--- tags: - time series - time series classification - monster - other - sensor license: other pretty_name: FordChallenge size_categories: - 10K. |FordChallenge|| |-|-:| |Category|Sensor| |Num. Examples|36,257| |Num. Channels|30| |Length|40| |Sampling Freq.|10 Hz| |Num. Classes|2| |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] (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. . Kaggle.