## 3 Data Fusion
### State-of-the-Art Research on Data Fusion
#### 3.1.1 Preprocessing  
When detecting human body physiological parameters, data collected by sensors is often affected by interferences, leading to a lot of noise. Reference [40] states that these disturbances are from two sources. Firstly, low Signal to Noise Ratio (SNR) signals caused by irregular movements and human respiratory actions. Secondly, interference with signal acquisition and transmission caused by instrumentation amplifiers in the surrounding environment or in sensors of the system itself. Noise types also vary due to the different types of sensors and different applications. MAs, such as baseline drift, flicker noise, and thermal noise, as well as wire interference may be caused by instrumentation amplifiers in measuring ECG, EEG, and EMG. High and low frequency noise will be introduced during the signal collection process by inertial sensors measuring body movements.  
Preprocessing is an efficient and effective way to carry out the denoising process on original data while maintaining useful information. The main techniques include piecewise linear representation, FT, WT, high pass and low pass filters, mathematical morphology filters, and Kalman filter. For example, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) are naturally suited for non-continuous data processing [23]. Techniques such as low-pass median value filter, Laplacian Transform, and Gaussian filter can be used to remove high frequency noise. The Kalman filter is an optimal recursive data processing algorithm, which is always used to correct measured values based on the observed value of the current moment and the best estimated value of a previous moment. It plays an important role in dealing with measured values which are not very accurate.  
Using dry electrodes is one of the significant factors which increases power line interference (PLI) and MA, and finding solutions to reduce these noises or interferences is an important issue. A lot of methods are proposed to remove the PLI in bio-potential signals, such as the notch filter and analog notch filter [63]. However, the removal of MA in wearable healthcare systems is more complex. Due to the nature of wearable devices, the captured ECG signals are severely distorted with baseline drift and MAs [64]. In Reference [65], researchers introduced the multiscale mathematical morphology filtering concept into QRS detection, while the method of mathematical morphology, initiated in the late 1960s, has become one of the favorite signal analysis tools in many shape-oriented problems [66,67]. They successfully designed the ECG QRS complex detection algorithm based on it. Other researchers use the algorithm in different ways. They utilize hybrid mathematical morphology filtering to suppress impulsive noise when doing opening-closing operations to get a more effective QRS detection [68]. This can remove baseline drift and use modulus accumulation to enhance signals. In Reference [63] a single-scale mathematical morphological (MM) filter and an approximated envelope are combined together. This MM filter removes baseline wand erring, impulsive noise, and the offset of DC componentd while the approximated envelope enhances QRS complexes. These algorithms have been further improved in removing MAs and other interference. In order to realize activity recognition and get accurate characteristic values which are not affected by system noises, adopting a Kalman filter can obtain precise parameters of moving objects, such as instantaneous angular and coordinate data. As data collected from sensors are not absolutely correct when monitoring movement and posture, researchers put the Kalman filter into use for processing data, so as to make output result close to the real data. In a practical application, a Kalman filter is always adopted to process acceleration data [69]. It can also be used in applications such as gait tracking to calculate thurl angle and swing angular velocity.