The Michael J. Fox Foundation is collaborating with Intel on a Parkinson’s research solution using wearable technology, Intel algorithms, Big Data analytics, and the Cloudera distribution of Hadoop.
Parkinson’s disease (PD), the second most common neurodegenerative 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 multidisciplinary 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.
The Michael J. Fox Foundation for Parkinson’s Research (MJFF) is working with Intel on tech-enabled solutions to gather relevant data about PD, analyze that data to identify patterns and make generalizations, and use insights gained to accelerate the development of therapeutic breakthroughs, and potentially even a cure for the disease.
Challenges
When seeking a cure for an “incurable” disease, we often face a knowledge deficit. 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 identify patterns—to create “order” from the massive chaos of raw data that such an endeavor generates. Identifying patterns, making generalizations 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 symptoms, so a second challenge is cataloging 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 movement, 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 conditions under which these symptoms manifest themselves, Intel is developing a configurable solution for wearable monitors that will passively track a patient’s motor functions along with self-reported information the patient enters via a smartphone app called Fox Insight Mobile. Fox Insight Mobile tracks movement and provides 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.
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 unreliable because patients tend to lose interest entering information, which leads to sporadic information-gathering from one patient to the next.
To entice patients to voluntarily and consistently provide us with information about how they are feeling and when they are taking medications—information we cannot automatically 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 analysis. 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 measures 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 determine whether a person’s motion is intentional (as from walking or running), accidental/incidental, or caused by involuntary tremors.
The latest clinical trial employs an “off-the-shelf” wristworn 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 performing 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 calculated 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 algorithms or gather data for later comprehensive 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.
• Tremor. We recognize and quantify hand tremor through frequency analysis, 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.
• Gait detection. This algorithm is based on supervised learning of labeled accelerometer data collected from patients. The data is transformed into aggregative features in the time and frequency domains, and a decision tree model is used to categorize 5-second segments into walking/non-walking groups. The output is used to calculate a personalized threshold for high activity level, and as an input to the nighttime tracking algorithm.
• Nighttime tracking. PD patients commonly have difficulty falling asleep and staying asleep, and they experience motor symptoms, such as rapid eye movement (REM) and periodic 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 disorders. The Fox Insight Mobile app provides an analysis of sleep quality based on the movements of the user during the night.
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 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 non-analytical 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 individual’s symptoms and responses (recorded from the wearable IMUs).
Armed with personalized information 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 visualize 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.
System architecture
Architecturally, our solution divides into two role-based categories: One for the patient and one for the researcher.
On the patient side, users log on to the Fox Insight Mobile app on their smartphones through a mobile API and manually add data to the raw or calculated data stream that is already automatically transmitted by the IMUs they are wearing.
On the research side, researchers, clinicians, and data analysts can pull data from the platform using secured RESTful APIs1 to view and analyze the data. SPARK is used to manipulate the data before export.
In between the IMUs and the EDH is where we interpret and analyze this information.
Our messaging framework is based on MQTT (Message Queue Telemetry Transport), a lightweight publish-subscribe messaging protocol that travels on top of TCP/IP. MQTT is ideal for wearables because of its small code footprint and economic use of network bandwidth. Because its “pub-sub” messaging pattern requires a message broker to distribute messages to interested clients, we use also Mosquitto*, an open source MQTT broker.
Another advantage of using this messaging method to transmit data is that it allows us to process the raw data either locally on the IMU or in transit—within the message packet itself. We are able to do this because we use a standalone Java library for our algorithms and Akka* (an open source toolkit for building message-driven applications on a Java virtual machine), which allows us to make calculations on the data while it is in the stream, rather than on the smartphone or smartwatch. This flexibility allows project leaders to customize their research projects—to design a trial, for example, where the phone app converts raw data and sends only the processed data on to the database; or sends the raw data, to be analyzed and forwarded on the fly within the protocol stream; or sends the data without any calculations at all, to be stored raw in the EDH.
