Unmet Need: Sensor Data Recognition and Analysis
In today’s connected world, businesses and individuals are more interested in capturing and understanding the data our smart phones, smart speakers, even smart refrigerators transmit. This data-mining craze has created a proliferation of sensor data and we are finding it difficult to track all of these smart devices in a given environment.
The Technology: Adaptive Smart Environment Activity Tracking System
This WSU invention is a smart home, named CASAS that uses artificial intelligence technologies to learn a profile of resident activities in a smart home. CASAS uses the learned information to analyze resident behavior, predict and identify activities, perform automated assessment of resident functional independence, and assist by automation activities in smart homes.
Recent advancements in supporting fields have increased the likelihood that smart home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user’s explicit or implicit wishes. Here we introduce CASAS, an adaptive smart home system that utilizes machine learning techniques to discover patterns in user behavior and to generate automation polices that mimic these patterns. The unique contribution of this work is the ability of CASAS to keep the resident in control of the automation. Users can provide feedback on proposed automation activities, can modify the automation policies, and can introduce new requests. In addition, CASAS continuously updates its models to reflect changes in resident behavior.
• Adult Care Facilities/Remote Care givers
• Retail stores
• Smart Home Consumers
• Uses machine learning technologies to monitor daily activities, identify critical situations, and perform automated assessment of functional performance;
• Predicts events and uses the learned information to provide health monitoring;
• Widely tested in a physical smart environment
Cook, et al., U.S. Patent No.: US 8,417,481 B2