diff --git a/datasets/data.csv b/datasets/data.csv
index 4a3e9f9..ac70f3f 100644
--- a/datasets/data.csv
+++ b/datasets/data.csv
@@ -145,3 +145,4 @@ ID,Main Author,Year,Location,Input Body Part,Gesture,Interaction_PANEL_Number of
 666,test,2026,"Face, Mouth","Facial Expression, Jaw, Tongue","Facial Gesture (Mouth), Slide",12,Semantic,Yes,Visual Attention,No,No,N/A,N/A,67 (N=5),59 (N=5),PPG,Yes,No,No,No,No,Lab/Controlled Room,Sitting,N/A,Rewear,Earbud,Research Prototype,N/A,No,No,"Novel Interaction Technique, System Extension",Device Control/Input,"Earable, Earbuds, Hearable, Human Activity Recognition, Mouth Gestures, PPG, Pulse Wave, Wearable","As wearable computing evolves, hearable devices---earphone-style wearables---are gaining attention. Beyond music playback, they now support voice assistants and biometric sensing for interactive experiences. Especially notable are devices with optical PPG (photoplethysmography) sensors for heart rate monitoring, which benefit from the ear's rich capillary network and stable sensor placement, enabling accurate measurements. However, many hearables require smartphone-based touchscreen control, which can be inconvenient. Alternatives like touch input to hearable devices suffer from accidental activations and physical discomfort, while voice and motion-based inputs raise social and practical concerns. This paper introduces a method to recognize 12 mouth gestures using a PPG-equipped hearable device. Jaw and cheek movements alter blood vessel shapes and ear canal structure, affecting the PPG signal. These signal changes are processed with Continuous Wavelet Transform (CWT) and peak detection, then classified via machine learning. In the evaluation with 5 male participants in their twenties, our system achieved an F-value of 0.67 for 12 gestures, and 0.85 when limited to 8, demonstrating the method's effectiveness. In the classification of 4 gestures, the F value was 0.95, enabling reliable gesture classification for most subjects.",https://dl.acm.org/doi/10.1145/3714394.3756205,Mouth Gesture Recognition Using PPG Sensors in Earbuds,"Taiki Yuma, Kazuya Murao"
 777,test,2026,"Face, Mouth","Facial Expression, Jaw, Tongue","Facial Gesture (Mouth), Slide",12,Semantic,Yes,Visual Attention,No,No,N/A,N/A,67 (N=5),59 (N=5),PPG,Yes,No,No,No,No,Lab/Controlled Room,Sitting,N/A,Rewear,Earbud,Research Prototype,N/A,No,No,"Novel Interaction Technique, System Extension",Device Control/Input,"Earable, Earbuds, Hearable, Human Activity Recognition, Mouth Gestures, PPG, Pulse Wave, Wearable","As wearable computing evolves, hearable devices---earphone-style wearables---are gaining attention. Beyond music playback, they now support voice assistants and biometric sensing for interactive experiences. Especially notable are devices with optical PPG (photoplethysmography) sensors for heart rate monitoring, which benefit from the ear's rich capillary network and stable sensor placement, enabling accurate measurements. However, many hearables require smartphone-based touchscreen control, which can be inconvenient. Alternatives like touch input to hearable devices suffer from accidental activations and physical discomfort, while voice and motion-based inputs raise social and practical concerns. This paper introduces a method to recognize 12 mouth gestures using a PPG-equipped hearable device. Jaw and cheek movements alter blood vessel shapes and ear canal structure, affecting the PPG signal. These signal changes are processed with Continuous Wavelet Transform (CWT) and peak detection, then classified via machine learning. In the evaluation with 5 male participants in their twenties, our system achieved an F-value of 0.67 for 12 gestures, and 0.85 when limited to 8, demonstrating the method's effectiveness. In the classification of 4 gestures, the F value was 0.95, enabling reliable gesture classification for most subjects.",https://dl.acm.org/doi/10.1145/3714394.3756205,Mouth Gesture Recognition Using PPG Sensors in Earbuds,"Taiki Yuma, Kazuya Murao"
