Fig. 1
From: Wrist accelerometer shape feature derivation methods for assessing activities of daily living
Bag-of-words variable representation. a First, the most common acceleration patterns (i.e., atoms) are discovered and gathered in a codebook. b Second, the time-series accelerometer data for a PA are split into subsequences. c Each subsequence is replaced with the label of the most resembling atom. After this step, the accelerometer data are converted to bag of words, which are used to calculate word frequencies for each PA. A normalization term-frequency function (i.e., inverse document frequency) is applied to adjust the values of the variable vectors (i.e., word frequencies) and make them suitable for machine learning methods