Integrating GaitAuth™ into Your Android App

GaitAuth™ is an SDK offered by UnifyID that allows you to authenticate your users based on how they move. This post demonstrates the process of adding GaitAuth to a simple Android app.

The UnifyID Developer Portal has high-level documentation that covers the basics of the GaitAuth SDK integration process. There are also auto-generated JavaDocs available for the specific details. The goal of this post is to meet the two in the middle and give some more color to the integration process. By the end of the post we’ll have built a sample Android app that uses GaitAuth to train and test a GaitModel. Along the way, we’ll also explore relevant best practices and important implementation details.

The Sample App

All of the sample app code is available on GitHub. To get a feel for the training and testing process you can build the app on your phone and try it for yourself. If you want to get right to building your own app, you can treat the repository as a collection of helpful code snippets. You can also use the code to follow along with this post in depth. Not all of the code is shown in this post, the code that is shown has been abbreviated for simplicity’s sake. Links to the original code on GitHub are provided at the top of the snippets.

The sample app closely mirrors the functionality of the GaitAuth SDK. On the starting screen you are presented with the option to create or load a model. You’ll want to create a new model when you first use the app. In future uses, you can use the load option to skip training. After creating a model you’re ready to collect data and train the model. First, turn on collection (it will continue to collect even if the phone is locked or the app is backgrounded or closed). Once you’ve collected some features you can add them to the model. After you’ve collected enough data you can train the model. While you wait for the model to train (which may take a few hours), you can manually refresh its status.

If the model fails to train you’ll need to start over by pressing the trashcan icon in the top left corner. When the model successfully trains you’re ready to test it. Turn on feature collection and walk around for a while. When you are ready, stop collecting features and score them. This will graph the scores and show you an authentication decision.

Now that we know what the sample app does, let’s go build it.

Configuration & Initialization

Adding GaitAuth to our Gradle configuration is a good starting point. We’ll also need to request some permissions in the Android Manifest. We can follow the steps outlined in the Developer Portal documentation to take care of that.

Before the GaitAuth SDK can be used anywhere in the app it must be initialized. The initialization is trivial; always initializing it before using it is harder. In something straightforward like this sample app we can simply initialize it in the onCreate method of the MainActivity.

if (GaitAuth.getInstance() == null) {
    // First initialize the UnifyID Core SDK
    UnifyID.initialize(getApplicationContext, SDK_KEY, USER, new CompletionHandler() {
        public void onCompletion(UnifyIDConfig config) {
            // Second initialize the GaitAuth SDK
            GaitAuth.initialize(context, config);
            // Save these values for debugging purposes
            Preferences.put(Preferences.CLIENT_ID, config.getClientId());
            Preferences.put(Preferences.INSTALL_ID, config.getInstallId());
            Preferences.put(Preferences.CUSTOMER_ID, config.getCustomerId());

When initialization succeeds we squirrel away a couple of relevant details in the Shared Preferences (the Preferences class hides the idiosyncrasies of the Shared Preferences API). These will come in handy for debugging. You can view them by tapping on the info icon in the top right corner of the sample app. We also start a foreground service — more on this later. Finally, we call route which determines what the app does next.

In an app with a more complex workflow, initialization of the GaitAuth SDK may not be so simple. In these scenarios we recommend you wrap the SDK in a class that protects its usage. With some simple synchronization this wrapper can ensure that the SDK is never used uninitialized.

Two important points remain. First, remember that the SDK key is a sensitive value and should be protected. In the sample app we ask the user to enter the SDK key the first time they use the app. For an app in production, something like Android Keystore can be used. Second, the user value has a few important stipulations on it. It must be unique, immutable, and should be unrelated to any personally identifiable information. We don’t recommend using things like email addresses or phone numbers for the user value.

The GaitModel

Now that we’ve gotten through the drudgery of initialization it’s time to talk about the star of the show — the GaitModel. In a sense the sample app can be viewed as a tool for managing the lifecycle of a GaitModel. It creates a new model, loads training data into it, initiates training, and then tests the model. For this reason, the sample app’s routing is based on the status of the current GaitModel.

