Luckily, another fundamental sensor which is often integrated in wearable devices is an accelerometer which helps in detection and removal of this noise. It is also reflected in the frequency domain and overlaps with the frequency range of breath or heart rate.
This noise can appear in the form of unruly signals of large amplitudes in the PPG signals. The largest problem with the proper extraction of these health parameters is that the PPG signals are often measured during various kinds of movement and therefore are corrupted with motion noise. From these features respiratory rate can be extracted. Through physiological mechanisms respiration modulates the blood volume pulse in three different ways: Amplitude Modulation (AM), Baseline Wanderer (BW) and Frequency Modulation (FM). PPG measures the blood pulse wave from which the heart rate, its variations and even the respiratory rate can be extracted. One of these fundamental and widely-used sensors is the Photoplethysmograph (PPG) because it is easy to use, cheap and can help in extracting many health-care parameters of interest. Thus, their inaccuracy can propagate through the system and lead to false diagnoses. This is an important factor since many of these devices have limited number of sensors and many of the extracted information are indirectly obtained through those sensors. However, one of the main challenges that WHSs face is that of accuracy and noise. This can reduce hospitalization and play an important role for the aging population. In contrast, wearable devices can be operated by general public, cost little, and can be deployed to perform their job inside and outside hospitals. Moreover, typical medical devices in the health-care domain that are present in a hospital are expensive and need trained practitioners to operate them. Their applications ranges from daily well-being purposes to emotion recognition, Early Warning Score (EWS), and detection of epileptic seizures. The face of health-care systems across the globe is changing thanks to Wearable Health-care Systems (WHS) and Internet of Things (IoT), and their benefits such as cost effectiveness and the extended information they provide. The insight provided by this paper can help the scientists and engineers to obtain a better understanding of the field and be able to use the most suitable technique for their work, or come up with innovative solutions based on existing ones. In this paper, we review the state of the art algorithms which are used to detect and filter motion artifacts in PPG signals and compare them in terms of their performance. The most important problem is that they are corrupted by motion artifacts.
This poses a challenge on reliable extraction of these metrics. On the other hand, signals, such as PPG from which the RR can be extracted, are not very clean. Part of the reason is that in some cases, such as RR measurements, the devices which directly measure them are cumbersome to wear and thus, rather impractical. Many of these physiological parameters, such as Heart rate (HR) and Respiratory Rate (RR), are extracted indirectly and using other signals such as Photoplethysmograph (PPG).
With the rise of wearable devices, which integrate myriad of health-care and fitness procedures into daily life, a reliable method for measuring various bio-signals in a daily setup is more desired than ever.