Submovements composition and quality assessment of reaching movements in subjects with Parkinson's Disease

Abstract

The segmentation of seemingly continuous movements into segments has been theorized for many years. These segments may be considered as 'primitive' movements, or building blocks of more complex movements. The existence of these fragments, or sub-movements as they are called, has been supported by a wide range of studies over the past 100 years. Evidence for the existence of discrete sub-movements underlying continuous human movement has motivated many attempts to 'extract' them. Recently, the sub-movement theory gained a great appeal in the rehabilitation field. In fact, understanding movement deficits following CNS lesions, and the relationships between these deficits and functional ability, is fundamental to the development of successful rehabilitation therapies. So, here a novel sub-movements decomposition method is proposed; it is based on a constrained-Expectation-Maximization. This representation allowed us to explore whether the movements are built up of elementary kinematic units by decomposing each signal into a weighted combination of 2D Gaussian functions. These can be used to assess the quality of reaching movements in subjects with Parkinson's Disease

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

“…Specifically, after a signal processing routine based on custom-made software, thirteen kinematic parameters, related to upper limb tasks, were estimated and, subsequently, statistically analyzed. From these analyses, it has been found that a subset of features (namely, maximum velocity, skewness, kurtosis, and smoothness) effectively allowed, in the first instance as proof-of-concept, distinguishing the reaching movements performed by healthy and Parkinson's Disease subjects, further confirming the preliminary evidence highlighted in a previous work [18].…”

“…• mean velocity (v_mean): obtained as the ratio between the amplitude and the duration of the submovement (expressed in degrees per second); • maximum velocity (v_max): maximum value of the velocity within the submovement (expressed in degrees per second); • maximum acceleration (a_max): maximum value of the acceleration within the submovement (expressed in degrees per squared second); • maximum jerk (jerk_max): maximum value of the jerk within the submovement (expressed in degrees per cubic second); • coefficient of symmetry (symmetry): obtained as the ratio between the duration of the deceleration phase and the duration of the acceleration phase within the sub-movement (expressed in dimensionless units), as described in our previous studies [18,25,40,41]; • mean value of the position (p_mean): calculated as the mean of the Gaussian-like morphology of the velocity profile of the sub-movement, as described in our previous studies [18,25,40,41]; • mean square root value of the position (p_root_mean): calculated as the mean square root value of the Gaussian-like morphology of the velocity profile of the submovement, as described in our previous studies [18,25,40,41]; • variance: calculated as the variance of the Gaussian-like morphology of the velocity profile of the submovement, as described in our previous studies [18,25,40,41]; • skewness of the velocity profile (skewness): calculated as the skewness of the Gaussianlike morphology of the velocity profile of the submovement, as described in our previous studies [18,25,40,41]; • kurtosis of the velocity profile (kurtosis): calculated as the kurtosis of the Gaussian-like morphology of the velocity profile of the submovement, as described in our previous studies [18,25,40,41]; • smoothness: calculated as the integral of the third time derivative of the position over the submovement, as described in our previous studies [18,25,40,41];…”

“…Several biomechanical parameters have been proposed in the scientific literature to date for assessing the quality of movement in healthy and diseased subjects in different rehabilitation settings and tasks [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Moreover, several studies have aimed to exploit motion analysis data and compare different instrumentations for diagnostic purposes [32,33].…”

Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models

“…Such parameters are suitable to investigate the "quality" of movement of healthy and diseased subjects; even more important, they can be monitored in different rehabilitation settings and for different tasks. Approaches in this sense have been proposed for robot-mediated or robot assisted rehabilitation exercises [21], to improve upper limb [22] or lower limb rehabilitation [23], after spinal cord injuries [24], stroke [25], or PD [26], [27], also with the aim of proposing low-cost robot platforms for physiotherapy [28], [29] and intelligent data-driven approaches for gait training [12], motion reaching tasks [30], [31], [32], and motion prediction purposes [33].…”