The sample app uses a single-activity/multiple-fragment architecture where each fragment is a different screen. In the MainActivity after the initialization of the GaitAuth SDK the following routing code is run.

private void route() {
    // Load the id of the current GaitModel
    String modelId = Preferences.getString(Preferences.MODEL_ID);
    if (Strings.isNullOrEmpty(modelId)) {
        showFragment(new SelectModelFragment());
    } else {

private void loadModel(String modelId) {
    AsyncTask.execute(() -> {
        model = GaitAuth.getInstance().loadModel(modelId);
        Preferences.put(Preferences.MODEL_ID, modelId);

private void renderFragmentBasedOnModelStatus(GaitModel.Status status) {
    switch (status) {
        case CREATED:
        case TRAINING:
            showFragment(new ModelPendingFragment());
        case FAILED:
        case READY:
            // treat it as a failure
            showFragment(ModelErrorFragment.newInstance("unknown model status"));

First the method route looks for the id of the current GaitModel and asynchronously loads it with the help of loadModel. After the model is loaded, renderFragmentBasedOnModelStatus is called. A simple switch statement then sends the user to the screen matching the current state of the model.

The method route will not find a model id on a user’s first time through the app or if the reset button was pressed. In these cases the user is immediately sent to the SelectModelFragment. From here, when the user clicks on the “Create Model” button, onCreateModelPressed is executed. It builds a new GaitModel, saves the model id in the shared preferences, and sends the user to the FeatureCollectionFragment. Alternatively, the user can opt to load a pre-existing model which leverages the same loadModel method.

public void onCreateModelPressed() {
    AsyncTask.execute(() -> {
        model = GaitAuth.getInstance().createModel();
        Preferences.put(Preferences.MODEL_ID, model.getId());
        showFragment(new FeatureCollectionFragment());

Feature Collection

So we have a GaitModel locked and loaded, but now what? The model is useless without training data so let’s start there. A GaitModel is trained on a collection of GaitFeatures which are data points collected from the user’s movement. These GaitFeatures are given to the model via the add method which has the signature void GaitModel.add(Collection<GaitFeature> features). Later during training, only the features explicitly added to the model will be used.

Now that we know how to use the features, how do we actually collect them? This is achieved by registering a FeatureEventListener that will fire the onNewFeature callback for every feature collected. Correctly managing feature collection requires tackling two key issues: feature storage and backgrounding.

Storing Features

In a naive implementation of feature collection, every time a new feature was received it would be immediately added to the GaitModel. There are two problems with this. First, adding features to the GaitModel may use network resources or trigger other expensive operations and thus is inefficient to call frequently. Second, if the model fails when training you will need to recollect all new data for the new model since you didn’t persist it anywhere.

The sample app solves the feature storage problem with a thread-safe FeatureStore class. This class exposes a method with the signature void add(GaitFeature feature), which serializes the feature and appends it to a file on disk. At a future time (when the user clicks the “Add Feature” button) all of the features can be loaded from disk, deserialized and returned via the method getAll with the signature List<List<GaitFeature>> getAll(). Note that it returns the features partitioned into multiple lists. This is because adding thousands of features to a model at once should be avoided. Finally, there is an empty method to clear the file after adding features. This prevents adding features twice. Deleting the features after using them reintroduces the persistence problem though. The sample app does it anyways to avoid re-adding features to the model in a simple manner.

public void onNewFeature(GaitFeature feature) {
    int count = Preferences.getInt(Preferences.FEATURE_COLLECTED_COUNT) + 1;
    Preferences.put(Preferences.FEATURE_COLLECTED_COUNT, count);

public int onAddFeaturesPressed() {
    FeatureStore featureStore = FeatureStore.getInstance(this);
    List<List<GaitFeature>> chunks = featureStore.getAll();
    int uploadedCounter = 0;

    for (List<GaitFeature> chunk: chunks) {
        uploadedCounter += chunk.size();
    if (uploadedCounter > 0) { // only truncate file after we uploaded something
    return uploadedCounter;

In a production application there are many improvements you would want to make to FeatureStore. First and foremost, it should implement some form of in-memory buffering. Writing to disk for every feature you collect is slow and can lead to poor battery-life. Second, it should rotate files to avoid size limitations and corruption. Third, it should not clear the file after adding the features to the model. Rather, it should keep track of what features have been added and only add the new ones.


