Continuous Parkinson's Disease Patient Monitoring

Continuous Parkinson’s Disease Patient Monitoring System Ava Hedayatipour, Rania Oueslati, Aysha Shanta 

School of Electrical Engineering and Computer Science University of Tennessee Knoxville, Tennessee

Significance Parkinson’s disease (PD) is a progressive neurodegenerative disorder of the central nervous system which is linked to decreased dopamine production early in the disease. The progressive neuronal degeneration is a combination of the environmental factors and genetic predisposition. About 3% of the population over the age of 65 years suffer from Parkinson’s disease1. In America alone, 60,000 new PD cases are diagnosed every year2. PD is characterized by motor (tremor, limb stiffness, slowing of movement, postural instability) and non-motor (depression, anxiety, apathy, cognitive impairment) clinical features. With disease progression, motor features become increasingly disabling and less treatable non-motor features such as dementia become increasingly problematic. Medication can provide symptomatic relief, but patients require larger and more frequent doses as sensitivity to these drugs decreases over time. Our eventual goal is to build a system for continuous monitoring of PD motor symptoms through wearable sensors, such as accelerometers. Continuous PD motor symptom monitoring will lead to better adjustment of medication and therefore improvement in quality of life. Clinic visits are not sufficiently frequent due to their high cost and inconvenience for the patients. Patient self-reports are often inaccurate and 15-20 minute clinic visits do not provide enough information for doctors to accurately assess their patients. The aim of the project is to suggest that the proposed mechanism can be used for evaluating and monitoring the disease progression. It must be noted that this system can be modified for any other disease associated with movement disorders, such as Epilepsy and Dyskinesia. Kinesia 360 is a commercially available wearable sensor for PD where patients wear a finger-worn motion sensor. Patients are monitored once per week, and the recorded data is sent to a neurologist for assessment3. APDM Oval is another commercially available sensor where the patients wear sensors in several different locations of the body, and standard tests such as walk, timed up and go, postural sway, 360° turn are measured. Parkinson’s Kinetigraph is a smart wrist worn device that tracks patients’ movement and measures the presence and severity of bradykinesia and dyskinesia. This information allows neurologists to accurately prescribe medication. The developed sensor system in this project will focus on the safety of the patient and measurement of the severity of the disease. Compared to the commercially available sensors, one sensor will be worn at the lower back for continuous monitoring. The patient will have a timed up and go test every day at a particular time, which will give a measure of the severity of the patient’s condition. The sensor will also be able to 

* Corresponding author. E-mail address: [email protected] (A. Hedayatipour). Min Kao Electrical Engineering and Computer Science Bldg1520 Middle Dr, Knoxville, TN 37996 Tel.: +1-(865)306-6203

S. Patel et al., “Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no 6, pp. 864-873, 2009. 2 A. Rueda et al., “Feature analysis of dysphonia speech for monitoring Parkinson's disease,” IEEE Engineering in Medicine and Biology Society, 2017. 3 D. Heldman et al., “Telehealth Management of Parkinson’s Disease Using Wearable Sensors: An Exploratory Study,” Digital Biomarkers, vol. 1, no ., pp. 43-51, 2017. 1

