Activity Recognition and Explanations for Cancer Health Awareness

Cancer patients' activity level and performance is difficult to assess outside the clinical setting. Patients are often not able to communicate subtleties in activity performance that may indicate secondary concerns or true pain levels. There is a need for doctors to monitor patient activity performance at home, especially to identify performance anomalies that may require the doctor's intervention. Many black box activity classification algorithms lack the specificity and succinctness required to alert doctors of degrading health issues, such as suddenly requiring a cane to walk. Additionally, traditional black box classification systems lack the ability to deliver personalized information on activity performance. For instance, in the previous example a doctor does not need to know if a patient who already uses a cane is using a cane during their exercises. By combining deep learning models and symbolic reasoning we have created the Characterizing Human Activities for Cancer Health Awareness (CHA-CHA) system to classify exercises performed at home and alert the doctor of patient specific anomalies in their performance.