Abstract: Constraint Induced Movement Therapy (CIMT) has been clinically proven to
be effective in restoring functional abilities of the affected arm among
stroke survivors. Current CIMT delivery method lacks a robust technique
to monitor rehabilitation progress, which results in increasing costs of
stroke related health care. Recent advances in the design and
manufacturing of Micro Electro Mechanical System (MEMS) inertial sensors
have enabled tracking human motions reliably and accurately. This thesis
presents three algorithms that enable monitoring of arm movements during
CIMT by means of MEMS inertial sensors.
The first algorithm quantifies the affected arm usage during CIMT. This
algorithm filters the arm movement data, sampled during activities of
daily life (ADL), by applying a threshold to determine the duration of
affected arm movements. When an activity is performed multiple times,
this algorithm counts the number of repetitions performed. Current
technique uses a touch/proximity sensor and a motor activity log
maintained by the patient to determine CIMT duration. Affected arm motion
is a direct indicator of CIMT session and hence this algorithm tracks
rehabilitation progress more accurately. Actual patients’ affected arm
movement data analysis shows that the algorithm does activity detection
with an average accuracy of >90%.
Second of the three algorithms, tracking stroke rehabilitation of
affected arm through histogram of distance traversed, evaluates an
objective metric to assess rehabilitation progress. The objective metric
can be used to compare different stroke patients based on their
functional ability in affected arm. The algorithm calculates the
histogram by evaluating distances traversed over a fixed duration window.
The impact of this window on algorithm’s performance is analyzed. The
algorithm has better temporal resolution when compared with another
standard objective test, box and block test (BBT). The algorithm
calculates linearly weighted area under the histogram as a score to rank
various patients as per their rehabilitation progress. The algorithm has
better performance for patients with chronic stroke and certain degree of
functional ability.
Lastly, Kalman filter based motion tracking algorithm is presented that
tracks linear motions in 2D, such that only one axis can experience
motion at any given time. The algorithm has high (>95%) accuracy. Data
representing linear human arm motion along a single axis is generated to
analyze and determine optimal parameters of Kalman filter. Cross-axis
sensitivity of the accelerometer limits the performance of the algorithm
over longer durations. A method to identify the 1D components of 2D
motion is developed and cross-axis effects are removed to improve the
performance of motion tracking algorithm.
Adviser: Dr. Anura Jayasumana Co-Adviser: Dr. Matthew Malcolm Non-ECE Member: Dr. Yashwant K. Malaiya Member 3: Dr. Sudeep Pasricha Addional Members: N/A,N/A
Publications: N/A
Program of Study: ECE450/451 ECE554 ECE571/575 ECE534/535 ECE695 ECE699 N/A N/A