detect if a patient has fallen and notify emergency contacts. Another sensor will be set up at a monitoring station, where the patient can measure heart rate. Apart from alarm systems, heart rate (which can be a good symptom of stress) and environment sensing (humidity and temperature) information can be integrated to develop a complete PD patient monitoring system. Technical description and measurement methodology Gait analysis and turning are important in diagnosing and quantifying the severity of Parkinson’s disease. Different motion tracking systems such as inertial measurement units (IMU) are widely used to detect gait parameters. Turning has clinical importance, increasing the risk for falls and injuries. To monitor gait and turning, the Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson’s disease (PD). It consists of rising from a chair, walking, turning, and finally sitting. The sensor tile, worn at the lower back, will be used to record the acceleration signals during the test and acceleration-derived measures will be extracted from the recorded signals. As shown in the figure 1, subjects will stand up from a chair without armrests, walk at their preferred speed on level ground for 3 m, reach to the turning point, walk back to the chair, and sit. The 3 m is a standard distance for measuring the gait of subjects4. The measurement session will store the information as a reference for patient condition. The test will use a 3D accelerometer with a sample rate of 100 Hz and a range of ±2g. The accelerometer will be worn on the lower back. Sensor locations and orientations must be fixed and known since this method needs to match the signals with Figure 1. 3 meter TUG test particular templates in each axis of motion. If the location or orientation of the sensors changes, the models would need to be adjusted and modified accordingly. Without proper adjustment, incorrect sensor placement results in incorrect computation of gait parameters. Second, in order to compute certain parameters such as stride length, it is necessary to know additional information about the subject such as height. Acceleration signals from the antero-posterior (AP), medio-lateral (ML), and vertical (V) directions will be recorded. The gait component, defined as the period of straight-trajectory walking in a steady state, starts at the end of the sit-to-walk (STW) and does not include turning and the last two identified heel strikes. Measurements for the signal e.g. root mean square, normalized jerk score (NJS)5 will be computed for each transition (STW, Turning, and WTS). Acceleration will be filtered at 20 Hz before the computation of the root mean square (rms) of the acceleration. The acceleration is bandpass filtered between 0.15 and 5 Hz to limit the effect of very slow or abrupt variations on the derivative of the acceleration. NJS is normalized with respect to the total movement time6. With regard to gait measures, step time (Tstep) will be computed by identifying heel strides. Tstep will also be computed for the turning component. Cadence (number of steps/min) is not considered as a distinct feature since it can be obtained by dividing 60 s by Tstep. The measures related to the gait phase, mean phase, standard deviation, STD, of the phase, coefficient of variation, CV, of the phase, and phase coordination index, PCI, will also be computed. Another sensor will be placed in the monitoring station in the patient living environment that can be used to measure patient’s heart rate and environmental setting. The patient is asked to measure heart rate after M. Milosevic, et al., “Quantifying Timed-Up-and-Go test: A smartphone implementation,” IEEE International Conference on Body Sensor Networks, 2013. 5 B. Caby, et al. “Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry,” Biomedical Engineering online, vol. 10, no.1, 2011. 6 L. Palmerini, et al. “Quantification of motor impairment in Parkinson's disease using an instrumented timed up and go test.,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 4, pp. 664-673, 2013. 4

each 3m TUG test, this information along with the TUG test result. This would aid the doctor in obtaining a wider perspective on the patient’s condition. In addition, the patient can do a heart rate test in case of any abnormal symptoms and the monitoring system would create alarms when an abnormal heart rate is measured. Patient data will be transmitted via Bluetooth communication to a mobile phone. Data analysis techniques will be used to extract clinically-relevant information from physiological and movement of the patient. Emergency situations (falls) are detected via data processing and an alarm message is sent to an emergency caregiver for a quick intervention.

Use of ST Microelectronics Sensor tile In this research we focus on inertial-based motion capture systems which rely on acceleration and rotational velocity measurements from accelerometers and gyroscopes, respectively. The LSM6DSM is a system-inpackage featuring high-performance low power 3D digital accelerometer and 3D digital gyroscope with an always-on low-power features. To improve the overall performance for providing accurate pose information during continuous operation (long term stability is affected) the system is combined with LSM303AGR, an ultra low-power high performance system-in-package featuring a 3D digital linear acceleration sensor and a 3D digital magnetic sensor. The magnetic sensor measures the earth magnetic field by using Hall Effect. Together these sensors can be used to give a complete understanding of a device’s acceleration, speed, position, orientation and more, which can then be translated to the patient gait and posture using a wearable sensor on lower back. Information extracted from accelerometer will be used for human fall detection as relevant information could be extracted from its measurements. The algorithm used in the proposed system can be seen in the flowchart below.

It should be noted that system alarms are based on thresholds, which will trigger the output to communicate an alarm to the motherboard through Sensor Tile wireless communication if a fall happens or the patient’s condition becomes critical. The second sensor tile will be located at a monitoring center in the living environment of the patient, which can monitor environment temperature and humidity using the sensors on expansion board. Pulse oximetry will be integrated in our monitoring system by adding LEDs to the expansion pins of the board.

Demo: Demonstration of our remote health monitoring system based on Sensor Tile will be presented. A volunteer will wear a sensor at the lower back, will stand up from a chair without armrests and walk on level ground for 3 m. After walking the measured distance, the volunteer will turn back and sit on the chair. The volunteer will simulate falling during the demonstration and an alert will be sent to the person’s simulated emergency contact. The other sensor will monitor the heart rate and environmental conditions of the person.

