Android app development for cognitive function analysis: 35% growth in active users

Innowise has developed an Android app and built an SDK that leverages AI to analyze cognitive functions and enhance the accuracy of data analysis.

Customer

Industry

Healthcare

Region

Switzerland

Client since

2023

Our customer is a neuroscience company specializing in developing an innovative technology platform for the early detection of cognitive disorders. By utilizing mobile applications, they collect and analyze user interaction data with the screen, enabling the identification of potential issues at early stages.

The company actively collaborates with leading neuroscience researchers and practitioners worldwide to advance brain health knowledge. They provide the scientific community with robust data analysis tools, supporting the development of new diagnostics and treatments.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Challenge

Develop an Android app for cognitive function analysis

The client had an iOS application for brain function analysis and aimed to expand its reach to Android users. However, the company had only iOS developers and didn’t see the benefit of hiring additional in-house engineers.

Innowise stepped in to tackle this challenge: developing a full-featured Android app that replicates the functionality of the iOS version while ensuring seamless data synchronization across both platforms. Additionally, the project required integrating advanced AI technologies for comprehensive analysis of the collected data.

Solution

Android application and SDK development with AI

Before starting work, the Innowise team conducted a thorough analysis of the project requirements and objectives. Leveraging our extensive experience in developing complex mobile solutions, we proposed tried-and-tested strategies and approaches that ensured the achievement of all project goals, meticulously considering all client’s wants and needs.

Android SDK development

Our team started by developing a comprehensive SDK for Android. This SDK is designed to gather information on the frequency and timing of screen taps and the overall time spent using the phone. Using Java and Kotlin, we built the SDK to be both flexible and robust. With the SDK in place, we then proceeded to develop Android mobile applications.

Android app development

To create a mobile application for Android, we went with Java and Kotlin for a smooth experience. The app runs in the background, gathering data from on-screen interactions and sending it to the server for analysis.

Our developers used Dart programming language to build the app’s logic and user interface, integrating it with the Android SDK. We made sure the app has minimal impact on device performance and battery life by optimizing the code and using efficient data processing algorithms.

AI integration

AI technology integration was a cornerstone of this project, aimed at the early detection of subtle signs indicative of possible cognitive impairment. We selected Python and the powerful TensorFlow framework as the foundation for model development. This choice enabled us to create flexible and efficient models capable of handling complex data related to users’ interactions with the screen.

To ensure high accuracy and robustness, the models were trained on extensive clinical study datasets encompassing various user behavior patterns. This comprehensive training allowed the models to recognize even minor abnormalities characteristic of early cognitive impairment stages.

The models analyzed a wide range of data, including:

Reaction time: The speed at which the user responds to different stimuli on the screen.
Touch accuracy: How accurately the user hits the right elements of the interface.
Motion trajectory: The nature of finger movements on the screen (straight-line, zigzag, etc.).
Typing dynamics: The speed and rhythm of typing on the virtual keyboard.

Additionally, we developed a mechanism for data transfer between the mobile applications and the server. This enabled real-time data analysis, providing immediate results to users and researchers.

Improved usability

Our experts developed an intuitive app interface where data is presented through clear graphs and charts. For example, the dynamics of user reaction time over a specific period are displayed as a graph, with the X-axis representing time and the Y-axis representing reaction time. This visualization allows for quick identification of potential issues, such as deviations from the norm or trends indicating a decline in performance.

Each visualization is accompanied by clear textual explanations. For instance, alongside the reaction time graph, there could be an explanation stating that an increase in reaction time may suggest a slowdown in cognitive processes. This approach makes the data easier to interpret and helps users quickly identify and address any potential issue.

Data security

To ensure that user data is securely protected and meets advanced security standards, the Innowise team has implemented a bulletproof approach to data protection:

Data encryption: Before being sent to the server, data is encrypted using the robust AES-256 algorithm, rendering it unreadable to unauthorized users.
Encryption key protection: Encryption keys are stored in a secure, restricted-access vault to prevent any potential compromise.
Reliable infrastructure: Data is stored on Microsoft Azure servers, providing a high level of data security.
Minimal data collection: Only the necessary data for analysis is collected, explicitly excluding users’ personal information.
Strict access control: Access to data is granted only to authorized users, making sure it doesn’t fall into the wrong hands.

Technologies

Programming languages

Java, Kotlin, Dart, Python

Frameworks and libraries

TensorFlow

Testing

JUnit, Espresso

VCS

Git

Cloud

Microsoft Azure

Project management

Jira

Development environment

Android Studio

APIs

RESTful APIs

Process

For this project, we used Scrum with two-week sprints to keep things organized and on track. We broke down the workflow into flexible iterations, letting us adapt quickly and meet deadlines.

Our dedicated project manager handled task assignments, action plans, deliverables, and milestone coordination with the customer. Regular status-check meetings three times a week kept everyone updated and allowed for timely adjustments. The customer was actively involved in the process, providing valuable feedback and information during our daily check-ins.

Team

Project Manager

Android Developers

ML Engineers

Data Security Expert

UI/UX Designer

Results

Boost audience engagement and enhance data analysis accuracy

The client has got a tried-and-tested solution that’s now a valuable tool in neuroscience research and diagnostics. Launching the Android app has led to a 35% increase in active users, expanding their reach and allowing for more diverse data collection.

With AI integration, data analysis accuracy has surged by 25%, making insights more precise and reliable. This enhancement has not only improved the validity of their findings but also added significant value to cognitive assessments — helping researchers and users spot potential issues earlier and with greater confidence.

The project has significantly expanded the client’s ability to research and analyze cognitive functions, solidifying their role as a leader in brain research tech.

Project duration

August 2023 - February 2024

35%

increase in active users

25%

increased accuracy of data analysis

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