Protahování nohou pomocí exoskeletonu na vyžádání pro lidi se spasticitou

People with neurological conditions, including individuals with upper motor neuron injury, experience motor and sensory deficits, muscle weakness, limited range of motion, low weight-bearing capacity, and impaired balance that limit ambulation and interfere with the ability to perform activities of daily living. Lower-limb rehabilitation robots can assist with physical therapy and alleviate the burden of caregivers and nurses. Thus, the goal of this project is to develop wearable and portable technology to assist leg motion and stretching, and correspondingly activate muscles in people with neurological conditions who experience spasticity.

Spasticity results from increased muscle tone and interferes with the ability to functionally use voluntary muscle contraction for limb coordination and range of motion, which limits body transfers, ambulation, and exercise. Severe or intractable spasticity can lead to loss of body control and balance, resulting in falls and injuries, pressure injury of the skin, contractures, pain, wheelchair seating difficulties, in addition to other problems. Existent treatments to manage spasticity and overcome functional deficits related to spasticity include medications, neurosurgery, whole body or limb vibration, physical therapy, passive cycling, functional electrical stimulation (FES), along with other methods. However, medications may induce significant side effects including drowsiness, malaise, muscle weakness and pain (e.g., at the injection site); in addition, their effectiveness is sensitive to dosing fluctuations. Neurosurgery and direct spinal cord stimulation can treat intractable or focal spasticity, but they carry surgical risks, and their long-term benefits vary across individuals.

Stretching can decrease the excitability of motor neurons, maintain the viscoelastic properties of muscles and joints, provide relief from muscles spasms, and improve range of motion and gait function; further, stretching can be combined with oral medications to manage spasticity. Stretching is performed routinely by therapists, nurses, and caregivers to provide muscle stretching or preserve range of motion in joints with relatively low adverse effects. However, providing on-demand, reliable, manual limb stretching throughout the day and night imposes a heavy burden on caregivers and nurses both at home and in medical facilities. Hence, a critical need exists to develop a wearable approach to applying on-demand, safe, and customized dynamic stretching to manage spasticity after SCI, which can minimize the burden of caregivers and nurses.

This project exploits the recent technological advances in wearable sensors and fully wearable exoskeletons with reduced form factor and weight, which the investigators have not exploited (nor developed) in our previous research protocols at Syracuse University. These novel wearable exoskeletons are smart garments that conform to the human body and provide leg assistance in people with muscle weakness or hemiplegia. Light-weight exoskeletons have the potential to expand the breadth of tasks and environments in which exoskeletons are used for. Hence, this project introduces a lightweight, wearable exoskeleton to assist leg motion and stretching (e.g., targeting people with spasticity) under different postures (e.g., while lying down on a mat or bed, sitting on a chair or in a wheelchair).

Aim 1 characterizes the performance of the wearable device and its closed-loop control algorithm to apply precise adjustments for safe, automatic limb stretching and motion of single joints (e.g., joints in isolation while sitting or lying on a mat). The methods in this aim include the following: 1) design a control algorithm to apply safe leg forces exploiting joint kinematics and inertial feedback data using wearable sensors and 2) examine the magnitude and timing of the applied forces to the hip, knee and ankle joints, and toes. The research tasks in this aim will enable the customization of the applied forces across participants.

Aim 2 expands the implementation of the closed-loop controller from Aim 1 to different body postures and activities that involve multi-joint control. Participants wear the device to experience the applied forces by the wearable exoskeleton while lying down on a bed/mat or sitting down. Since the device is fully wearable, it serves as a powerful tool to examine the motion and limb-stretching of individuals with neurological conditions and spasticity. Thus, the device is not limited to fixed/stationary experimental conditions.

The last aim collects qualitative data from participants, caregivers, nurses, and clinicians (if involved) about the ease of use and satisfaction from using the wearable device. Questionnaires and surveys are used to gather qualitative data. Ease of use of the wearable device is assessed by examining if the protocol can be implemented within the expected duration including the time to don and doff the exoskeleton; further, it will be determined if the adherence to the protocol is achieved for all participants.

Research Design- Methodology

A candidate for the study will be asked to complete a Screening Questionnaire by phone or in person to check for eligibility. If the potential participant is eligible to enroll in the study, the participant will receive more information regarding the enrollment process. Participant will then complete a Demographics Questionnaire and answer questions about personal health and physical functioning.

Each lab visit may take up to 2 hours (including fitting the device, warm-up, testing procedures, rest breaks, unfitting the device, and removing the electrodes and wearable sensors).

Raw data will be collected from wearable sensors including the following: joint angles, 3D leg motion, muscle electromyography (EMG), and motor currents from the wearable device. Digital data recorded from experiments will be stored in .mat files (or .csv), password protected. There are no biological specimens collected.

Metadata will be applied to each component of the dataset following acquisition to ensure data is prepared for proper archiving. Metadata will include type, format, date, time, storage location, and context of data. Data generated during this project use conventional digital formats including:

• MS Word, Power Point, Excel, Portable Data Format (PDF), and LaTeX (tex) files
• Graphics, figures, tables, animations, videos from tests (jpeg, png, eps, mp4 files)
• Code including MATLAB/Simulink files, C++ code, and ASCII text files

The paper participation records will be kept in locked file cabinets in the laboratory and/or offices, and the digital data will be stored on password-protected computers/servers or encrypted electronic storage devices in the offices and laboratory of the PIs' research team.