Using Wearable Technology to Advance Parkinson’s Research

White Paper

Intel and the Michael J. Fox Foundation Data Center Software Division 

September 12, 2016

Intel and the Michael J. Fox Foundation collaborate on a Parkinson’s research solution using wearable technology, Intel algorithms, Big Data analytics, and the Cloudera distribution of  Hadoop. 

Sample

Introduction

Parkinson’s disease (PD), the second most common neurodegen- erative disorder after Alzheimer’s, is estimated to affect one million people in the United States and perhaps as many as seven million globally. There is currently no cure; medications, surgery, and multidis- ciplinary management can provide relief, but they address only some of the symptoms patients face, and are effective only for a limited time. Many also introduce serious side effects that can be as disabling as the disease itself.

Intel is working with the Michael J. Fox Foundation for Parkinson’s Research (MJFF) on tech-enabled solutions to gather relevant data about PD, analyze that data to iden- tify patterns and make generaliza- tions, and use insights gained to accelerate the development of ther- apeutic breakthroughs, and poten- tially even a cure for the disease.

Challenges

When seeking a cure for an “incur- able” disease, we often face a knowledge deficit. It’s difficult to solve a problem we don’t fully understand, and there is still so much about Parkinsonism that remains a mystery. So the first step is to gather as much data as we can. That is why MJFF is actively seeking volunteers for many clinical trials. Data science tells us, however, that in order to extract meaningful knowledge from data, we must have relevant data to work with. In other words, we have to sort through a lot of haystacks to find a few needles.

The goal for researchers is to iden- tify patterns—to create “order” from the massive chaos of raw data that such an endeavor generates. Identifying patterns, making gener- alizations about those patterns, and translating them into quantifiable symptoms of the disease is part of the big data analysis procedure.

Today Parkinson’s research lacks an objective way to measure symp- toms, so a second challenge is cata- loging and quantifying measurable, observable symptoms for analysis.

In the early stages of PD, patients may experience sleep disorders and olfactory loss, but the most obvious symptoms are movement-related— shaking, rigidity, slowness of move- ment, and difficulty walking. These motor symptoms result from the death of dopamine-generating cells in the brain, the cause of which is still unknown. In later stages of the disease, cognitive and behavioral problems may arise, including dementia and depression.

To better understand the condi- tions under which these symptoms manifest themselves, Intel is devel- oping a configurable solution for wearable monitors that will pas- sively track a patient’s motor functions along with self-reported information the patient enters via a smartphone app called Fox Insight Mobile. Created by Intel, Fox Insight Mobile tracks movement and pro- vides an electronic “diary” that patients use to enter medication times/dosages and how they are feeling throughout the day. Unlike a paper diary, the electronic diary engages the patient by providing useful feedback and information.

Gathering data automatically from wearable devices is one thing, but expecting patients to provide input manually is another. Because few doctors can see their PD patients every day, active user involvement is critical. How can we keep patients actively involved?

In the past, patient contribution of data has required a tedious paper- and-pencil solution, where patients keep a diary of their feelings and medication dosages throughout the day, for weeks or months. These diaries often are criticized as unreli- able because patients tend to lose interest entering information, which leads to sporadic information-gath- ering from one patient to the next.

To entice patients to voluntarily and consistently provide us with infor- mation about how they are feeling and when they are taking medica- tions—information we cannot auto- matically acquire from a wearable device—we must make this process easy and personally useful to the patient. Because the Fox Insight Mobile app reminds patients to take medicine and provides information that tracks their progress, they’ll be more likely to use it, which adds a positive side benefit: The more valuable we can make the app to the end-user, the more end-users we might be able to attract to using it, which means more data.

More data is good, but it presents a technical challenge: The sheer quantity we have to work with— from acquisition to storage to anal- ysis. Tracking patients for long periods—twenty-four hours a day,seven days a week, for months, maybe years—requires a system that can collect a massive amount of data...and make use of all the data once it is gathered and stored.

The monitoring devices

An inertial measurement unit (IMU) is an electronic device that mea- sures velocity, orientation, and gravitational forces, using triaxial accelerometers and gyroscopes. Traditionally installed in boats and aircraft, IMUs have been adapted for monitoring human movement. Many wearable IMUs on the market, for example, help users with athletic performance tuning and physical therapy. These devices can deter- mine whether a person’s motion is intentional (as from walking or run- ning), accidental/incidental, or caused by involuntary tremors.

For our latest clinical trial, we are employing an “off-the-shelf” wrist- worn smartwatch with a triaxial accelerometer and an application we built for it, and a smartphone with the Intel-developed Fox Insight Mobile app installed. We calibrate the devices, with each patient per- forming a series of normal activities. These devices automatically pair with each other and share their data with MJFF servers.