Aysha Siddique Shanta Ph.D. Student, University of Tennessee, Knoxville E-mail: [email protected]

 Education  

Ph.D. in Electrical Engineering , University of Tennessee, Knoxville, USA (Expected July 2019) B.Sc. (Honors) in Electrical and Electronic Engineering, BRAC University, Bangladesh (2011)

 Job Experience (Current) Graduate Teaching/Research Assistant, at University of Tennessee Knoxville. (2016) Course Instructor of ECE201 (Electrical Circuits I) (2012-2015)Lecturer, Department of Electrical and Electronic Engineering, BRAC University, Dhaka, Bangladesh (2010-2011)Teaching Assistant, Department of Electrical and Electronic Engineering, BRAC (2007) Teacher, Maple leaf International School, Dhanmondi, Dhaka, Bangladesh

 Honors and awards • Chancellor’s Citation Award for “Extraordinary Professional Promise” (2016, 2017), UTK • Outstanding Graduate Teaching Assistant, EECS Department, University of Tennessee, Knoxville. • Four Year Chancellor’s Graduate Fellowship, EECS Department, University of Tennessee, Knoxville. • Achieved Vice Chancellor’s Gold Medal in the 7th convocation of BRAC University, Dhaka, Bangladesh • Achieved Daily Star Award for O’ Level results in 2006, Dhaka Bangladesh • Achieved Daily Star Award for A’ Level results in 2008, Dhaka Bangladesh

 Publications • A. S. Shanta, K. A. Al-Mamun, D. K. Hensley, N. V. Lavrik, S. K. Islam, and N. McFarlane, “Carbon Nanospikes for Biosensing Applications,” International Conference of the IEEE Engineering in Medicine and Biology Society, 2017 • A. S. Shanta, K. A. Al-Mamun, S. K. Islam, and N. McFarlane, “Carbon Nanotubes, Nanofibers and Carbon Nanospikes for Electrochemical Sensing: A Review,” International Journal of High Speed Electronics and Systems, Vol: 26, No: 3, 1740008 (12 pages), 2017 • Y. Yu, K. A. Al-Mamun, A. S. Shanta, S. K. Islam, and N. McFarlane, “Vertically Aligned Carbon Nanofibers as Cell Impedance Sensor,” IEEE Transactions on Nanotechnology, Vol: PP, Issue: 99, 2016 • S. K. Islam, A. S. Shanta, K. A. Al-Mamun, N. McFarlane, D. Hensley, and I. Kravchenko, “Vertically Aligned Carbon Nanofiber and its Applications as a Sensor,” Connecticut Microelectronics and Optoelectronics Consortium, 2016 • A. S. Shanta, D. B. Hossain, T. R. Huq, R. Mahmood, and M. S. Islam, “Analytical Model of GaN MESFETs for High Power and Microwave Frequency Applications,” International Conference on Electronics and Optoelectronics, Shenyang, Liaoning, July 27-29, 2012

 Skills Languages: Java, Assembly Language, VHDL SoftwareTools: MATLAB, Cisco Packet Tracer, Digital Schematic Editor and Simulator, PSpice, LTSpice, Cadence Virtuoso Nanofabrication Tools: PECVD process, Scanning Electron Microscopy (SEM) Imaging, Raman Spectroscopy, Familiar with clean room procedures

Rania Oueslati Ph.D. Student, University of Tennessee, Knoxville Email: [email protected]

 Education    

Ph.D. in Electrical Engineering , University of Tennessee, Knoxville, USA (Expected July 2019) Engineering Diploma (BS+MS) in Electrical Engineering, National High School of Engineers of Tunis, University of TUNIS, Tunisia, Major: Electronics and advanced technologies (2016) Diploma of Entrance to Engineering Schools, Preparatory Institute for Engineering Studies of MANAR, Tunisia, Major: Math -physics (MP) (2012) Baccalaureate Diploma, Tunisia, Major: Math (pass with high honors) (2010)

 Honors and awards • Awarded “EECS Department Excellence Fellowship” by The University of Tennessee • Outstanding Graduate Teaching Assistant Award at EECS Department, The University of Tennessee, Knoxville.

