Abstract
This demo paper puts forward our interactive and inspectable real-time recognition demo for human activity recognition. The demo captures online data from wearable sensors such as inertial measurement units, recognizes the performed human activity using Hidden Markov Models, and displays the search’s current state along with the recognition results in real-time. Therefore, allowing users to interact with the system and probe its recognition by altering their actions and comparing their expectations with the demo’s performance. Students, Researchers, or the general public can easily extend the detected classes by utilizing the integrated recording, segmentation, and re-training tools — all while running on moderate hardware or even IoT devices like a Raspberry Pi. The demo is titled "ASK2.0" and provides an easy-to-use and powerful way to teach and explain real-time human activity recognition and machine learning.