AktivTalk: Digitizing the Talk Test for Voice-Based Exercise Intensity Self-Assessment and Exploring Automated Classification from Speech
This work addresses the need for accessible exertion monitoring, particularly for individuals with cardiovascular disease, though it is incremental as it builds on the existing Talk Test method.
The researchers tackled the problem of unreliable exercise intensity monitoring by developing AktivTalk, a mobile prototype that digitizes the Talk Test for voice-based self-assessment, achieving up to 90% accuracy in automated classification of high vs. non-high exertion from speech.
Monitoring exercise intensity is critical for safe and effective physical activity, particularly for individuals with cardiovascular disease, where overexertion can pose serious risks. Although physiological measures such as heart rate are widely used for avoiding overexertion, they can be unreliable in certain cases, such as when affected by medication or when wearables are worn too loosely. We introduce AktivTalk, a mobile prototype that digitizes the clinically validated Talk Test to support voice-based, in-the-moment self-assessment of exertion. In a within-subject study with 20 participants, we collected exertion-labeled voice samples and found that AktivTalk was rated as highly usable and preferred over conductor-guided assessment. We further explored automated exertion classification from Talk Test speech. Using MFCC-based features with class balancing and cross-validation, a lightweight neural classifier achieved up to 90% accuracy for detecting high vs.non-high exertion from Talk Test recordings. This work highlights the potential of structured voice interactions for accessible exertion assessment and motivates future passive exertion monitoring from speech.