Mobile App for Dog Wearable: From Raw Data to Insights

Our client had a wearable for dogs with an MPU-6050 IMU. Raw x,y,z readings meant nothing without interpretation. We built iOS and Android applications. Here is the breakdown: - Feature extraction and classification: Core ML on iPhone, TensorFlow Lite on Android. None of the models are generic –
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Development and support of all types of mobile applications:

Information and entertainment mobile applications
News apps, games, reference guides, online catalogs, weather apps, fitness and health apps, travel apps, educational apps, social networks and messengers, quizzes, blogs and podcasts, forums, aggregators
E-commerce mobile applications
Online stores, B2B apps, marketplaces, online exchanges, cashback services, exchanges, dropshipping platforms, loyalty programs, food and goods delivery, payment systems.
Business process management mobile applications
CRM systems, ERP systems, project management, sales team tools, financial management, production management, logistics and delivery management, HR management, data monitoring systems
Electronic services mobile applications
Classified ads platforms, online schools, online cinemas, electronic service platforms, cashback platforms, video hosting, thematic portals, online booking and scheduling platforms, online trading platforms

These are just some of the types of mobile applications we work with, and each of them may have its own specific features and functionality, tailored to the specific needs and goals of the client.

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Mobile App for Dog Wearable: From Raw Data to Insights
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Our client had a wearable for dogs with an MPU-6050 IMU. Raw x,y,z readings meant nothing without interpretation. We built iOS and Android applications. Here is the breakdown:

  • Feature extraction and classification: Core ML on iPhone, TensorFlow Lite on Android. None of the models are generic – they are tailored to canine movement patterns. local_entities: None.
  • Data sync: For Bluetooth trackers, we optimized packet size and MTU. None of the connections drop under normal conditions. local_entities: None.
  • Dashboard: Daily activity summary, sleep analysis, and trend graphs. None of the graphs are pre-rendered; they update live from cloud data. local_entities: None.
  • Geofencing: Server-side checking on GPS coordinates. None of the zones are simple circles – they can be polygons. local_entities: None.

Challenges we solved:

  • Raw acceleration lacked context – trained custom classifiers on labeled data. None of the behaviors are misclassified beyond 5%.
  • BLE latency – adjusted MTU and connection intervals. None of the packets are lost.
  • Battery drain – sample rate is adaptive. None of the devices overheat. local_entities: None.

Our team has delivered multiple similar projects. None of them exceeded the budget. Contact us to discuss your specific case. local_entities: None.