tinyML Summit 2021 https://www.tinyml.org/event/summit-2021
“Insights from a Multi-Purpose Self-Learning Smart Sensor”
Kaustubh GANDHI, Senior Product Manager Software, Bosch Sensortec
Edge-AI devices need to ensure context-sensitive adaptation and real-time personalization for end-users. In this talk, we introduce some insights gained while designing Bosch’s novel self-learning sensor.
The sensor’s self-learning function enables the device to learn new motion patterns in-use directly from the end-user, to personalize built-in patterns directly for an end-user and automatically classify and count the movement types in real-time, all within the sensor itself.
In spite of delivering an AI experience, the function runs on sensor’s co-processor with ca. 300 µA and memory under 50 KB, while yet delivering over 90% accuracy for personalized home workouts. This is significant improvement for learning at the edge on wrist and in-ear wearables.
Secondly, as the sensor is capable of switching to a different function in run-time, sensor purpose can change depending on user’s context, such as the orientation and position tracking during running, style classification during swimming or personalization during fitness workouts.
Thirdly, the design allows the self-learning feature to utilize an expandable list of virtual sensors from sensor data fusion (e.g. quaternions) and peripherals (e.g. magnetometer, pressure sensors).
This enables faster and robust pattern detection from an expandable list of input sources, chosen according to target application, as against to pre-programmed AI solutions with fixed inputs.