1. USING A SPOKEN DIARY AND HEART RATE MONITOR IN MODELING HUMAN EXPOSURE TO AIRBORNE POLLUTANTS FOR EPA’S CONSOLIDATED HUMAN ACTIVITY DATABASE Curry I. Guinn, UNC Wilmington Daniel J. Rayburn Reeves, UNC Wilmington

2. Collecting Human Activity Data Purpose: To develop a method of generating an activity/location/time/energy expenditure database of sufficient detail to accurately predict human exposures and dose.

3. Goals of our study To Evaluate To Evaluate the use of digital voice recordings the use of digital voice recordings the use of the ambulatory heart rate monitor the use of the ambulatory heart rate monitor participant/instrumentation interactions participant/instrumentation interactions To Develop To Develop a protocol for automating the processing of voice recordings a protocol for automating the processing of voice recordings an autocoding program that will be able to map the text of the diary entries to CHAD an autocoding program that will be able to map the text of the diary entries to CHAD

4. Problems with Collecting Human Activity Data Recall Data Recall Data Failure to recollect many daily activities Failure to recollect many daily activities Lack of detail Lack of detail Real-Time Paper Diaries Real-Time Paper Diaries Increased number of reports/better detail Increased number of reports/better detail Burdensome Burdensome Direct Observation Direct Observation Greatest number of reports/most detail Greatest number of reports/most detail Inefficient and expensive Inefficient and expensive

5. The Experimental Platform Data Collection Data Collection Audio diary using a digital voice recorder Audio diary using a digital voice recorder Ambulatory Monitoring System that monitors heart rate and prompts subjects to provide diary entries when heart rate increases by a specified criterion level. Ambulatory Monitoring System that monitors heart rate and prompts subjects to provide diary entries when heart rate increases by a specified criterion level.

6. Digital Voice Recorder Allows “Real-Time” Activity/Location Data to be Collected Easily Allows “Real-Time” Activity/Location Data to be Collected Easily Reduce burden of paper or computerized diary entries Reduce burden of paper or computerized diary entries Relies on efficient, simple naturally spoken reports Relies on efficient, simple naturally spoken reports Potentially richer, more detailed reports Potentially richer, more detailed reports No restrictive diary format No restrictive diary format Electronic format Electronic format

7. Ambulatory Monitoring System Provides an objective measure of exertion that is more reliable than self-reported respiratory rates Provides an objective measure of exertion that is more reliable than self-reported respiratory rates Prompts subjects to report activity when heart rate variation exceeds criterion levels Prompts subjects to report activity when heart rate variation exceeds criterion levels

8. Natural Language Processing Application Applies contextual language constraints to facilitate speech-to-database conversion Applies contextual language constraints to facilitate speech-to-database conversion Speech  Text  Database Encoding Processes and codes the diary reports using the CHAD code scheme Processes and codes the diary reports using the CHAD code scheme Reduce need for manual transcription and coding Reduce need for manual transcription and coding

9. Spoken Diary From an utterance like “I am on the bus on my way to South Square Mall”, map that utterance into From an utterance like “I am on the bus on my way to South Square Mall”, map that utterance into 18400: Travel for goods and services 18400: Travel for goods and services 31140: Travel by bus) 31140: Travel by bus) Text abstraction Text abstraction Technique Technique Statistical language processing using n-grams and Bayesian statistics Statistical language processing using n-grams and Bayesian statistics

10. Subjects IDSexOccupationAgeEducation 1F Manages Internet Company 52 Some College 2F Grocery Deli Worker 18 Some College 3M Construction Worker 35 High School 4F Database Coordinator 29GraduateDegree 5F Coordinator for Non-profit56 Some College 6MUnemployed50 High School 7MRetired76 8MDisabled62 9MEnvironmentTechnician56GraduateDegree

11. Voice Diaries Average: 29 entries/ day Average: 29 entries/ day With average monitoring time of 8.56 hours, 3.39 recordings/hour With average monitoring time of 8.56 hours, 3.39 recordings/hour First 3 days of trial: 34.44/ day First 3 days of trial: 34.44/ day Last 2 days of trial: 20.65/ day Last 2 days of trial: 20.65/ day 1 out of 63 reporting periods data lost (1.6%) 1 out of 63 reporting periods data lost (1.6%)

12. Recordings Per Day

13. Quality of Diary Entries Advantages Advantages High Entry Rate High Entry Rate Timed correlation with heart rate data Timed correlation with heart rate data Disadvantages Disadvantages Little prompting Little prompting Unformatted data Unformatted data Variable reporting of subjects Variable reporting of subjects

14. Quality of Diary Entries Entry Length Entry Length 9.39 words average 9.39 words average Some entries invalid because of length (subject failed to turn off recording) Some entries invalid because of length (subject failed to turn off recording) 1/30 recordings (3%) 1/30 recordings (3%)

15. Heart Rate Change Indicator Tones and Subject Compliance S Number of Tones Per Day (Avg.) % of Times Subject Made a Diary Entry Corresponding to a Tone 122.145% 241.829% 332.536% 433.055% 533.336% 615.640% 732.537% 826.022% 922.731%

16. Statistical Processing Accuracy of Hand- Transcribed Data with Threshold of 0.3 Subject Act. Prec. Act. Recall Loc. Prec. Loc. Recall 193.366.676.561.9 254.527.962.546.5 355.635.762.547.6 482.551.574.160.6 587.566.078.073.6 687.939.967.348.6 768.164.055.980.6 860.440.456.068.4 989.572.393.085.1 Total75.5%51.6%69.5%63.7%

17. Threshold values affect the precision and recall: The higher the threshold, the greater the precision but the lower the recall

18. Time, Activity, Location, Exertion Data Gathering Platform

19. Research Topics How do we fuse data from other sources (gps, beacons, heart rate monitor, etc.)? How do we fuse data from other sources (gps, beacons, heart rate monitor, etc.)? How do we provide interactive prompts to the subject to improve reporting? How do we provide interactive prompts to the subject to improve reporting?