The second key issue to solve for feature collection is backgrounding. It would not be wise to register the FeatureEventListener on the app’s main thread. As soon as the user closed the app, GaitFeatures would no longer be collected. Android services can help us solve this problem. Services provide a way to execute a long-running operation in the background. There are two relevant types of Android services: background and foreground. A background service needn’t provide any indication to the user that it is running, but is more likely to be shut down by the OS if resources are scarce. A foreground service must present a notification to the user indicating its presence the entire time it is alive. But, it is unlikely to be killed by the OS.

The sample app takes the most straightforward approach. In the onCreate method of the MainActivity a foreground service is created regardless of whether or not the service will be used. This avoids complex state management and synchronization issues that arise when dynamically building a service. In the onCreate method the activity binds to the service. And in the onStop method it unbinds from the service. This gives the activity direct access to the feature collection service.

protected void onStop() {
    if (gaitAuthServiceBound.get()) {

You may want to handle services differently in a production app. For example, you may only want to start the service when you are actually collecting features. If you go this route make sure to take special care in synchronizing the usage of the GaitAuth SDK.

Final Considerations

That was a lot of details. Let’s take a step back for a moment and consider the entire training process. Feature collection for training is the most critical stage for the GaitAuth SDK. To get good authentication results you need to have a well trained model. The Developer Portal documentation has a number of suggestions on how to best do this.

Training the Model

The hard work of feature collection was well worth the effort. We now have a plethora of GaitFeatures to train with. We’re nearly ready to use our GaitModel, but first we need to train it. Thankfully, training is easy. When a user clicks on the “Train Model” button the method below is called. In a background thread it kicks off the training process for the model and sends the user to the ModelPendingFragment.

public void onTrainModelPressed() {
    AsyncTask.execute(() -> {
        try {
            showFragment(new ModelPendingFragment());
        } catch (GaitModelException e) {
            showToast("Failed to start training model.");

At the pending model screen a user can manually refresh the status of a model while they wait for it to train.

public void onRefreshPressed() {
    AsyncTask.execute(() -> {
        try {
        } catch (GaitModelException e) {
            showToast("Failed to refresh model status.");

After refreshing the status of the model, our old friend renderFragmentBasedOnModelStatus is called to bring the user to the right screen given the model’s status. If the training failed for some reason the ModelErrorFragment will load. From there the user has no choice other than to reset and start over. After a successful training, the user will be presented with the TestingFragment. And of course, if the model status is still TRAINING after a refresh then no navigation will occur.

Testing the Model

The hard work is over now — we’ve trained a GaitModel and can now reap the benefits. All of the testing functionality is managed by the Authenticator interface. When you instantiate an Authenticator you pass it a GaitModel and a scoring policy. Once created, it automatically starts collecting features. You can ask it for an authentication decision at any time and it will say if the user is authenticated or unauthenticated. In the scenario where this is not enough data to make a decision it will return inconclusive. When the user presses the “Start Collection” button onStartCollectionBtnPressed is called. It in turn tells the foreground service to create a new Authenticator object.

public void onStartCollectionBtnPressed() {
    try {
    } catch (GaitAuthException e) {
        showToast("Failed to start feature collection for testing.");

public void startFeatureCollectionForTesting(GaitModel model) throws GaitAuthException {
    GaitQuantileConfig config = new GaitQuantileConfig(QUANTILE_THRESHOLD);
    authenticator = GaitAuth.getInstance().createAuthenticator(config, model);

A quick aside on the GaitQuantileConfig policy and how it works. In short it will authenticate the user if at least X percent of scores in the session scored Y or more (learn more about scores here). X is known as the quantile and Y is the score threshold. GaitQuantileConfig sets the quantile to 50% by default and in the sample app the score threshold is set to 0.8. The table below shows three example sessions with these numbers. The first session has 60% of scores at or above the 0.8 score threshold, and therefore has an authenticated result. However, the second and third sessions only have 40% and 20% of scores that meet the 0.8 score threshold, and therefore they produce an unauthenticated result.

You can customize more than just the quantile and score threshold of the GaitQuantileConfig. The method setMinNumScores lets you configure how many scores are required for an authentication decision to be made. Any attempt to authenticate with a number of scores less than this minimum will return inconclusive. Similarly, setMaxNumScores configures the maximum amount of scores that will be considered. If there are more scores than the maximum, the most recent scores will be chosen. Finally, setMaxScoreAge determines how old of scores can be used. Scores older than the given age will not be used for an authentication decision.

