**1.2. Surface electromyography and kinematic measurements in PD**

Surface electromyography (EMG) and kinematic measurements are non-invasive and relatively simple and cost-effective methods for quantifying neuromuscular function and movement. Therefore, these methods may be suitable for quantifying objectively the motor impairment in PD and the effects of treatment. A few new technologies based on kinematic sensors have been recently commercialized for measuring motor symptoms of PD. The kinematic measurements provide information about human movements. However, it is possible that surface EMG provides earlier or more direct information about PD than the sole kinematic measures based on movement.

Several studies have analyzed the surface EMG and kinematic signals of PD patients in comparison to the signals of healthy subjects and aimed to correlate the most significant findings with the clinical rating scales. Differences between patients and healthy subjects have been observed in the tremor-EMG coherence [50], in the cortico-muscular coherence [37] and in the muscle activation patterns during limb movements [13, 26, 35]. In the gait characteristics, differences have been observed in the gait speed and stride length, in the arm and leg swing and in the muscle activation patterns of gait [5–7, 36, 43].

Several studies have evaluated effects of PD treatment (medication and DBS) on the basis of EMG and kinematic measurements. It has been observed that the medication and DBS may modify the tremor amplitude, regularity and frequency [4, 41, 42], movement speed [3, 8, 34, 40, 44, 49, 51, 52], joint kinetics and muscle activation during movements [55], EMG burst patterns during movement [34, 51, 52] and the cortico-muscular coherence [25, 37]. There is currently a lot of interest for characterizing EMG and kinematic signals of PD patients. However, many studies have analyzed the EMG signals of PD patients by using conventional amplitude- and spectral based methods. More information about PD could be extracted from the EMG signals by using also more modern methods of signal analysis, by analyzing sets of signal features and by analyzing the signal characteristics also on individual level.

EMG signals are impulse-like waveforms because they consist of motor unit (MU) action potentials. The level of MU synchronization is increased in PD [14, 50], which appears as an increased number of recurring spikes and bursts in the EMG signals. Therefore, there is important information about PD in the morphology of the EMG signal and in the recurring signal patterns. It has been observed that the conventional EMG signal parameters (amplitudes and the mean and median frequencies) are not effective in capturing impulse-like structures [23]. Therefore, more modern methods of signal analysis are needed for analyzing the EMG signals of PD patients.

#### **1.3. Our approach for studying surface EMG and kinematic measurements in PD**

In order to extract PD-related information from the surface EMG signals effectively, we proposed specific methods based on signal morphology, nonlinear dynamics and wavelets for analyzing the EMG signals of PD patients in [28–32]. One aim of those studies was to develop objective methods for discriminating between PD patients and healthy subjects on the basis of surface EMG signal morphology [32] and on the basis of simultaneous EMG and acceleration (ACC) recordings during isometric [28] and dynamic muscle contractions [29]. Another aim was to develop methods based on surface EMG and kinematic measurements and analysis for quantifying effects of PD treatment (medication and DBS) on individual level. All of those studies presented an innovative approach, that combines a principal component (PC) -based method with a set of effective signal features, for analyzing the EMG and acceleration signals in PD. In the following sections 2, 3 and 4, we describe the methods that were developed and used for feature extraction and discrimination between subjects in [28–32]. All methods were tested with the measured data. In total, the measurement data from 62 PD patients and 72 healthy subjects were analyzed. The main findings of those studies are also described.