These IMUs serve two functions: For the patient, they help track activity level and medication usage, provide reminders, and monitor tremors. For research purposes, they gather the data, both automatic (from the wearables performing calculations intrinsically) and manual (from the end-users entering information via the Fox Insight Mobile app) and pass the data to an enterprise data hub (EDH) for Big Data analysis. This central repository of information is available to researchers worldwide.

Our solution’s flexibility allows researchers to tailor data collection to the specific requirements of each project. One can configure which sensor (accelerometer or gyro) to collect data from, how frequently to collect data, and most importantly whether to collect all of the raw data or just the calculated data (activity levels, tremors, etc.). For long-term studies, for example, where patients may be wearing the devices for months, using only cal- culated data for tremor, activity levels, and other algorithms might be more appropriate. For short- term clinical trials, however, where we are trying to develop new algo- rithms or gather data for later com- prehensive analysis, we would probably collect all of the raw data.

Intel algorithms

Intel has developed several algorithms for these devices, including activity level, tremor, nighttime tracking, and gait detection.

• Activity level. This algorithm measures the intensity of a wristworn device’s movement, computed as the average of absolute values of acceleration, over intervals of 30 seconds, (after filtering out frequencies typical to tremor). The Fox Insight Mobile app shows users their activity levels on a graph that depicts activity over time. It also provides a daily summary of active time.

This algorithm does not capture the physical intensity of activities like bicycle riding or walking on a tread- mill, but we can augment the mea- surements with additional sensors, such as a heart rate monitor or gyro- scope, to quantify these activities.

• Tremor. We recognize and quantify hand tremor through frequency anal- ysis, particularly in amplitudes within the 4 to12 Hz range, and subtract these typical tremor frequencies from the activity level measurement. A 5-second segment with a high average difference between these values is considered a tremor point. We aggregate these occurrences into “tremor minutes” and provide the user with a graphical overview of daily tremor symptoms.

Very weak tremors and short tremor episodes are difficult to detect, and some activities, such as driving over a bumpy road or brushing one’s teeth, can be misclassified as tremor. To reduce these type I and II errors, we rely on the controlled data we collect when we calibrate the devices for each patient at the commencement of the trial.

• Gait detection. This algorithmis based on supervised learning of labeled accelerometer data collected from patients. The data is trans- formed into aggregative features in the time and frequency domains, and a decision tree model is used to cate- gorize 5-second segments into walking/nonwalking groups. The output is used to calculate a person- alized threshold for high activity level, and as an input to the nighttime tracking algorithm.

The model accuracy on the validation set is 98.5%, where false-positive cases usually have periodic move- ments with similar frequencies as walking, and false-negative cases are usually affected by extensive hand movements that impact the ability to detect walking periodicity. 

• Nighttime tracking. PD patients commonly have difficulty falling asleep and staying asleep, and they experience motor symptoms, such as rapid eye movement (REM) and peri- odic limb movement (PLM).

Existing sleep-tracking apps don’t always fit the PD patient’s needs, as most of them are designed for people who do not suffer from sleep disor- ders. The Fox Insight Mobile app pro- vides an analysis of sleep quality based on the movements of the user during the night. We distinguish between three levels of movement during sleeping or waking states: minimal movements, such as when the person is at full rest or is lying still; moderate movements, including tossing/turning, periodic limb move- ments, and sipping/drinking; and extensive movements, such as get- ting out of bed or performing strong or violent movements during sleep. These calculations are based on the quantity, duration, and type of move- ments, in addition to activity level.

We will continue to refine these algorithms and develop others that will help Parkinson’s research. 

The Fox Insight Mobile app

Another important piece of this study is Intel’s Fox Insight Mobile smartphone application (Figure 1), currently available on the Android* platform. In addition to being the conduit to the data warehouse, Fox Insight Mobile brings value directly to patients on a daily basis, showing them their activity levels and helping them with nonanalyt- ical features like medication reminders.

Patients create reminders for each medication they are taking, with specific days, times, and amounts, but Fox Insight Mobile also provides feedback that could prompt optimum medication dosages and times, based on analysis of the indi- vidual’s symptoms and responses (recorded from the wearable IMUs).

Armed with personalized informa- tion and graphs about their activity levels and medication history, patients can compare medication dosages/frequencies to physical activity, allowing them to manage their regimen to suit their personal preferences and needs. To motivate them to increase their physical activity, data summaries reveal low activity cycles and help users visu- alize their exercise regimens.

Once they log on to Fox Insight Mobile, users will be able to use the application to report activities (such as taking a dosage of a specific medicine) or log how they feel. This electronic diary simplifies reporting and reduces patient subjectivity by limiting entries to four emoticons (poor, fair, good, or very good). Our approach makes this data more objective, and standardizing this way allows better global analysis.

Coupled with empirical data from multiple triaxial sensors, these timestamped records of behavior will help researchers correlate patients’ activities, feelings, and medications, to devise meaningful hypotheses that can later be tested through normal scientific methods. 

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