From aa3df26984071b85a9ee49a3a1b3386eedfc95ae Mon Sep 17 00:00:00 2001 From: Belinda Larocca Date: Sun, 12 Oct 2025 23:32:48 +0000 Subject: [PATCH] Add 'Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise' --- ...ection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md diff --git a/Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md b/Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md new file mode 100644 index 0000000..6fccdab --- /dev/null +++ b/Adaptive-R-Peak-Detection-on-Wearable-ECG-Sensors-for-High-Intensity-Exercise.md @@ -0,0 +1 @@ +
Fascinating stuff. Makes me feel like I really want to enhance my exercise routine. LLMs provide a bonus by eliminating the need for take a look at case development compared to traditional e-assessment systems. AI explainability is especially challenging when based on deep learning fashions, given that among the paths that AI programs use to provide recommendations are usually not interpretable Ehsan and Riedl (2020), and the supply of many generative outputs is advanced (e.g. Kovaleva et al, 2019). While understanding ML in its technical sense is vital, recent approaches in the explainability of AI have pointed at other ways of understandings which are not based on technical explanations and as an alternative, promote experimentation, difficult boundaries, or promoting respect Nicenboim et al (2022) \ No newline at end of file