Monitoring Practitioner's Skills in Pure-Tone Audiometry

Abstract

So far, there exists no standard, to evaluate a practitioner's skills in pure-tone audiometry. To narrow the gap, this article presents an artificial patient (AP) emulating various types of hearing impairment. In contrast to other solutions, the AP autonomously listens to real pure-tones in soft real-time, while taking into account elements from psycho-acoustics. The emulated patient profiles are reproducible. New profiles can be easily added. The AP is able to recover from error. In this contribution, the authors develop software requirements specifications and derive a modular system architecture. To analyze the performance, the article proposes a stochastic extension to existing synchronous data flow graphs, taking into account the unbounded nature of the tasks' worst case response time. Maximization and summation over the graph reveals the joint distribution of the response time with first and second central moments corresponding to, respectively, the expected response time and the jitter of the task. The theoretical results have finally been validated by measurements on the target.