A senior care platform company that uses a proprietary blend of hardware and software to continuously monitor and assess the well-being of seniors. Their solution uses environmental sensors to interpret what a senior is doing at any given time, offering caregivers real-time, data-driven insights into each individual’s wellness.
To further enhance the platform’s intelligence, the client wanted to infer the well-being of seniors based on ambient sounds in their environment. This required distinguishing between a wide variety of sounds—often unique to each living situation—and building a scalable, personalized system that could interpret them meaningfully across hundreds of thousands of devices.
thinkbridge worked with the client to evaluate strategies for identifying, classifying, and interpreting sound patterns in order to deduce wellness insights.
thinkbridge implemented a machine learning-based sound classification system capable of identifying and interpreting ambient sounds in real time. The algorithm learns the patterns and context of sounds within each senior's living space and infers relevant activity or potential concerns—such as detecting a fall, changes in routine, or signs of distress.
The ML model continues to improve through feedback and ongoing data collection, enabling greater personalization with each interaction. The intelligence is embedded into each device, requiring no manual tagging or static rules—and no changes to how caregivers interact with the system.
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