Development of an IoT-Integrated Mobile Training Monitoring App for Horse Trainers

Horse trainers and riders face difficulties in objectively monitoring and analyzing training sessions due to lack of real-time, sensor-based data integration. This limits their ability to optimize training plans, prevent overtraining, and ensure responsible muscle development for horses.

A mid-sized sports technology company specializing in IoT solutions for equestrian training and horse management.

• Create a mobile application capable of integrating with wearable sensors to collect relevant training data in real-time.
• Present training metrics through intuitive visualizations such as graphs and statistics for user-friendly analysis.
• Support early-stage product development, including MVP onboarding and initial user adoption.
• Enable objective assessment of training intensity, gait, and reins usage to improve training outcomes and recovery planning.

• Sensor Data Integration: Seamless connection with dedicated sensors to capture reins usage, gait detection, and session intensity.
• Real-Time Data Visualization: Graphical representation of training metrics for easy interpretation.
• Data Analytics & Reporting: Generate statistics to track progress, identify patterns, and inform training decisions.
• User Management: Support multiple user profiles, including riders and trainers.
• Session Monitoring: Start, pause, and finalize training sessions with data synchronization.
• Notification System: Alerts for abnormal data patterns or session completions.

• Sensor hardware SDKs for real-time data collection
• Cloud storage services for data persistence
• Authentication and user management systems

• App should process real-time sensor data with minimal latency
• Data transmission must adhere to security standards to protect user privacy
• Scalable architecture to support increasing user base and sensor connections
• High availability and reliability for uninterrupted training tracking

The developed application is expected to enable horse trainers and riders to objectively monitor training sessions, leading to more effective and responsible animal training practices. Improved data-driven insights could reduce overtraining risks, optimize muscle development, and enhance overall training efficiency, resulting in increased user satisfaction and higher adoption rates.