人類活動偵測的資料集是來自 30 個實驗者在The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. 最終目標是將活動歸入六個可能的活動的其中一類。The objective is to classify activities into one of the six activities performed.
Description of experiment
實驗是在 30 位 19 到 48 歲的成年人身上進行的。每個都會在手腕上配戴智慧型手機 (Samsung Galaxy S II)，並進行以下 6 種動作：走路、上樓梯、下樓梯、靜坐、靜止站立、躺下。
實驗者利用手機內建的加速度感測器和能用來感測方向的陀螺儀，以每秒 30 次的頻率蒐集 3 維空間的加速度與角速度資料。此外，為了手動標示資料，實驗全程被全程錄影。最終得出的資料集已隨機區分的方式區分為 70% 訓練資料和 30% 測試資料。The experiments have been carried out with a group of 30 volunteers within an age bracket of 19–48 years. Each person performed six activities (WALKING, WALKINGUPSTAIRS, WALKINGDOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist.
Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.
在過濾雜訊並
The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
