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Language Activity Monitoring (LAM): Extended Data Logging to Handle Character-based Languages

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Evidence-based practice is an expectation of AAC intervention. Collecting data related to communication performance assists clinical professionals in measuring the effectiveness of AAC interventions. Currently, software is available to support the analysis of logfile data to measure AAC communication performance. However, the current Language Activity Monitoring (LAM) format only provides for alphabet-based languages. The standard format cannot be used to collect logfiles generated from the AAC device that supports character-based languages, e.g. Chinese, or Japanese. In order to handle the limitations of the current LAM, this study aimed to propose rules for handling data that are generated from spelling-based and character-based languages in an AAC device. Though analyzing the formats and features of current LAM logfiles, and the features of the text entry methods of character-based language, the authors propose universal principles for logfile formats. The results of simulated testing indicated these proposed rules solve the limitations of current LAM data.


Ming Chung Chen    
Carnegie Mellon University
United States

Katya Hill    
Pittsburgh University
United States

Eric Nyberg    
Carnegie Mellon University
United States

Szu-Han Chen    
University of Pittsburgh
United States


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