Caspar AI enables you to build intelligent smart home applications such as fall detection, activity detection, personalization and adaptation for your residents.
Caspar runs on the edge — does not send sensitive data to the cloud, ensuring privacy.
Edge Computing ensures reliability. The AI modules are compiled on your devices for low-latency.
Caspar’s API easily integrates with your smart home IoT platform to help you build innovative applications.
Caspar learns residents’ preferences and activities using CasparAdapt and CasparSense technology.
Our compiler produces local and computationally-efficient AI inference models for your IoT platform to power intelligent applications. It preserves privacy by design and keeps the users’ sensitive data within the home.
CasparSense uses multi-modal sensors within the home to predict complex user activities and intents such as cooking, falling and taking medicine.
CasparAdapt is a library of Deep Co-Reinforcement Learning tools that are designed to intelligently adapt and personalize the home to the residents.
import casparSense
import iotPlatform
platform = iotPlatform(room="bathroom”)
sensor = ["thermal", "sound", "vision"])
sense = casparSense(room="bathroom")
prob = sense.fall()
if prob > 0.5:
platform.alert(method="text")
import casparSense
import iotPlatform
platform = iotPlatform(room="bedroom”)
sensor = ["thermal", "sound", "vision"])
sense = casparSense(room="bedroom")
sense.run()
adapt = casparAdapt(room="bedroom")
@callback (userAction)
def userActionUpdate():
adapt.learnPrefs(sense.activity, userAction)
@callback (sense.activity)
def automatedBehavior():
platform.takeAction(adapt.getLearnedPrefs(sense.activity))
