Prediction of Motion Intentions as a Novel Method of Upper Limb Rehabilitation Support

Abstract

This article is devoted to the novel method of upper limb rehabilitation support using a dedicated mechatronic system. The mechatronic rehabilitation system’s main advantages are the repeatability of the process and the ability to measure key features and the progress of the therapy. In addition, the assisted therapy standard is the same for each patient. The new method proposed in this article is based on the prediction of the patient’s intentions, understood as the intentions to perform a movement that would be not normally possible due to the patient’s limited motor functions. Determining those intentions is realized based on a comparative analysis of measured kinematic (range of motion, angular velocities, and accelerations) and dynamic parameter values, as well as external loads resulting from the interaction of patients. Appropriate procedures were implemented in the control system, for which verification was conducted via experiments. The aim of the research in the article was to examine whether it is possible to sense the movement intentions of a patient during exercises, using only measured load parameters and kinematic parameters of the movement. In this study, the construction of a mechatronic system prototype equipped with sensory grip to measure the external loads, control algorithms, and the description of experimental studies were presented. The experimental studies of the mechanism were aimed at the verification of the proper operation of the system and were not a clinical trial.

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

“…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].…”