Stroke-induced hemiparesis is associated with loss of mobility and independence, preventing survivors from participation in activities of daily living. Survivors can recover motor function of their paretic limbs by adhering to a rehabilitation regimen, consisting of repetitive, high-intensity exercises. In telerehabilitation, information and communication technologies are leveraged to deliver such physical therapy to patients’ homes. However, monitoring motor performance remotely remains a challenging task, especially in light of high variability of motor impairments among patients. In order to evaluate motor performance, therapists require the aid of technicians, who would analyze sensor data and produce meaningful metrics. The therapists would then provide patients with feedback, after a few days at best. To automate this process and offer patients real-time feedback, we propose to train machine learning algorithms that detect impaired movements. We test this approach with ten healthy participants who interact with a low-cost telerehabilitation platform we have previously developed. The platform engages users in bimanual training, where movement of the affected arm is supported by the unaffected arm, and relies on a Microsoft Kinect sensor to record user movement. We report the accuracy of a classification algorithm in distinguishing movements of simulated of disability from normal ones. This effort constitutes a significant step toward programmed assessment of upper-limb movements in authentic telerehabilitation paradigms.
Stroke-induced motor impairment often prevents survivors from participating in activities of daily living, adversely impacting their quality of life. Desktop delta robots such as the Novint Falcon have been utilized in various home-settings to help recover fine-motor skills. They are compact and affordable, and can provide programmable sensorimotor feedback. In spite of these favorable features, it is presently not possible to directly measure the user’s wrist angles while interacting with these robots, which undermines their prospective use in telerehabilitation as patients’ motor performance cannot be reliably assessed. Here, we propose an experimental set-up where patients strap a smartphone device to their forearm and manipulate a haptic robot. In this setting, data from inertial sensors embedded in the smartphone will be integrated with data from the robot in a classification algorithm that infers the wrist angle. To study the viability of this approach, we perform experiments with one healthy user. We fix two inertial measurement units on their body, one on their forearm and one on the back of their hand, to measure the true wrist angle as they perform a motor task with a Novint Falcon device. We train a machine learning algorithm that predicts wrist angles from a single wearable sensor and the Novint Falcon movements. This effort constitutes a step toward automatic assessment of wrist movements in fine motor telerehabilitation and could enable real-time feedback in the absence of a therapist.
Stroke survivors commonly experience unilateral muscle weakness, which limits their engagement in daily activities. Bimanual training has been demonstrated to effectively recover coordinated movements among those patients. We developed a low cost telerehabilitation platform dedicated to bimanual exercise, where the patient manipulates a dowel to control a computer program. Data on movement is collected using a Microsoft Kinect sensor and an inertial measurement unit to interface the platform, as well as to assess motor performance remotely. Toward automatic classification of bimanual movements executed by the user, we test the performance of a linear and a nonlinear dimensionality reduction techniques.
Biomimetic robotics is emerging as a promising research tool in the study of animal behavior, providing highlycontrollable and customizable stimuli in laboratory experiments and field trials. Here, we introduce a novel robotics-based approach to study predator-prey interactions in fish. Our animal model, zebrafish, is gaining traction as a species of choice for investigations of fear and anxiety in preclinical research. The platform integrates three-dimensional real-time tracking, four-degree-of-freedom robotic manipulation, and data-driven Markov chains to allow for unprecedented, interactive experiments on zebrafish.
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