Posts Tagged real-time systems

[Abstract] Kinematic analysis and control for upper limb robotic rehabilitation system – IEEE Conference Publication

Abstract

Present physical rehabilitation practice implies one-to-one therapist — patient interactions. This leads to shortage of therapists and high costs for patient or healthcare insurance systems. Along with Prokinetic Rehabilitation Clinic, we proposed a new intelligent, adaptive robotic system (RAPMES), which can provide the rehabilitation protocols, defined by a therapist, for the wrist and elbow of upper limb, considering the patient reactions and based on real-time feedback. RAPMES is a passive rehabilitation robotic system (RRS) with 3 degrees of freedom, and assists the rehabilitation process for elbow, forearm and wrist movements. Computation of the kinematic model for the RAPMES robotic device is required in order to determine the parameters associated with the mechanical joints, so that the experimental model executes certain trajectories in space. In this paper, we will present both forward and inverse kinematics determined for the experimental model. The kinematic model was implemented in Matlab environment, and we present a series of simulations, conducted in order to validate the proposed kinematic model. Then, we impose the functional movements (determined using the real-time video motion analysis system, as polynomial movement functions) as input to the kinematic model, and we present a series of simulations and results. The RAPMES control algorithm includes the kinematic model, and uses the polynomial movement functions as control input.
Date of Conference: 28-31 May 2018

 

I. Introduction

Statistics shows that, at European Union level, the upper limb is second common body part injured, as a result of unintentional physical injury [1]. Also, one can note the shortage of therapists and high costs for patient or healthcare insurance systems. Current development in robotics may offer a solution for this problem [2], allowing the creation of robotic devices to support the rehabilitation process, in a supervised or unsupervised way, in physiotherapy clinics or at home. In this context, we proposed RAPMES, a new intelligent, adaptive robotic system, which can provide the rehabilitation protocols, defined by a therapist, for the wrist and elbow of upper limb, considering the patient reactions and based on real-time feedback. RAPMES robotic system is designed on an ongoing research project, which implies several stages of development. In a first stage, we conducted a study involving therapists, the personnel and devices existent in a physiotherapy clinic. The role of this study was to determine the requirements for the robotic device, and to reveal the specific therapeutic needs of patients with rehabilitation indications at wrist and elbow level. On a second stage, we used a real-time video motion analysis system, to determine and understand specific functional movements frequently made with the dominant upper limb, by healthy persons. One of our research objectives is to include these movements as a part of RAPMES control algorithm, as they may offer a better rehabilitation of the upper limb, for specific moves. Next, we designed the robotic device, based on findings described above, and realized an experimental model of the robotic device.

via Kinematic analysis and control for upper limb robotic rehabilitation system – IEEE Conference Publication

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[Abstract] Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton

Abstract:

The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.

via Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton – IEEE Journals & Magazine

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[ARTICLE] Agent-based systems for telerehabilitation: strengths, limitations and future challenges (PDF Download Available) – Full Text

Abstract
Telerehabilitation in older adults is most needed in the patient environments, rather than in formal ambulatories or hospitals. Supporting such practices brings significant advantages to patients, their family, formal and informal caregivers, clinicians, and researchers. Several techniques and technologies have been developed aiming at facilitating and enhancing the effectiveness of telerehabilitation. This paper gives a quick overview of the state of the art, investigating video-based, wear-able, robotic, distributed, and gamified telerehabilitation solutions. In particular, agent-based solutions are analyzed and discussed addressing strength, limitations, and future challenges. Elaborating on functional requirements expressed by professional physiotherapists and researchers, the need for extending multi-agent systems (MAS) peculiarities at the sensing level in wearable solutions establishes new research challenges. Employed in cyber-physical scenarios with users-sensors and sensors-sensors interactions, MAS are requested to handle timing constraints, scarcity of resources and new communication means, which are crucial for providing real-time feedback and coaching.
1 Introduction
Healthcare institutions are facing the strain of a significantly larger elderly population [1]. Lengthening life expectancy is met by an increasing demand for medical and technological contributions to extend the ”good-health”, and disability free period.
The major factor catalyzing the elderly’s impairing process is the progres-
sive reduction of mobility, due to the natural aging process, inactivity, dis-
eases such as osteoarthritis, stroke or other neurological conditions, falls with its consequences, such as fear of falls (leading to inactivity), or fractures (needing surgery).Despite the emergence of less-invasive surgical techniques, post-intervention rehabilitation still requires extended periods and tailored therapies, which usually involve complications. Performing traditional rehabilitative practices is leading to a significant increase in public-health costs and, in some cases a lack of resources, thus worsening the services’ quality. Rehabilitation is often a long process and needs to be sustained long after the end of the acute care. Simplifying the access to health services [2] can raise the number of patients, maintaining (or even increasing) the quality of care. For example, patients requiring support, such as continuous or selective monitoring, can benefit from systems that automatically transmit the information gathered in their domestic environment to the health clinics, thus enabling telemonitoring on their health conditions [3].
Although in traditional solutions telemonitoring is a self-contained practice
limited to passively observing the patients, the need for remote sensing is crucially coupled with the need for coaching older adults in their daily living [4,5].
For example, a critical activity such as telerehabilitation cannot be limited
to observing the patients’ behaviors. Indeed, patient adherence and acceptability of rehabilitative practices need to be actively enhanced, overcoming pitfalls due to motor (e.g., endurance), non-motor (e.g., fatigue, pain, dysautonomic symptoms, and motivational), and cognitive deficits. According to Rodriguez et al. [6], telerehabilitation can be formally defined as:
“the application of telecommunication, remote sensing and operation tech-
nologies, and computing technologies to assist with the provision of med-
ical rehabilitation services at a distance.”
Patients, physiotherapists, and health institutes can gain several benefits
from an extensive adoption of telerehabilitation systems [7]. Considering the
economical point of view, Mozaffarian et al. [8] figured out that the total cost
of stroke in the US was estimable to be 34.3 billion dollars in 2008, rising up to 69.1 billion dollars in 2016.
Even though to date they are not precisely quantifiable due to insufficient evidence [9], Mutingi et al. [10] presented as “inevitable advantages”
(i) a substantial cost saving primarily due to the reduction of specialized human resources,
(ii) an enhancement of patient comfort and lifestyle, and (iii) improvements of therapy and decision making processes. Moreover, Morreale et al. [11] mentioned one of the most appreciated benefits: the increase of adherence to rehabilitation protocols.
The multitude of scientific contributions fostering telerehabilitation exploits
new technologies and various architectures to better understand and serve user requirements. However, due to technological or technical limitations, physiotherapists’ needs have not yet been completely satisfied. To fill this gap, a system evolution is required. For example, telerehabilitation systems cannot offer the same behavior to users with diverse conditions. Viceversa, according to the environment condition, they must rather be able to adapt themselves to the user needs [6].
Telerehabilitation is characterized by a very delicate equilibrium between
environment, devices, and users. Thus, the capabilities such as self adaptation, flexibility, and ubiquity are crucial to facilitate and promote the usability and then the actual practices.
Agent-based systems for telerehabilitation: strengths, limitations and future challenges (PDF Download Available). Available from: https://www.researchgate.net/publication/316790326_Agent-based_systems_for_telerehabilitation_strengths_limitations_and_future_challenges [accessed May 26, 2017].

Continue —> Agent-based systems for telerehabilitation: strengths, limitations and future challenges (PDF Download Available)

Fig. 2. Agent-based sensing: future challenge for telerehabilitation MAS. 

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