The enterprise data hub sits at the other end of this data stream. This is the secure data warehouse and staging area where data is stored and advanced analysis takes place.
Data acquisition and analysis
Using a series of Java library tools, we have developed several algorithms to interpret raw data from the IMUs. We constantly refine these algorithms and create new ones, as analysis findings indicate promising areas for future research.
We currently identify more than 100 features for data acquisition from the IMUs—such as average acceleration (every 5 seconds), range of accelerometer, variance, zero-crossing, variables that describe the spectrum of the signal, and so on—and we perform calculations for all of them. We use walking sessions as an agreed upon “active” activity baseline, and use a band-pass filter to remove noise, particularly tremor, which we don’t want erroneously interpreted as a high level of activity.
For example, for a recent clinical trial on the effectiveness of L-DOPA (a common PD drug), patients equipped with Fox Insight Mobile IMUs take the medication and perform a series of physical tasks eight times, to measure the impact the medication has over time.
The Fox Insight Mobile platform’s flexibility will allow researchers conducting subsequent trials to add sensors or include specialized algorithms, as such needs arise. To date, we have implemented activity level, activity recognition, and tremor recognition algorithms into the monitoring system. If we later decide to expand monitoring to include sleep analysis, dyskinesia (detection and quantification), or non-motor symptoms, all we would need to do is add the necessary algorithms in a subsequent release, and include whatever additional sensors these algorithms may require for the next trial. For example, we might need to add a heart rate sensor to include sleep, REM sleep, and deep sleep pattern analysis. Other algorithms might take advantage of physiological metrics such as skin temperature, perspiration, calorie burning, blood flow, etc.
In later data modeling and evaluation phases of the project, analysts will connect these explaining variables with the actual activity that was recorded at the time. Evaluation involves examining the validity of the extracted patterns.
Using Big Data to find these patterns and make generalizations can lead to insights about PD (and other diseases, for that matter), but they can also lead to Type I “false positives” as a result of what is known as the “multiple comparisons” problem. Because even randomly generated data can sometimes produce interesting patterns, independent of causality, researchers and clinicians will have to pursue these “hunches” with controlled experiments, to verify that the results are reproducible. As the size of the data set increases, the chance of encountering Type I errors diminishes, but researchers must follow up with scientific methods to verify that the insights were indeed valid and the relationships causal.
The Big Data picture
Looking at the sheer volume of information we are able to gather, it becomes clear why Cloudera is a critical piece of this project. The wearable IMUs we are using are capable of recording tremors, sleep patterns, gait, and balance—between 150 and 300 samples per data. With 1,000 concurrent users, each wearing two or more devices, we require a data warehouse capable of handling a large volume of data every day. Cloudera Enterprise can manage such a heavy workload.
Cloudera’s comprehensive security package includes complete governance—data protection, integrated authentication, authorization, encryption, key management, audit, and lineage—allowing you to track data and manage user interactions.
Next steps
Our latest trial, taking place in the Netherlands, marks the fourth collaborative effort worldwide between Intel and the Michael J. Fox Foundation, which is sponsoring this research project and many others. Through these trials and others to follow, we hope to enable breakthroughs in Parkinson’s disease research through Big Data analytics technologies.
Analysts rarely discover new insights the first time they examine their data. Usually an initial inspection may hint at a more promising approach. With a few adjustments to the computations, the information may begin to look more meaningful on subsequent runs. Hundreds of skilled neurologists, mathematicians, and data analysts across the globe are looking for rich data sets like ours on which to exercise their knowledge expertise and come up with innovative ideas...the first step in a long journey toward better understanding of the disease and ultimately a cure.
Although the immediate goal is to improve the quality of life for Parkinson’s sufferers and lead clinical research scientists to potential cures, the information we learn from these trials will undoubtedly also help people with other Parkinsonian disorders, and the tools, methods, and algorithms should be applicable to clinical trials for other afflictions and for other scientific discoveries in general.
http://health.einnews.com/article/345989031/l7vINB3hLUMtYGi7
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