Back to the action. The sample app requires the user to stop collecting features before they can score them. Note that this is not a requirement of the GaitAuth SDK and is only done to simplify state management. The Authenticator has a stop method which helps us do this. Clicking the “Stop Collection” button kicks off this sequence of events.

public void onStopCollectionBtnPressed() {

public void stopFeatureCollectionForTesting() {
    if (authenticator != null) {

Now the user can press the “Score Features” button. This entails getting the authenticator from the foreground service and then getting an authentication status from the authenticator. Then it sends the user to the ScoreFragment. The authenticator also returns the individual scores that lead to the authentication decision. ScoreFragment puts these in a graph to help visualize the decision. How you use the authenticator will be highly dependent on the specifics of your use case.

public void onScoreFeaturesPressed() {
    Authenticator authenticator = gaitAuthService.getAuthenticator();
    authenticator.getStatus(new AuthenticationListener() {
        public void onComplete(AuthenticationResult authenticationResult) {

        public void onFailure(GaitAuthException e) {
            showToast("Failed to get scores.");


And just like that we’ve integrated GaitAuth into a simple Android application. For more implementation details you can explore the code in depth on GitHub. If you build something with GaitAuth, let us know on social media. We’d love to hear about it. Finally, reach out to us if you have any trouble integrating.

Interview With John Whaley – UnifyID by Safety Detectives

John Whaley: Founder and CEO of UnifyID





SD: What motivated you to start UnifyID?




SD: What have been some challenges?


SD: Which industries use UnifyID and why?



SD: What do you feel is the worst cyberthreat today? 





John Whaley




Interview originally published on Safety Detectives.

UnifyID™ Raises $20M Series A Funding from NEA to Fuel Next Gen Authentication

Company Uses Behavioral and Environmental Factors, Not Passwords, to Identify Users

SAN FRANCISCO, CA – August 1, 2017 – UnifyID is leading the development of an implicit authentication platform that requires zero conscious user actions. The Company announced today that it has closed $20 million in Series A financing led by NEA. Its General Partners Scott Sandell and Forest Baskett will be joining UnifyID’s Board. Investors Andreessen Horowitz, Stanford StartX, and Accomplice Ventures previously invested in the company’s Seed round, bringing the total invested to $23.4 million. This latest round of funding will be used to grow the team to expand enterprise trials, accelerate research and maintain the company’s position as the leader in implicit authentication and behavioral biometrics.

“Our goal is seamless security: you can be yourself and the devices and services you interact with will naturally recognize you based on what makes you unique,” said UnifyID founder John Whaley. Since 2015, UnifyID has been using a combination of signal processing, optimization theory, deep learning, statistical machine learning, and computer science to solve one of the oldest and most fundamental problems in organized society: How do I know you are who you say you are?

To date, the company has developed the first implicit authentication platform designed for online and physical world use. Named RSA’s Unanimous Winner for 2017, UnifyID utilizes sensor data from everyday devices and machine learning to authenticate you based on unique factors like the way you walk, type, and sit. The company has also partnered with global corporations to assess the generalizability of their software across industries.

The UnifyID solution combines over 100 different attributes to achieve 99.999% accuracy without users changing their behavior or needing specific training. The key is the proliferation of sensors combined with innovations in machine learning. UnifyID is the first product to develop neural networks to run locally on the phone to process sensor data in real-time.

“A large percentage of data breaches involve weak, default or stolen passwords, and we think passwords – as we know them – need an overhaul,” said Forest Baskett, NEA General Partner. “We are excited about the world-changing potential of UnifyID’s frictionless, universal authentication solution.”

In the past six months, UnifyID received national attention by winning security innovation competitions at TechCrunch Disrupt, RSA, and SXSW and continued to grow its engineering, machine learning, and enterprise deployment talent. For career and partnership inquiries, learn more at


Headquartered in San Francisco, UnifyID is the first implicit authentication platform. Its proprietary approach uses behavioral and environmental factors to identify users. In February of 2017, the Company was recognized as the most innovative start-up at RSA. For career and partnership inquiries, learn more at



Grace Chang
grace [at]

Vulnerability of deep learning-based gait biometric recognition to adversarial perturbations











2. Methodology and Results



3. Future Work



Our Pledge to Inclusion and Diversity: 1 Year Later




Our team on a normal day in the office.











We encourage more companies to sign this Tech Inclusion Pledge here.