-line post-processing of the raw RGB-data and depth data).
Markerless tremor assessment
Tremors are involuntary rhythmic movements of one or more body parts, typically visible in the arm, hand, leg, and/or head. Various types of disorders can cause tremors to occur, including Parkinson’s Disease (PD) and Cerebro Vasculair Accident (CVA). Accurately measuring tremor frequency and amplitude is key to successful diagnosis, and to evaluating treatments. Conventional tremor assessment techniques require placing physical sensors on the patient’s body, such as accelerometers (motion sensors) or EMG (sensors that measure muscle activity). This requires preparation time, expensive equipment, and an initial idea of where the tremors will manifest most prominently during the evaluation protocol.
The markerless tremor assessment techniques developed by the TU Delft in the TIM project aim to alleviate the need for specialized equipment and time consuming preparation, making assessment more widely accessible (e.g. at your general practitioner) and easier to perform. The developed approach uses video to record the subtle body movements, and specialized analysis software then computes the tremor parameters over the observed body surface. In effect, this markerless approach results in many `virtual’ markers placed all over the body, and can therefore also discover patterns in tremor activity than remain undiscovered by the traditional approaches that only measure a few body locations at the same time.
Hand/arm function with Augmented Reality
TU Delft joined in the Technology in Motion (TIM) research project to investigate the potential of the novel Augmented Reality (AR) technology to improve the assessment of motion disorders for different patient groups. Our aim is to use AR technology combined with serious gaming and automatic tracking of the hand and body to facilitate unobtrusive, cost-effective and patient-friendly methods for objective evaluation of upper extremity motor dysfunction. The tracking of the hand and body should be marker-less, i.e. no markers or other sensors should be attached to the patient’s body, as a way to insure natural movement of the patients during assessment sessions. Part of the project is the design of games that will entice the patient, by interacting with the AR environment, to make the movements that are currently used in motion disorder assessment sessions (e.g. pointing and reaching a target, reaching and grasping an object, determining the reachable workspace of the arm).
A single-joint haptic robot, the WristalyzerTM (MOOG, Nieuw-Vennep, The Netherlands) is incorporated in the TIM motion laboratory as part of the NEURAS project .
The haptic robot enables precise assessment of motor action in a variety of mechanical environmental conditions, while real-time visual feedback is provided in a virtual reality surrounding. This setup is used to separately assess the functioning of systems involved in motor control (i.e., motor, sensory, cognitive) and their functional integration in various contexts (i.e., using different tasks and systematic manipulations of the mechanical/visual environment) in order to understand how these factors contribute to activity limitations.
To quantify motor performance and the adopted control strategy, kinematic parameters (e.g., tracking accuracy and correctional actions) are related to underlying neuromuscular properties using a System Identification and Parameter Estimation technology in collaboration with the TU Delft [van der Helm et al. 2002, Schouten et al. 2003, 2008]. This technology comprises a series of precise position and force perturbations to a the wrist by a computer-controlled manipulator. The relation between the perturbations and the resulting reactions by the body, i.e. muscle activity, forces and positions, is quantified by the system identification technique and is subsequently broken down in physiologically meaningful parameters by neuromechanical modelling.
The Interactive Walkway
Falling during walking is a major social problem. Most falls occur due to incorrect foot placement with respect to irregularities in the walking surface, by which one trips, slips or loses balance. The risk of falls during walking increases when step adjustments have to be made under time pressure and/or in combination with a cognitive task. Currently, people are referred to a falls prevention program when they have already experienced a fall, but this reference is one fall too late. There is a need for an early detection method to assess the aforementioned risk factors and identify those at risk of falling to direct them to a targeted falls prevention program.
With the newly developed Interactive Walkway it is technically possible to assess the aforementioned risk factors for walking related falls in a quick, simple, objective and systematic manner. The Interactive Walkway is an instrumented augmented reality walkway and uses multiple Microsoft Kinect
sensors for motion registration. Kinect technology is a patient-friendly manner to register motions of a large number of body segments in real-time, without attaching markers or sensors to the body. The Interactive Walkway uses a projector which enriches the surface with meaningful visual objects, such as obstacles and targets. This visual context can be presented in a movement-dependent manner (real-time coupling between Kinect output and augmented reality). Therefore, an obstacle can be presented exactly at the predicted foot placement position of the next step. In addition to the ability to avoid obstacles, many other functional aspects of walking can also be assessed on the Interactive Walkway, such as the ability to turn under time pressure, the ability to make sudden stops, the ability to walk an imposed speed, the ability to accurately place the feet on targets, the ability to walk with a narrow base of support, the ability to perform a cognitive dual task while walking, etcetera. With the Interactive Walkway it is possible to assess walking of patients without the use of markers or sensors, but also to assess the ability to adapt walking to the presented augmented reality context.
The C-Mill is a treadmill that uses visual and acoustic cues for training and evaluation of impaired gait. The C-Mill allows for learning gait adaptability strategies through obstacle avoidance in a safe and controlled environment. Gait parameters like step length, width, frequency and symmetry are automatically registered, without the need for attaching markers or wires to the patient, thereby saving precious treatment time. The C-Mill: a complete, advanced gait-lab and training centre on 4m². Read more about the C-Mill on the product page at Motekforce Link.