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.

翻译：监测运动强度对于安全有效的体力活动至关重要，尤其是对心血管疾病患者而言，过度 exertion 可能带来严重风险。虽然心率等生理指标被广泛用于避免过度 exertion，但在某些情况下（如受药物影响或可穿戴设备佩戴过松时）可能不可靠。我们提出 AktivTalk，一种将临床验证的谈话测试数字化的移动原型，支持基于语音的即时 exertion 自我评估。在包含20名受试者的受试者内研究中，我们收集了带有 exertion 标签的语音样本，发现 AktivTalk 被评为高度可用，且优于引导式评估。我们进一步探索了基于谈话测试语音的自动 exertion 分类。使用基于 MFCC 的特征，结合类别平衡和交叉验证，一个轻量级神经网络分类器在检测谈话测试录音中的高与非高 exertion 时达到了90%的准确率。这项工作凸显了结构化语音交互在便捷 exertion 评估中的潜力，并为未来基于语音的被动 exertion 监测提供了动机。