The minimum sampling rate fsampling is dependent on the maximum frequency contained in the data signal fmax (the sampling theorem) [4]. In the area of AAL, a review of the literature has not uncovered a typical sampling frequency.The highest sampling rate for AAL that the authors found during their research is 512 Hz by [5] followed by the works of [6] where the authors use a sampling rate of 256 Hz to collect accelerometer data. [7] use a two-axis accelerometer and a sampling frequency of 76.25 Hz, which is less than 1/3 of [6] sampling rate. [8] choose fsampling to be 64 Hz. The authors acknowledge the high frequency sampling rate used by [6] however they reduced the sampling frequency on the bases that lower values are more feasible with off-the-shelf activity monitors.

They further mention the work of [9], who sample accelerometer data at 50 Hz, therefore resampling their own data at the same frequency as well. Overall the literature highlights that values around 50 Hz are one of the more common sampling rates. [10] use 52 Hz, [11] use 50 Hz to sample their tri-axial accelerometer, while [12] and [13] also report a 50 Hz sampling rate for an eWatch with two-axis accelerometer and a light sensor. To the authors’ best knowledge, [13] are the only ones that tested different sampling frequencies (from 1 to 30 Hz) for the sensor data. The outcome highlights that the recognition of ADLs improves with higher sampling rates but only marginally improves with sampling rates above 20 Hz. In [14] the authors demonstrate that 98% of the FFT spectrum amplitude is contained below 10 Hz, and 99% below 15 Hz.

This corresponds to the findings of [15] who state that a sampling frequency of 20 Hz is GSK-3 sufficient to successfully classify ADLs. The lowest sampling rate that the authors found in the literature is 5 Hz by [16].2.2. Data Preprocessing Techniques2.2.1. Segmentation MethodOne of the challenges of data pre-processing following acquisition consists in deciding which points to actually use in the live stream of data. Several different segmentation methods exist to divide a larger data stream into smaller fit for processing chunks. The selection of the right segmentation technique is crucial, as it immediately impacts on the extracted features used for the ADL classification and the resulting classification accuracy.

Therefore even the best classifier performance will be weak when the extracted features are non-differentiable [3]. Furthermore, the segmentation techniques can also have an impact on the real time capabilities as complex segmentation methods can increase CL but might result in improved classification accuracy. Moreover, the segmentation method also dictates how often features need to be extracted and classification algorithms need to be executed.