F - Wearable Devices for Improving Spasticity Assessment
Wearable Devices for Improving Spasticity
Tina Ng (Contact PI）
Prof. Truong Nguyen
Overview: In this project, we will develop an instrumented glove worn by doctors to improve spasticity assessment in patients. “Spasticity” is a debilitating musculo-skeletal condition caused by Traumatic Brain Injury, Cerebral Palsy, Stroke, and other disorders. It manifests as a failure of the coordinated integration of peripheral sensations an central motor commands that are needed to produce smooth and fluid limb movements. Spasticity has several key characteristics, which aid in differentiation from other similar disorders. The increased tone of spasticity is velocity dependent – a faster stretch results in more resistance to movement. There is often a clasp-knife phenomenon with spasticity, where the spastic limb initially resists movement and then suddenly gives way like the resistance of folding a pocketknife blade. During initial movement, the tone is exaggerated due to the over active muscle stretch reflex. On sustained movement, the inverse muscle stretch reflex kicks in, relaxing the muscles with the resulting give away feel. Considerable clinical effort is directed toward relieving spasticity including intramuscular administration of botulinum toxin, spinal administration of baclofen, orthopedic surgery, etc. [1-3]. There are no good outcome measures for quantifying the relief (or lack thereof) resulting from such clinical interventions. The current “gold standard” is the modified Ashworth score shown in Table 1 [4,5]. It has several limitations to the scale including poor inter-rater reliability, and poor sensitivity to changes in spasticity [6-8].
Table 1 Original Ashworth Scale (2nd column) and modified Ashworth scale (3rd column)
The aims of this research are to (i) develop a prototype glove using commercial, off-the shelf (COTS) components; (ii) calibrate the glove and (iii) conduct a pilot study to compare the objective metrics from the glove data with subjective assessments by doctors.
Project Description and Milestones:
Figure 1 shows a COTS grip sensor with embedded force sensitive resistors (FSRs). It comes with two cuff modules to digitize the sensor data and communicate with a hub. The hub connects to a computer through USB . There are software includes tools to export data to MATLAB, LABView and C/C++ APIs. The students are responsible the following tasks:
(1) Familiarize with software tools; export sensor data to MATLAB; write a C program to print raw sensor data; document the software and make a presentation.
(2) Incorporate the sensors in 4 wearable gloves of two different sizes. You will be evaluated on the functionality and aesthetics of the prototype glove.
(3) Design a test sequence to calibrate the gloves against known resistances. (This may involve building a simple robotic testbed for automated testing, under consultation with Prof. Yip and Prof. Ng).
(4) Complete calibration on the 4 gloves against known resistances and present the results.
(5) [STRETCH GOAL for bonus marks]. You will work with Dr. Leanne Chukoskie of Research for Autism and Development Lab (RADLab) and Dr. Andrew Skalsky of Rady Childrens in a pilot data collection. You will recruit at least 4 pairs of volunteers to participate in the study. One volunteer will be a “mock-patient” one volunteer will be “mock-clinician”. The mock-patient will be asked adopt a range of muscle tone in the forearm and lower leg from completely passive to as resistive as possible. We will number 25 cards with 1-5 indicating requested rigidity and present cards pseudorandomly shuffled. Only the mock-patient participant will see the requested level of rigidity at the time of the assessment. The mock-clinician (wearing the glove) will not see the level. The order will have been pre-recorded on an experimental tracking sheet, however, so that the requested level of rigidity can be related to the measurements from the glove after the testing session. We will train the mock-clinician volunteers on the modified Ashworth scale and ask them to use it give an assessment of the level of resistance they observe. Upon successful completion, you will document the work in a format suitable for submission to a conference or a journal.
Required Skills: Prototyping, MATLAB and/or Python, C/C++. Strong interest in one or more of 2D signal processing, machine learning, medical devices and lab experiments.
Contact PI: Tina Ng, firstname.lastname@example.org
Other PIs: Hari Garudadri, Michael Yip, Leanne Chukoskie, Andrew Skalsky, Truong Nguyen
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