3.2.2.2 Human activity recognition (HAR) task [3]

Dataset. The dataset used for HAR is the PUC dataset [34] which consists of 8 hours of human activities collected at a sampling frequency of 8 Hz by 4 tri-axial ADXL335 accelerometers located at the waist, left thigh, right ankle, and right arm. The activities are: sitting, standing, sitting down, standing up, and walking.

Pre-processing. The PUC data is further converted into time and frequency domain signals. In time-domain, a 1 s time window (e.g., L ¼ 8) with 125 ms overlapping (e.g., s ¼ 1) is employed to generate 8 � 1 time-domain samples. However, knowing that an 8 � 1 input matrix is not a vector long enough for training a CNN, signals are resampled from 8 to 50 using an antialiasing FIR low-pass filter and compensating for the delay introduced by the filter. The resultant time-domain input samples are 50 � 1 � 12 matrix where 12 stands for the number of channels (14accelerometers � 3coordinates). In frequency-domain, raw signals are resampled from 8 to 16 Hz; then, the ST is computed to obtain, for each input sample, the power of 50 frequencies in the range of 0–8 Hz, resulting in frequency-domain input samples of size 50 � 1 � 12.

CNN training. In this experiment, one CNN is trained for each domain (time and frequency domain), with a 10-fold cross-validation. CNN architecture and parameters are set the same as in the SMM recognition task.
