Update README.md

README.md

@@ -23,6 +23,6 @@ Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
 |License|Other|
 |Citations|[1]|
 
-***FordChallenge*** is obtained from Kaggle and consists of data from 600 real-time driving sessions, each lasting approximately 2 minutes and sampled at 100ms intervals [1]. These sessions include trials from 100 drivers of varying ages and genders. The dataset contains 8 physiological, 11 environmental, and 11 vehicular measurements, with specific details such as names and units undisclosed by the challenge organizers. Each data point is labeled with a binary outcome: 0 for "distracted" and 1 for "alert". The objective of the challenge is to design a classifier capable of accurately predicting driver alertness using the provided physiological, environmental, and vehicular data.
+***FordChallenge*** is obtained from Kaggle and consists of data from 600 real-time driving sessions, each lasting approximately 2 minutes and sampled at 100ms intervals [1] (i.e., a sampling rate of 10 Hz). The processed dataset consists of 36,257 multivariate time series each of length 40 (i.e., representing 4 seconds of data per time series at 10 Hz).  These sessions include trials from 100 drivers of varying ages and genders. The dataset contains 8 physiological, 11 environmental, and 11 vehicular measurements, with specific details such as names and units undisclosed by the challenge organizers. Each data point is labeled with a binary outcome: 0 for "distracted" and 1 for "alert". The objective of the challenge is to design a classifier capable of accurately predicting driver alertness using the provided physiological, environmental, and vehicular data.
 
 [1] Mahmoud Abou-Nasr. (2011). Stay Alert! The Ford Challenge. <https://kaggle.com/competitions/stayalert>. Kaggle.