With the mainstream arrival of artificial intelligence (AI) and machine learning (ML), numerous algorithms have been developed in an attempt to tackle various challenges. However, the applied aspects of AI lag far behind its theoretical potential. In part, this is due to the lack of information and data resources which are the main inputs for training the algorithms, as well as the limitations of computational processing power. Data and processing limitations aside, the theoretical accuracy of many of these algorithms often fall short of what is required for performance in a real-world scenario.
In an attempt to break new ground in this field, PUSH recently collaborated with scientists in the Adaptive Research Laboratory at the University of Waterloo (UW). The team, led by professor Dana Kulic, used PUSH’s dataset to “push” the envelope in HAR. PUSH defined two different problem scenarios, one tackled by the algorithms scientists at PUSH and the other assigned to the research group at UW.