[ARTICLE] Multi-Sensor Validation Approach of an End-Effector-Based Robot for the Rehabilitation of the Upper and Lower Limb – Full Text

Abstract

End-effector-based robots are widely adopted by physiotherapists and caregivers as support in the delivery of the rehabilitation training to the patient. The validation of these devices presents critical aspects, since the system performance must be assessed analyzing the movement performed by the subject limb, i.e., elements outside the device. This paper presents a multi-sensor approach for the validation of an innovative end-effector-based device, comparing different measurement strategies for evaluating the system effectiveness in imposing an expected training. The study was performed monitoring the movement induced by the device on the upper limb of a young male healthy subject during a set of fictitious rehabilitation sessions. The kinematic structure of the device is characterized by a compact differential mechanism with two degrees of freedom. A sequence of repetitions of a planar reaching pattern was analyzed as illustrative training task. A kinematic model of subject and system was developed, and the kinematics of a set of specific landmark points on the subject limb was evaluated. Data obtained from two measurement systems were compared: (1) an optoelectronic system with two cameras and eight skin passive markers, and (2) two triaxial accelerometers. Results were analyzed in MATLAB and R environment, revealing a high repeatability of the limb movement. Although both the measurement systems allow evaluating the acceleration of subject’s arm and forearm, accelerometers should be preferred for punctual analysis, like components optimizations, whereas optical markers provide a general overview of the system, particularly suitable for the functional design process.

1. Introduction

The motor skills reduction in subjects affected by neurologically based disorders, like stroke, spinal cord injuries and traumatic brain lesions, strongly influences the quality of life [1,2,3]. In particular, the ability of independently and self-sufficiently execute everyday motor tasks is greatly reduced by extremities functional limitations [4]. For this reason, more than restoring the capacity of realizing a task in the natural way, the primary aim of rehabilitation techniques is allowing the execution of the lost motor functions, re-educating the subjects to coordinate movements. Besides, motor and neuro-motor rehabilitation, combined with the use of orthoses and functional electrical stimulation, improves also the subject mental abilities and prevents secondary complications such as spasticity, muscle atrophy and osteoporosis [5].The physical rehabilitation process begins with a preliminary analysis of the patient’s residual abilities, suitable for identifying the most effective rehabilitation protocol. According to literature, biofeedback and robot-assisted therapy, as well as virtual reality training, intensify the rehabilitation therapy allowing the accurate repetition of motor patterns [6]. Indeed, onset, intensity, duration and task-orientation of the training significantly affect the achievement of positive outcomes. As literature enlightens, repetitive training generates functional improvements which depend on patients inclusion criteria and time elapsed from the stroke, but also on the repetitions quantity [7]. In fact, scientific evidences suggest to extend the duration of training sessions, since longer sessions have better effects on motor functions [7,8].In recent literature, many works deal with the use of robotic devices for rehabilitation purposes, offering a wide variety of solutions for the upper and lower limbs rehabilitation, both in clinical environments and at home [5,9,10,11]. Several devices may provide a different kind of motion assistance, like passive or active mobilization, as well as haptic assistance or coaching [9]. Active devices, presenting at least one actuator, can induce the movement of specific parts of the limb, performing active or passive exercises. Moreover, the device may support the subject, which actively performs the task by moving the limb; on the contrary, in passive exercises the patient movement is guided by the device during the rehabilitation session.An alternative taxonomy classifies devices considering the mechanical design. Actually they can be (1) end-effector-based, i.e., the contact between machine and patient’s limb arises only at the end-effector level (e.g., MIT Manus [12]), or (2) exoskeleton-based, in which the mechanical configuration of the device mirrors the limb’s skeletal structure [13,14,15]; in those devices the contact between subject and system is distributed along the limb with multiple contact areas. Literature also presents many devices that combine these two structures, like the MIME-RiceWrist rehabilitation system [16].Besides, some devices are characterized by specific and significant features. Among them, reconfigurability represents the capability of the system of changing its mechanical structure, adapting it to different use conditions, or the ability to follow the subject necessities (e.g., MUNDUS [6]). Back-drivability describes instead the possibility of the patient to induce the movement of the system when the device is in passive state (e.g., HWARD [17]). Mechanical structure of the system, as well as type, number and location of the actuators determine the allowed movements of the device and the degrees of freedom (DOFs) of the system consequently. As the literature reports, most of the devices allow three-dimensional movements [18], whereas only a restricted group of devices enable the movement on a specific plane (e.g., ARC-MIME [19]).Focusing on the control of the devices, several signals may be evaluated, like externally imposed triggers [20], kinematic or dynamic signals from the device [21,22], as well as biomechanical signals from the subject, such as data derived from surface electromyography (sEMG [23,24]). Nonetheless, biomechanical systems can be considered characterized by low dynamics phenomena, and rehabilitation training even more, given the low velocities required for a correct training [25].Literature provides numerous examples of multi-sensor validation in clinical or rehabilitative contexts when considering the human motion [26,27], whereas multiple units of the same sensor are generally used when validating devices [28].Within this complex context, the validation process of new rehabilitation devices becomes critical, since the true analysis dimension for evaluating the system performance coincides with the analysis of the motion performed by the subject, referring therefore to elements outside the device. Hence, the system to monitor can include the device, but mainly focuses on the final user, i.e., the patient. We can describe some validation methods as device-oriented, meaning that the validation is pursued through the comparison of the performance, as recorded by the rehabilitation system, with those detected by the sensors located along the device. This is the typical condition for exoskeleton devices, in which the design of the system, with the distributed contact between machine and subject, justifies the hypothesis of negligible approximation errors between motion profile realized by device and movement of the patient limb. In this case, sensors like the inertial measurement unit (IMU) [29] or camera-based systems [30] are mostly used. Besides, according to the same rationale, other validation methods can be defined as user-oriented, since the analysis is performed detecting the patient movement, thanks to wearable sensors like EMG sensors [31] and optoelectronic systems [29] placed on significant landmarks of the testing subjects. This approach is necessary for end-effector based devices, since the process to monitor is partially independent from the device constraints, and differently from the device-oriented methods, it demands for the identification of a proper kinematic or dynamic model of the subject.In this work a multi-sensor validation approach is investigated and an innovative rehabilitation device was considered for the study. The device is an end-effector-based robotic system that has been developed within the SIMeRiON (Innovative Mechatronics System for Orthopedic and Neurological Rehabilitation) project, funded by Regione Lombardia [32]. The device is back-drivable and reconfigurable, presents an electro-mechanical actuation system, and is able to provide passive, active and assisted rehabilitation [32,33]. The mechanical system is based on a compact differential system and is characterized by two DOFs; this allows implementing every kind of motion profile within a plane. The device performance is analyzed evaluating the movement induced by the device on the upper limb of a healthy subject in a sequence of repetitions. The movement characteristics are investigated monitoring specific landmark points of the subject’s limb, with the aim of verifying the system effectiveness in imposing the expected training. The kinematics of those points has been detected thanks to (1) an optical marker-based tracking system, and (2) an inertial sensor-based system. Acquired data have been compared to evaluate strength points and drawbacks of each measuring strategy for the proper tuning of the system model; in fact, the kinematic model of subject and system has been defined and used as reference for the interpretation of the collected data. In the next section, a synthetic description of the device is reported, and the adopted methods for the performed data treatment are described. Results are then presented and discussed in the following sections, whereas main strength points and limits of the work are finally described in the conclusions.[…]

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Figure 2. Positioning of accelerometers, indicated with the yellow frames, and of the markers, identified by the red frames. Circles are used for the markers of the moving elements, and square frames for the markers adopted for the environment analysis.