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Graduate Exam Abstract


Saket Doshi

M.S. Final

August 16, 2011, 4.00 pm

Engineering B105

APPLICATIONS OF INERTIAL MEASUREMENT UNITS IN MONITORING REHABILITATION PROGRESS OF ARM IN STROKE SURVIVORS


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