 987,test,2026,"Face, Mouth","Facial Expression, Jaw, Tongue","Facial Gesture (Mouth), Slide",12,Semantic,Yes,Visual Attention,No,No,N/A,N/A,67 (N=5),59 (N=5),PPG,Yes,No,No,No,No,Lab/Controlled Room,Sitting,N/A,Rewear,Earbud,Research Prototype,N/A,No,No,"Novel Interaction Technique, System Extension",Device Control/Input,"Earable, Earbuds, Hearable, Human Activity Recognition, Mouth Gestures, PPG, Pulse Wave, Wearable","As wearable computing evolves, hearable devices---earphone-style wearables---are gaining attention. Beyond music playback, they now support voice assistants and biometric sensing for interactive experiences. Especially notable are devices with optical PPG (photoplethysmography) sensors for heart rate monitoring, which benefit from the ear's rich capillary network and stable sensor placement, enabling accurate measurements. However, many hearables require smartphone-based touchscreen control, which can be inconvenient. Alternatives like touch input to hearable devices suffer from accidental activations and physical discomfort, while voice and motion-based inputs raise social and practical concerns. This paper introduces a method to recognize 12 mouth gestures using a PPG-equipped hearable device. Jaw and cheek movements alter blood vessel shapes and ear canal structure, affecting the PPG signal. These signal changes are processed with Continuous Wavelet Transform (CWT) and peak detection, then classified via machine learning. In the evaluation with 5 male participants in their twenties, our system achieved an F-value of 0.67 for 12 gestures, and 0.85 when limited to 8, demonstrating the method's effectiveness. In the classification of 4 gestures, the F value was 0.95, enabling reliable gesture classification for most subjects.",https://dl.acm.org/doi/10.1145/3714394.3756205,Mouth Gesture Recognition Using PPG Sensors in Earbuds,"Taiki Yuma, Kazuya Murao"
+964,test,2026,"Face, Mouth","Facial Expression, Jaw, Tongue","Facial Gesture (Mouth), Slide",12,Semantic,Yes,Visual Attention,No,No,N/A,N/A,67 (N=5),59 (N=5),PPG,Yes,No,No,No,No,Lab/Controlled Room,Sitting,N/A,Rewear,Earbud,Research Prototype,N/A,No,No,"Novel Interaction Technique, System Extension",Device Control/Input,"Earable, Earbuds, Hearable, Human Activity Recognition, Mouth Gestures, PPG, Pulse Wave, Wearable","As wearable computing evolves, hearable devices---earphone-style wearables---are gaining attention. Beyond music playback, they now support voice assistants and biometric sensing for interactive experiences. Especially notable are devices with optical PPG (photoplethysmography) sensors for heart rate monitoring, which benefit from the ear's rich capillary network and stable sensor placement, enabling accurate measurements. However, many hearables require smartphone-based touchscreen control, which can be inconvenient. Alternatives like touch input to hearable devices suffer from accidental activations and physical discomfort, while voice and motion-based inputs raise social and practical concerns. This paper introduces a method to recognize 12 mouth gestures using a PPG-equipped hearable device. Jaw and cheek movements alter blood vessel shapes and ear canal structure, affecting the PPG signal. These signal changes are processed with Continuous Wavelet Transform (CWT) and peak detection, then classified via machine learning. In the evaluation with 5 male participants in their twenties, our system achieved an F-value of 0.67 for 12 gestures, and 0.85 when limited to 8, demonstrating the method's effectiveness. In the classification of 4 gestures, the F value was 0.95, enabling reliable gesture classification for most subjects.",https://dl.acm.org/doi/10.1145/3714394.3756205,Mouth Gesture Recognition Using PPG Sensors in Earbuds,"Taiki Yuma, Kazuya Murao"