Biosignal-Based Mobile App Development for Smart Clothing

About

A leading smart clothing wearable technology brand partnered with Rishabh Software to develop a tracker app capable of real-time analysis of biosignals transmitted from e-textiles. We were engaged to facilitate near real-time transmission of biosignals received from smart clothing, allowing machine learning algorithms to predict health conditions and trigger alerts instantly. The collaboration aimed to empower users to manage stress and prevent fatal conditions through timely insights and interventions.

Capability

Healthcare Analytics

Industry

Healthcare

Country

UK

Key Features

We developed biosignals monitoring mobile app specifically tailored to the needs of our client’s smart clothing for health monitoring. The app offers seamless access to real-time biosignals recorded from smart clothing. It enables users to actively monitor their health and take preventive or corrective actions to manage stress and mitigate the risk of fatal conditions.

The key features of the mobile app include:

Stress monitoring

Tracks physiological indicators of stress, such as skin temperature, EEG, ECG, and heart rate, to determine the optimum zone for people doing workouts. It provides users real-time data on their stress levels and notifies them about deviations.

Fitness tracking

Alongside monitoring physical activity, calorie count, and exercise performance, the app offers enhanced insights into muscle usage during workouts. Users can visualize the activation of different muscle groups in real-time and aim for more targeted and efficient workouts.

Integration with smart clothing

Seamlessly integrates with intelligent clothing sensors to gather biometric data and sync with the mobile app for centralized data analysis and visualization.

Actionable insights

The mobile interface presents stress and fitness data captured from smart clothing in an actionable format. It allows users to make informed decisions about their physical and mental well-being.

Predictive analytics

We developed advanced algorithms to anticipate health pattern changes and alert users when it’s time to slow down and hydrate. It ensures optimal performance and well-being. This feature allows fitness and healthcare professionals to implement personalized interventions for better lifestyle and health outcomes.

Smart clothing connectivity

Through the user’s personal account, seamless integration allows for adding new smart tech clothing items while providing real-time monitoring of battery status for connected garments.

Smart clothing application data dashboard

Challenges

To safeguard sensitive personal data collected from smart clothing against potential breaches to ensure compliance with stringent privacy regulations.
To ensure robust and secure data transmission between e-textiles and mobile applications while overcoming connectivity and security challenges.
Critical need for a scalable solution that could grow with user demand while strictly adhering to global data privacy and protection standards.
Needed data analytics expertise to develop highly accurate and reliable machine learning models that could accurately predict health conditions from biosignals.
Robust data processing and analysis capabilities to transmit biosignals in near real-time.

Solution

To address the client’s challenges, we developed a robust backend system and used a scalable messaging framework to ensure real-time processing of biosignals from smart wearable clothing sensors. We also leveraged advanced technologies such as Java NIO, Apache Kafka, and Netty to ensure high-performance data transmission and ingestion, capable of handling massive volumes of data with minimal latency.

Advanced backend system development

With a focus on reliability and scalability, we engineered a powerful backend system. It can effortlessly manage the real-time receipt of biosignals from over 10,000 devices, processing over 100GB of data per hour. Leveraging Java NIO, we ensured lightning-fast data processing, facilitating near real-time analysis of health data.

Dynamic temporal feature construction

Our expertise in data science and machine learning enabled us to construct temporal features based on non-stationary, periodic systems. We developed algorithms capable of analyzing time-series data from biological systems connected to smart textiles. This solution played a crucial role in predicting health conditions and detecting stress levels in near real-time.

Data science pipeline implementation

Our data science pipeline, powered by Apache Kafka, Spring Boot, and TensorFlow, ensured seamless data ingestion and processing workflows. We orchestrated containerization for scalability and reliability. This streamlined pipeline facilitated the development of machine-learning models for analyzing biosignals and predicting health outcomes.

Activity modeling and biosignal analysis

Smart clothing sensors track biosignals and workout intensity for various activities like cycling, running, weightlifting, and push-ups. Live streaming from the mobile app syncs with a 3D model to show real-time muscle tension. Users can monitor muscle activity, productivity, performance, emotional state, and changes in temperature and blood flow. The app alerts users to potential training injuries and offers advice to prevent harm.