 Job Experience (Current) Graduate Research assistant & Teaching Assistant at University of Tennessee Knoxville. (2015) Intern at University of Tennessee Knoxville, TN: Implementing imaging functions on an FPGA Board. (2014) Intern at Valeo, Tunisia: Design and implementation of embedded system to control electrical signals (STM32) (2013) Intern at TUNISAIR, flag carrier airline of Tunisia (2012) Undergraduate research assistant, University of Tunis

 Publications • C. Cheng, R. Oueslati, J. Wu, J. Chen, and S. Eda, “Capacitive DNA sensor for rapid and sensitive detection of whole genome human herpesvirus-1 dsDNA in serum,” Journal of Electrophoresis, 2017. • H. C. Dylewski, M. Hoang, J. Ramos, J. H. Rice, C. J. Vaz, R. Oueslati, C. Cheng, J. Wu, and S. Eda, “Proof-of-concept study for rapid detection of Zika viral RNA via DNA biosensor,” National Conference on Undergraduate Research, University of Memphis, April 6-8, 2017 • H. C. Dylewski, M. Hoang, J. Ramos, J. H. Rice, C. J. Vaz, R. Oueslati, C. Cheng, J. Wu, and S. Eda, “Proof-of-concept study for rapid detection of Zika viral RNA via DNA biosensor,” Exhibition of Undergraduate Research and Creative Achievement, University of Tennessee, April 17 – 21, 2017.

 Skills Technical: Analog circuit and layout design, Pattern recognition, Image processing, Chips design, Bacterial pathogenesis, Microbiological assay design, Drug sensitivity testing. Languages: C++, Python, VHDL (basic) Software: MATLAB, Cadence, IAR Embedded Workbench, Simulation Software (Advance Design System (ADS), Orcad Family (HSpice, Pspice), Vivado Design suite. Development boards: STM32, FPGA (ZYBO), Arduino, NXP LPC1768

Ava Hedayatipour Ph.D. Student, University of Tennessee, Knoxville E-mail: [email protected]

  

 Education Ph.D. in Electrical Engineering , University of Tennessee, Knoxville, USA (Expected July 2019) M.Sc. in Electrical Engineering (Circuit and systems), ShahidRajaee University, Iran (2015) B.Sc. in Electrical Engineering, Iran University of Science and Technology, Tehran, Iran (2012)

 Honors and awards • Awarded “EECS Department Excellence Fellowship” by The University Of Tennessee • Awarded the best presenter by the 2nd International congress of electrical engineering, computer science and information technology (2015). • Awarded by IEEE Iran Section and Iranian Academic Center of Education Culture and Research for cooperation in holding the 2ndInternational Conference on Electronic City (2009). • Full Tuition-Waiving Fellowship in B.Sc. degree (2007). • Top 0.2% among over 400,000 participants in the university entrance exam for B.Sc. degree in Mathematics and Physics (2007).

 Job Experience (2016-Now)Working as a teaching and research assistant at the University of Tennessee, Knoxville. (2012-2016)Working as Electrical Engineer (Tech Support) at “DIDARC”. (2012-2015)Working as Electrical Engineer (Digital Circuit Designer) at “SamanehFarda”.

 Publications •A. Hedayatipour, A. S.Shanta, and N. McFarlane, “A Sub-μW CMOS Temperature to Frequency Sensor for Implantable Devices,” IEEE International Midwest Symposium on Circuits and Systems, 2017. •A. Hedayatipour, S. Aslanzadeh, and P. Amiri, “A Novel CMOS Neural Amplifier Based On Self-Biased Cascode,” Journal of Electronics Information & Planning, 2015. •A. Hedayatipour, S. Aslanzadeh, and P. Amiri, “RGC Preamplifier Design with 20GHz Bandwidth and 60dBΩ Trans impedance for Telecommunication Systems,” Tabriz Journal of Electrical Engineering, Vol: 46 , No: 2 (76), 2015. •A. Hedayatipour, S. Aslanzadeh, and P. Amiri, “A novel low power bio-amplifier based on self bias designs,” International Congress of Electrical Engineering, Computer Science and Information Technology, 2015. •S. Aslanzadeh, A. Hedayatipour, and P. Amiri," Current Mirror versus Two-Stage CMOS Amplifier: Noise-Power Trade-off," The International Conference in New Research of Electrical Engineering and Computer Science, 2015.

 Skills Technical: Analog circuit and layout design, Chip design, proficient in AVR and PIC microcontrollers programming and familiar with ARM (Codevision, IAR, Keli). Languages: C++, Python, VHDL (basic) Software: MATLAB, Cadence, IAR Embedded Workbench, Simulation Software (Advance Design System (ADS), Orcad Family (HSpice, Pspice), MATLAB/Simulink

To the organizing and proposal selection committee, By signing this Agreement, we commit to attend the live demo session at the IEEE I2MTC 2018 in Houston, TX May 12-15, 2018. Printed Name_________________________________________________________________ Date________________ Student Signature__________________________________________

Printed Name_________________________________________________________________ Date________________ Student Signature__________________________________________

Printed Name_________________________________________________________________ Date________________ Student Signature__________________________________________