**2. Wavelet in biomedical applications**

For decades, signal processing has been applied to multiple sectors such as industrial, military, health, and entertainment, among others. Regarding the health sector, these techniques have been used to facilitate access to technology and support or complement the diagnosis of a wide variety of diseases. As presented by Suk and Kojima, who use signal processing techniques to clean and extract information from speech signals to make speech recognition, with the purpose of generating a tool that allows disabled people to control by multiple voice home appliances and allow voice control of a wheelchair [31].

Conventional processing techniques and methods allow to filter signals in a frequency range, extract relevant characteristics such as maximum and minimum peaks, fill data by interpolation, and transform and decompose signals in other domains such as frequency and time. Among these processing techniques, wavelet has shown to have a broad application panorama; the literature documents wavelet uses in different and varied fields such as detection of anomalies associated with seismic events in ultralow-frequency geomagnetic signals [32]; it is also possible to use wavelet techniques for image compression, as detailed in [33], who decompose into singular values and use a discrete wavelet transform to improve the maximum ratio of signal-to-noise ratio compared to techniques such as JPEG2000.

In biomedical signals, wavelet transforms have also been suggested for signal compression [34], cardiac pattern recognition [35], EMG classification and decodification [34, 36], feature detection and extraction for ECGs [35] and PPGs [37], and epilepsy diagnosis [38]. Finally, in this chapter, we detail two potential usage scenarios for wavelet techniques, such as gait analysis and arm swing analysis. These two approaches were designed and tested in Parkinson's disease patients, but we consider are not limited to this population, other potential use cases are gait and arm swing alterations in stroke patients, gait analysis in patients with knee replacement, and gait detection and recognition for surveillance.
